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Renal risk stratification with the new oral anticoagulants
To the Editor: I read with interest the review of the new oral anticoagulants by Fawole et al1 and agree with their comments on the prevention of bleeding and the importance of monitoring renal function in managing patients on the new classes of oral anticoagulants. However, no specifics were given on how to proceed. Thus, I recommend that renal risk stratification be done before and 1 week after starting these new drugs.
Originally, the US Food and Drug Administration approved dabigatran (Pradaxa) at a dose of 150 mg orally twice daily in patients with a creatinine clearance of 15 to 30 mL/min/1.73 m2. This dosing corresponded to the estimated glomerular filtration rate (eGFR) in patients with stage 4 chronic kidney disease, but this dosing is contraindicated in other guidelines worldwide (Canada, Europe, the United Kingdom, Japan, Australia, and New Zealand).2 Not unexpectedly, 3,781 serious adverse effects were noted in the 2011 US postmarketing experience with dabigatran. These included death (542 cases), hemorrhage (2,367 cases), acute renal failure (291 cases), stroke (644 cases), and suspected liver failure (15 cases).3 Thirteen months after dabigatran’s approval in the United States, Boehringer Ingelheim changed the dosage and product guidelines.2–4 The new dosage4 is 75 mg twice daily for patients with a creatinine clearance of 15 to 30 mL/min/1.73 m2.
Therefore, I suggest a nephrologic “way out”5 when using the new oral anticoagulants to avoid the problems with dabigatran noted above.
First, if these drugs are to be used in nonvalvular atrial fibrillation, risk factors should be determined using the CHADS2 or the CHADS2-VASc score. Special attention should be given to patients age 75 and older, women, and patients with a history of stroke, transient ischemic attack, or systemic embolism. All of these have been noted to be major risk factors.6,7
Second, renal risk stratification8 should be done using a comprehensive metabolic panel before and 1 week after starting new oral anticoagulants, or if there is a change in the patient’s clinical condition. Most US laboratories now provide an eGFR and the stage of chronic kidney disease.3,5 For example (Table 1), if dabigatran is used, one should follow current dosing guidelines for chronic kidney disease stages 1 through 3, ie, 150 mg twice daily. If stage 4 chronic kidney disease is detected (creatinine clearance 15–29 mL/min/1.73 m2), the updated recommended dosage is 75 mg twice daily. If stage 5 is noted (eGFR ≤ 15 mL/min/1.73 m2), dabigatran is not indicated. Similar steps can be done using current guidelines for the other new oral anticoagulants.
This simple renal risk stratification guideline should help avoid some of the problems noted in the dabigatran postmarketing experience, which were aggravated by the lack of approval of a 110-mg dose and by misleading advertising, claiming that no blood monitoring was required.2–5 Thus, the new oral anticoagulants should be a welcome addition to our armamentarium in patients who need them, and we hope to avoid the risks, morbidity, mortality, and expense of trying to reverse adverse effects.
- Fawole A, Daw HA, Crowther MA. Practical management of bleeding due to the anticoagulants, dabigatran, rivaroxaban, and apixaban. Cleve Clin J Med 2013, 80:443–451.
- Pazmiño PA. Dabigatran associated acute renal failure (DAARF). El Paso Physician 2011; 34:7–9.
- Pazmiño PA. Adverse effects of dabigatran (Letter). Ann Intern Med 2012; 157:916.
- Pradaxa (prescribing information). Ridgefield, CT. Boehringer Ingelheim Pharmaceuticals 2011.
- Pazmiño PA. Dabigatran: a nephrological way out. Am J Med 2013; 126;e21–e22.
- Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach. The Euro Heart Survey on Atrial Fibrillation. Chest 2010; 137:263–272.
- Reinecke H, Brand E, Mesters R, et al. Dilemmas in the management of atrial fibrillation in chronic kidney disease. J Am Soc Nephrol 2009; 20:705–711.
- National Kidney Foundation. K/DOQI clinical practice guidelines for chronic disease: evaluation, classification and stratification. Am J Kidney Dis 2002; 39(suppl 1):S1–S266.
To the Editor: I read with interest the review of the new oral anticoagulants by Fawole et al1 and agree with their comments on the prevention of bleeding and the importance of monitoring renal function in managing patients on the new classes of oral anticoagulants. However, no specifics were given on how to proceed. Thus, I recommend that renal risk stratification be done before and 1 week after starting these new drugs.
Originally, the US Food and Drug Administration approved dabigatran (Pradaxa) at a dose of 150 mg orally twice daily in patients with a creatinine clearance of 15 to 30 mL/min/1.73 m2. This dosing corresponded to the estimated glomerular filtration rate (eGFR) in patients with stage 4 chronic kidney disease, but this dosing is contraindicated in other guidelines worldwide (Canada, Europe, the United Kingdom, Japan, Australia, and New Zealand).2 Not unexpectedly, 3,781 serious adverse effects were noted in the 2011 US postmarketing experience with dabigatran. These included death (542 cases), hemorrhage (2,367 cases), acute renal failure (291 cases), stroke (644 cases), and suspected liver failure (15 cases).3 Thirteen months after dabigatran’s approval in the United States, Boehringer Ingelheim changed the dosage and product guidelines.2–4 The new dosage4 is 75 mg twice daily for patients with a creatinine clearance of 15 to 30 mL/min/1.73 m2.
Therefore, I suggest a nephrologic “way out”5 when using the new oral anticoagulants to avoid the problems with dabigatran noted above.
First, if these drugs are to be used in nonvalvular atrial fibrillation, risk factors should be determined using the CHADS2 or the CHADS2-VASc score. Special attention should be given to patients age 75 and older, women, and patients with a history of stroke, transient ischemic attack, or systemic embolism. All of these have been noted to be major risk factors.6,7
Second, renal risk stratification8 should be done using a comprehensive metabolic panel before and 1 week after starting new oral anticoagulants, or if there is a change in the patient’s clinical condition. Most US laboratories now provide an eGFR and the stage of chronic kidney disease.3,5 For example (Table 1), if dabigatran is used, one should follow current dosing guidelines for chronic kidney disease stages 1 through 3, ie, 150 mg twice daily. If stage 4 chronic kidney disease is detected (creatinine clearance 15–29 mL/min/1.73 m2), the updated recommended dosage is 75 mg twice daily. If stage 5 is noted (eGFR ≤ 15 mL/min/1.73 m2), dabigatran is not indicated. Similar steps can be done using current guidelines for the other new oral anticoagulants.
This simple renal risk stratification guideline should help avoid some of the problems noted in the dabigatran postmarketing experience, which were aggravated by the lack of approval of a 110-mg dose and by misleading advertising, claiming that no blood monitoring was required.2–5 Thus, the new oral anticoagulants should be a welcome addition to our armamentarium in patients who need them, and we hope to avoid the risks, morbidity, mortality, and expense of trying to reverse adverse effects.
To the Editor: I read with interest the review of the new oral anticoagulants by Fawole et al1 and agree with their comments on the prevention of bleeding and the importance of monitoring renal function in managing patients on the new classes of oral anticoagulants. However, no specifics were given on how to proceed. Thus, I recommend that renal risk stratification be done before and 1 week after starting these new drugs.
Originally, the US Food and Drug Administration approved dabigatran (Pradaxa) at a dose of 150 mg orally twice daily in patients with a creatinine clearance of 15 to 30 mL/min/1.73 m2. This dosing corresponded to the estimated glomerular filtration rate (eGFR) in patients with stage 4 chronic kidney disease, but this dosing is contraindicated in other guidelines worldwide (Canada, Europe, the United Kingdom, Japan, Australia, and New Zealand).2 Not unexpectedly, 3,781 serious adverse effects were noted in the 2011 US postmarketing experience with dabigatran. These included death (542 cases), hemorrhage (2,367 cases), acute renal failure (291 cases), stroke (644 cases), and suspected liver failure (15 cases).3 Thirteen months after dabigatran’s approval in the United States, Boehringer Ingelheim changed the dosage and product guidelines.2–4 The new dosage4 is 75 mg twice daily for patients with a creatinine clearance of 15 to 30 mL/min/1.73 m2.
Therefore, I suggest a nephrologic “way out”5 when using the new oral anticoagulants to avoid the problems with dabigatran noted above.
First, if these drugs are to be used in nonvalvular atrial fibrillation, risk factors should be determined using the CHADS2 or the CHADS2-VASc score. Special attention should be given to patients age 75 and older, women, and patients with a history of stroke, transient ischemic attack, or systemic embolism. All of these have been noted to be major risk factors.6,7
Second, renal risk stratification8 should be done using a comprehensive metabolic panel before and 1 week after starting new oral anticoagulants, or if there is a change in the patient’s clinical condition. Most US laboratories now provide an eGFR and the stage of chronic kidney disease.3,5 For example (Table 1), if dabigatran is used, one should follow current dosing guidelines for chronic kidney disease stages 1 through 3, ie, 150 mg twice daily. If stage 4 chronic kidney disease is detected (creatinine clearance 15–29 mL/min/1.73 m2), the updated recommended dosage is 75 mg twice daily. If stage 5 is noted (eGFR ≤ 15 mL/min/1.73 m2), dabigatran is not indicated. Similar steps can be done using current guidelines for the other new oral anticoagulants.
This simple renal risk stratification guideline should help avoid some of the problems noted in the dabigatran postmarketing experience, which were aggravated by the lack of approval of a 110-mg dose and by misleading advertising, claiming that no blood monitoring was required.2–5 Thus, the new oral anticoagulants should be a welcome addition to our armamentarium in patients who need them, and we hope to avoid the risks, morbidity, mortality, and expense of trying to reverse adverse effects.
- Fawole A, Daw HA, Crowther MA. Practical management of bleeding due to the anticoagulants, dabigatran, rivaroxaban, and apixaban. Cleve Clin J Med 2013, 80:443–451.
- Pazmiño PA. Dabigatran associated acute renal failure (DAARF). El Paso Physician 2011; 34:7–9.
- Pazmiño PA. Adverse effects of dabigatran (Letter). Ann Intern Med 2012; 157:916.
- Pradaxa (prescribing information). Ridgefield, CT. Boehringer Ingelheim Pharmaceuticals 2011.
- Pazmiño PA. Dabigatran: a nephrological way out. Am J Med 2013; 126;e21–e22.
- Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach. The Euro Heart Survey on Atrial Fibrillation. Chest 2010; 137:263–272.
- Reinecke H, Brand E, Mesters R, et al. Dilemmas in the management of atrial fibrillation in chronic kidney disease. J Am Soc Nephrol 2009; 20:705–711.
- National Kidney Foundation. K/DOQI clinical practice guidelines for chronic disease: evaluation, classification and stratification. Am J Kidney Dis 2002; 39(suppl 1):S1–S266.
- Fawole A, Daw HA, Crowther MA. Practical management of bleeding due to the anticoagulants, dabigatran, rivaroxaban, and apixaban. Cleve Clin J Med 2013, 80:443–451.
- Pazmiño PA. Dabigatran associated acute renal failure (DAARF). El Paso Physician 2011; 34:7–9.
- Pazmiño PA. Adverse effects of dabigatran (Letter). Ann Intern Med 2012; 157:916.
- Pradaxa (prescribing information). Ridgefield, CT. Boehringer Ingelheim Pharmaceuticals 2011.
- Pazmiño PA. Dabigatran: a nephrological way out. Am J Med 2013; 126;e21–e22.
- Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach. The Euro Heart Survey on Atrial Fibrillation. Chest 2010; 137:263–272.
- Reinecke H, Brand E, Mesters R, et al. Dilemmas in the management of atrial fibrillation in chronic kidney disease. J Am Soc Nephrol 2009; 20:705–711.
- National Kidney Foundation. K/DOQI clinical practice guidelines for chronic disease: evaluation, classification and stratification. Am J Kidney Dis 2002; 39(suppl 1):S1–S266.
In reply: Renal risk stratification with the new oral anticoagulants
In Reply: We agree with the comments of Dr. Pazmiño regarding specifics of renal risk stratification in patients taking the new oral anticoagulants. In order to reduce the bleeding risks associated with these agents, they should be prescribed on the basis of the individual patient’s clinical characteristics. We did not discuss this since the focus of our article was management of bleeding that resulted from use of these drugs. We appreciate the recommendations of Dr. Pazmiño.
In Reply: We agree with the comments of Dr. Pazmiño regarding specifics of renal risk stratification in patients taking the new oral anticoagulants. In order to reduce the bleeding risks associated with these agents, they should be prescribed on the basis of the individual patient’s clinical characteristics. We did not discuss this since the focus of our article was management of bleeding that resulted from use of these drugs. We appreciate the recommendations of Dr. Pazmiño.
In Reply: We agree with the comments of Dr. Pazmiño regarding specifics of renal risk stratification in patients taking the new oral anticoagulants. In order to reduce the bleeding risks associated with these agents, they should be prescribed on the basis of the individual patient’s clinical characteristics. We did not discuss this since the focus of our article was management of bleeding that resulted from use of these drugs. We appreciate the recommendations of Dr. Pazmiño.
Not all joint pain is arthritis
To the Editor: I was somewhat confused by the Clinical Picture case in the May 2013 issue.1 The caption for Figure 1 stated that the MRI showed erosions and marrow edema, which were “asymmetrical compared with the other wrist, a finding highly suggestive of rheumatoid arthritis.” However, rheumatoid arthritis is generally considered to be symmetrical.2 Was this a typographical error, or did I miss a crucial concept somewhere?
- Kochhar GS, Rizk M, Patil DT. Not all joint pain is arthritis. Cleve Clin J Med 2013; 80:272–273.
- Bukhari M, Lunt M, Harrison BJ, Scott DG, Symmons DP, Silman AJ. Erosions in inflammatory polyarthritis are symmetrical regardless of rheumatoid factor status: results from a primary care-based inception cohort of patients. Rheumatology 2002; 41:246–252.
To the Editor: I was somewhat confused by the Clinical Picture case in the May 2013 issue.1 The caption for Figure 1 stated that the MRI showed erosions and marrow edema, which were “asymmetrical compared with the other wrist, a finding highly suggestive of rheumatoid arthritis.” However, rheumatoid arthritis is generally considered to be symmetrical.2 Was this a typographical error, or did I miss a crucial concept somewhere?
To the Editor: I was somewhat confused by the Clinical Picture case in the May 2013 issue.1 The caption for Figure 1 stated that the MRI showed erosions and marrow edema, which were “asymmetrical compared with the other wrist, a finding highly suggestive of rheumatoid arthritis.” However, rheumatoid arthritis is generally considered to be symmetrical.2 Was this a typographical error, or did I miss a crucial concept somewhere?
- Kochhar GS, Rizk M, Patil DT. Not all joint pain is arthritis. Cleve Clin J Med 2013; 80:272–273.
- Bukhari M, Lunt M, Harrison BJ, Scott DG, Symmons DP, Silman AJ. Erosions in inflammatory polyarthritis are symmetrical regardless of rheumatoid factor status: results from a primary care-based inception cohort of patients. Rheumatology 2002; 41:246–252.
- Kochhar GS, Rizk M, Patil DT. Not all joint pain is arthritis. Cleve Clin J Med 2013; 80:272–273.
- Bukhari M, Lunt M, Harrison BJ, Scott DG, Symmons DP, Silman AJ. Erosions in inflammatory polyarthritis are symmetrical regardless of rheumatoid factor status: results from a primary care-based inception cohort of patients. Rheumatology 2002; 41:246–252.
In reply: Not all joint pain is arthritis
In Reply: We apologize for the confusion. We wanted to convey that, in that patient at that time, synovitis with erosions and edema indicating inflammation (greater on the right than on the left left) was suggestive of rheumatoid arthritis despite the asymmetry seen (findings greater in the right wrist than in the left). Given the patient’s clinical findings at that time and the above imaging findings, the initial diagnosis of rheumatoid arthritis was correct. But since the patient was not responding to therapy and since the abdominal pain was worsening, we probed further. Subsequently, the patient was diagnosed with Whipple disease. The fact that inflammatory arthritis can occur in other conditions that are not rheumatologic is a primary reason we found this case worth sharing.
In Reply: We apologize for the confusion. We wanted to convey that, in that patient at that time, synovitis with erosions and edema indicating inflammation (greater on the right than on the left left) was suggestive of rheumatoid arthritis despite the asymmetry seen (findings greater in the right wrist than in the left). Given the patient’s clinical findings at that time and the above imaging findings, the initial diagnosis of rheumatoid arthritis was correct. But since the patient was not responding to therapy and since the abdominal pain was worsening, we probed further. Subsequently, the patient was diagnosed with Whipple disease. The fact that inflammatory arthritis can occur in other conditions that are not rheumatologic is a primary reason we found this case worth sharing.
In Reply: We apologize for the confusion. We wanted to convey that, in that patient at that time, synovitis with erosions and edema indicating inflammation (greater on the right than on the left left) was suggestive of rheumatoid arthritis despite the asymmetry seen (findings greater in the right wrist than in the left). Given the patient’s clinical findings at that time and the above imaging findings, the initial diagnosis of rheumatoid arthritis was correct. But since the patient was not responding to therapy and since the abdominal pain was worsening, we probed further. Subsequently, the patient was diagnosed with Whipple disease. The fact that inflammatory arthritis can occur in other conditions that are not rheumatologic is a primary reason we found this case worth sharing.
Ponatinib sales and marketing suspended
After follow-up data from the phase 2 PACE trial revealed that ponatinib-treated patients experienced an increase in arterial and venous thrombotic events, the FDA decided to investigate the drug’s safety.
The agency placed current ponatinib trials on partial clinical hold and asked the drug’s makers, Ariad Pharmaceuticals, to end the phase 3 EPIC trial.
Now, the FDA has asked Ariad to temporarily suspend marketing and sales of ponatinib while the agency further evaluates the drug.
Ponatinib is approved in the US and the European Union to treat adults with chronic myeloid leukemia or Philadelphia chromosome-positive acute lymphoblastic leukemia that is resistant to or intolerant of other tyrosine kinase inhibitors.
Recommendations for ponatinib use
Until its safety evaluation is complete, the FDA is recommending that healthcare professionals reconsider the use of ponatinib.
For patients who are taking ponatinib but not responding, immediately discontinue their treatment and discuss alternative treatment options.
For patients who are currently taking ponatinib and responding, determine whether the potential benefits of the therapy outweigh the risks. If they do, these patients should be treated under a single-patient investigational new drug (IND) application or expanded access registry program while the FDA’s safety investigation continues.
Do not start treating new patients with ponatinib unless no other treatment options are available and all other available therapies have failed. Patients who meet these criteria can be considered for treatment under an IND or expanded access registry program.
For more information on obtaining access to treatment for your patient under an IND, please refer to the following website: Physician Request for an Individual Patient IND under Expanded Access for Non-emergency or Emergency Use.
Ponatinib safety data
Thus far, the FDA’s investigation of ponatinib has revealed an increased frequency of arterial and venous thrombotic events since the drug was approved in December 2012.
In clinical trials conducted before the drug’s approval, serious arterial thrombosis occurred in 8% of ponatinib-treated patients, and venous thromboembolism occurred in 3%. In the most recent clinical trial data, at least 20% of all participants treated with ponatinib have developed thrombosis or arteriosclerosis.
Serious adverse vascular events have occurred in about 24% of patients in the phase 2 trial of ponatinib (median treatment duration of 1.3 years) and about 48% of patients in the phase 1 trial (median treatment duration of 2.7 years).
These included fatal and life-threatening heart attack, stroke, loss of blood flow to the extremities resulting in tissue death, and severe narrowing of blood vessels in the extremities, heart, and brain requiring urgent surgical procedures to restore blood flow.
In the phase 2 trial, adverse events affecting the blood vessels that supply the heart, brain, and extremities were observed in 12%, 6%, and 8% of patients, respectively. Patients with and without cardiovascular risk factors, including patients in their 20s, have experienced these events.
Serious adverse reactions involving the eyes, which led to blindness or blurred vision, occurred in ponatinib-treated patients. High blood pressure occurred in 67% of patients treated with ponatinib in the trials. Heart failure, including fatalities, occurred in 8% of patients who received the drug.
In some patients, fatal and serious adverse events have occurred as early as 2 weeks after starting ponatinib therapy.
The phase 1 and 2 trials did not include a control group, so it is not possible to determine the relationship of these adverse events to ponatinib. However, the increasing rate and pattern of the events strongly suggests that many are drug-related, according to the FDA.
The agency said it cannot currently identify a dose level or exposure duration of ponatinib that is safe. Prior to the issues with adverse events, the recommended dose of ponatinib was a 45 mg tablet taken once daily.
The FDA said it plans to continue its investigation and will notify healthcare professionals and patients as more information becomes available.
After follow-up data from the phase 2 PACE trial revealed that ponatinib-treated patients experienced an increase in arterial and venous thrombotic events, the FDA decided to investigate the drug’s safety.
The agency placed current ponatinib trials on partial clinical hold and asked the drug’s makers, Ariad Pharmaceuticals, to end the phase 3 EPIC trial.
Now, the FDA has asked Ariad to temporarily suspend marketing and sales of ponatinib while the agency further evaluates the drug.
Ponatinib is approved in the US and the European Union to treat adults with chronic myeloid leukemia or Philadelphia chromosome-positive acute lymphoblastic leukemia that is resistant to or intolerant of other tyrosine kinase inhibitors.
Recommendations for ponatinib use
Until its safety evaluation is complete, the FDA is recommending that healthcare professionals reconsider the use of ponatinib.
For patients who are taking ponatinib but not responding, immediately discontinue their treatment and discuss alternative treatment options.
For patients who are currently taking ponatinib and responding, determine whether the potential benefits of the therapy outweigh the risks. If they do, these patients should be treated under a single-patient investigational new drug (IND) application or expanded access registry program while the FDA’s safety investigation continues.
Do not start treating new patients with ponatinib unless no other treatment options are available and all other available therapies have failed. Patients who meet these criteria can be considered for treatment under an IND or expanded access registry program.
For more information on obtaining access to treatment for your patient under an IND, please refer to the following website: Physician Request for an Individual Patient IND under Expanded Access for Non-emergency or Emergency Use.
Ponatinib safety data
Thus far, the FDA’s investigation of ponatinib has revealed an increased frequency of arterial and venous thrombotic events since the drug was approved in December 2012.
In clinical trials conducted before the drug’s approval, serious arterial thrombosis occurred in 8% of ponatinib-treated patients, and venous thromboembolism occurred in 3%. In the most recent clinical trial data, at least 20% of all participants treated with ponatinib have developed thrombosis or arteriosclerosis.
Serious adverse vascular events have occurred in about 24% of patients in the phase 2 trial of ponatinib (median treatment duration of 1.3 years) and about 48% of patients in the phase 1 trial (median treatment duration of 2.7 years).
These included fatal and life-threatening heart attack, stroke, loss of blood flow to the extremities resulting in tissue death, and severe narrowing of blood vessels in the extremities, heart, and brain requiring urgent surgical procedures to restore blood flow.
In the phase 2 trial, adverse events affecting the blood vessels that supply the heart, brain, and extremities were observed in 12%, 6%, and 8% of patients, respectively. Patients with and without cardiovascular risk factors, including patients in their 20s, have experienced these events.
Serious adverse reactions involving the eyes, which led to blindness or blurred vision, occurred in ponatinib-treated patients. High blood pressure occurred in 67% of patients treated with ponatinib in the trials. Heart failure, including fatalities, occurred in 8% of patients who received the drug.
In some patients, fatal and serious adverse events have occurred as early as 2 weeks after starting ponatinib therapy.
The phase 1 and 2 trials did not include a control group, so it is not possible to determine the relationship of these adverse events to ponatinib. However, the increasing rate and pattern of the events strongly suggests that many are drug-related, according to the FDA.
The agency said it cannot currently identify a dose level or exposure duration of ponatinib that is safe. Prior to the issues with adverse events, the recommended dose of ponatinib was a 45 mg tablet taken once daily.
The FDA said it plans to continue its investigation and will notify healthcare professionals and patients as more information becomes available.
After follow-up data from the phase 2 PACE trial revealed that ponatinib-treated patients experienced an increase in arterial and venous thrombotic events, the FDA decided to investigate the drug’s safety.
The agency placed current ponatinib trials on partial clinical hold and asked the drug’s makers, Ariad Pharmaceuticals, to end the phase 3 EPIC trial.
Now, the FDA has asked Ariad to temporarily suspend marketing and sales of ponatinib while the agency further evaluates the drug.
Ponatinib is approved in the US and the European Union to treat adults with chronic myeloid leukemia or Philadelphia chromosome-positive acute lymphoblastic leukemia that is resistant to or intolerant of other tyrosine kinase inhibitors.
Recommendations for ponatinib use
Until its safety evaluation is complete, the FDA is recommending that healthcare professionals reconsider the use of ponatinib.
For patients who are taking ponatinib but not responding, immediately discontinue their treatment and discuss alternative treatment options.
For patients who are currently taking ponatinib and responding, determine whether the potential benefits of the therapy outweigh the risks. If they do, these patients should be treated under a single-patient investigational new drug (IND) application or expanded access registry program while the FDA’s safety investigation continues.
Do not start treating new patients with ponatinib unless no other treatment options are available and all other available therapies have failed. Patients who meet these criteria can be considered for treatment under an IND or expanded access registry program.
For more information on obtaining access to treatment for your patient under an IND, please refer to the following website: Physician Request for an Individual Patient IND under Expanded Access for Non-emergency or Emergency Use.
Ponatinib safety data
Thus far, the FDA’s investigation of ponatinib has revealed an increased frequency of arterial and venous thrombotic events since the drug was approved in December 2012.
In clinical trials conducted before the drug’s approval, serious arterial thrombosis occurred in 8% of ponatinib-treated patients, and venous thromboembolism occurred in 3%. In the most recent clinical trial data, at least 20% of all participants treated with ponatinib have developed thrombosis or arteriosclerosis.
Serious adverse vascular events have occurred in about 24% of patients in the phase 2 trial of ponatinib (median treatment duration of 1.3 years) and about 48% of patients in the phase 1 trial (median treatment duration of 2.7 years).
These included fatal and life-threatening heart attack, stroke, loss of blood flow to the extremities resulting in tissue death, and severe narrowing of blood vessels in the extremities, heart, and brain requiring urgent surgical procedures to restore blood flow.
In the phase 2 trial, adverse events affecting the blood vessels that supply the heart, brain, and extremities were observed in 12%, 6%, and 8% of patients, respectively. Patients with and without cardiovascular risk factors, including patients in their 20s, have experienced these events.
Serious adverse reactions involving the eyes, which led to blindness or blurred vision, occurred in ponatinib-treated patients. High blood pressure occurred in 67% of patients treated with ponatinib in the trials. Heart failure, including fatalities, occurred in 8% of patients who received the drug.
In some patients, fatal and serious adverse events have occurred as early as 2 weeks after starting ponatinib therapy.
The phase 1 and 2 trials did not include a control group, so it is not possible to determine the relationship of these adverse events to ponatinib. However, the increasing rate and pattern of the events strongly suggests that many are drug-related, according to the FDA.
The agency said it cannot currently identify a dose level or exposure duration of ponatinib that is safe. Prior to the issues with adverse events, the recommended dose of ponatinib was a 45 mg tablet taken once daily.
The FDA said it plans to continue its investigation and will notify healthcare professionals and patients as more information becomes available.
Hospitalist‐Run Postdischarge Clinic
Currently, healthcare systems rarely provide ideal transitions of care for discharged patients,[1] resulting in fragmented care,[2, 3, 4, 5] significant patient uncertainty about how to manage at home,[6, 7] and frequent adverse events.[8, 9] These factors are so commonly experienced by discharged patients that they are recognizable as a postdischarge syndrome.[10]
One element important for reducing the postdischarge risk of adverse events is provision of adequate follow‐up.[11, 12] However, supplying this care is challenging in the modern era, and it will become progressively more difficult to achieve. In 2004, 50% of readmitted Medicare fee‐for‐service patients had no postdischarge visit within 30 days of their discharge,[9] likely due in part to difficulty arranging such care. Changes in insurance coverage and demographics are expected to result in more than 100 million newly insured patients by 2019, yet the primary‐care workforce is projected to begin shrinking by 2016.[13, 14] In the increasingly uncommon situation that a primary‐care clinician is available promptly after discharge, information transfer is often inadequate[4, 15, 16, 17] and can be exacerbated by the growing discontinuity between inpatient and outpatient care.[2, 3, 4] Efforts to increase the supply of primary‐care clinicians and thereby improve early access to postdischarge care are important for the future, but hospitals, particularly those penalized for high risk‐adjusted readmission rates, are seeking novel solutions now.
One increasingly common innovation is to extend the role of inpatient providers (usually hospitalists) into the postdischarge period.[18] Preliminary evidence suggests improved continuity[19] and access[20] achieved by providing this care may decrease postdischarge adverse events,[19, 20, 21] though evidence is conflicting.[22]
As a closed, multilevel healthcare system, the Denver VA Medical Center is uniquely positioned to evaluate the influence of alternative postdischarge‐care strategies on subsequent adverse events. Discharged patients are seen in a well‐established hospitalist‐run postdischarge clinic (PDC), a robust urgent‐care system (UC), or by a large primary‐care provider (PCP) practice. The purpose of this study was to evaluate whether patients seen in a hospitalist‐run PDC have reduced adverse outcomes in the 30 days following hospital discharge compared with follow‐up with the patient's PCP or in an UC clinic.
METHODS
Patients
This was a retrospective cohort study of consecutive adult patients discharged from the general medical services of the Denver VA Medical Center after a nonelective admission between January 2005 and August 2012. This time range was chosen because all 3 clinics were fully operational during this period. The Denver VA Medical Center is an academically‐affiliated 128‐bed hospital that provides a full range of tertiary services. All medical patients, including intensive care unit (ICU) patients, are cared for on general medical teams by University of Colorado housestaff with hospitalists or subspecialty attendings. Patients who lived in the Denver metropolitan area, were discharged home, and who followed up with a PCP, UC clinic, or PDC within 30 days of discharge were included. Patients discharged to subacute facilities, hospice, or this tends to be capitalized as a special program at our VA were excluded. For patients with multiple admissions, only the first was included.
Clinics
Primary Care
Primary‐care clinics in the VA system are organized into Patient‐Aligned Care Teams (PACTs) and are available for appointments 5 days per week. Patients discharged from the medical service who have PCPs are called within 48 hours of discharge by PACT nurses to evaluate their postdischarge state. Primary‐care physicians could be resident housestaff or ambulatory attending physicians. Seventy‐two percent of patients seen at the Denver VA have an assigned PCP.
Urgent Care
The Office‐based Medical Team provides UC and short‐term regular appointments for recently discharged medical patients or patients who require frequent follow‐up (such as those that require serial paracenteses). It is a separate clinic from an emergency department (ED)‐based walk‐in clinic. It is also available 5 days per week; patients are seen by resident housestaff unfamiliar with the patient, and the clinic is staffed with an ambulatory attending physician. Patients are commonly seen multiple times in the same clinic, though usually with different providers.
Postdischarge Clinic
The hospitalist‐run PDC is scheduled 2 afternoons per week. Patients are always seen by housestaff and medical students from the team that cared for them as an inpatient, then staffed with a rotating hospitalist attending who may have been the supervising inpatient attending during the patient's inpatient stay. Thus, continuity is preserved with the housestaff team in all cases, although attending continuity is variable. This is added to the daily responsibility of the resident and hospitalist physicians who are providing care on the inpatient service at the time of the clinic. Capabilities of the clinic are similar to UC and PCP clinics. Patients are usually seen once postdischarge with referral to the PCP for further follow‐up; however, patients can be seen multiple times by the same provider team.
If a patient followed up with multiple clinics, the first clinic visited determined the group to which that patient was allocated for the purpose of analysis. If a patient was scheduled for clinic follow‐up but did not attend within 30 days of discharge, he or she was excluded. We did not collect data on visits outside of these 3 clinics, as pilot data demonstrated they accounted for nearly all (>90%) of posthospitalization follow‐up visits. During the study period, there were no guidelines for discharging physicians about which clinic to have the patient follow up in. The UC and PDC were known to have better early access to follow‐up appointments and thus tended to see patients requiring early follow‐up in the judgment of the discharging clinician.
Statistical Analysis
The VA's Computing and Informatics Infrastructure (VINCI) was used to collect predischarge patient data for descriptive and analytic purposes. Pertinent potential confounders included patient age, sex, marital status, comorbidities, number of prescribed medications on discharge, previous hospital admissions in the last year, ICU admission (as a dichotomous variable), ICU length of stay (LOS), and hospital LOS. Postdischarge variables included time to first follow‐up appointment and hospital LOS if readmitted.
The primary outcome was a composite of ED visits, hospital readmissions, and mortality in the 30 days following hospital discharge. These outcomes were captured in the VA system; we did not measure outside utilization. A power analysis indicated that the sample has >90% power to detect small differences (4%) in the composite outcome between types of outpatient care. We also evaluated the effect of different types of follow‐up on the 3 individual components of the primary outcome. To compare baseline categorical variables across 3 groups, 2 trend tests were used; analysis of variance (ANOVA) or Kruskal‐Wallis test was used for continuous variables in univariate analysis.
We then used propensity scoring to adjust for baseline differences between groups in an attempt to adjust for referral bias, using multivariate logistic regression to calculate a propensity score for each patient in 2‐way comparisons, and a single score for every patient in a multinomial comparison.[23] Our final propensity score incorporated age, number of hospital admissions in the past year, and Elixhauser comorbidity score,[24] with excellent overlap in propensity scores between groups. Although hospital LOS was different between groups, inclusion in the propensity score did not reduce this significant difference, and its inclusion in the propensity model decreased model fit. Limitations of the accessible data prevented high‐dimensional propensity scoring and limited the outcome of the propensity score to attendance at the clinic assigned, rather than referral to the clinic assigned. The propensity score, hospital LOS, time to the first outpatient visit, and group assignment (PDC, PCP, UC) were entered into a multivariate logistic regression model.
To find a subgroup who may benefit most from follow‐up in the PDC, we a priori identified patients with one of the 5 discharge diagnosis‐related groups (DRGs) most commonly associated with subsequent readmission[9] and examined outcomes between the 3 different kinds of follow‐up, restricted to patients discharged with one of these diagnoses. All analyses were conducted using SAS 9.3 (SAS Institute, Inc., Cary, NC).
RESULTS
9952 patients who met criteria were discharged during this time period; however, 48.9% did not follow up with one of these clinics within 30 days, leaving 5085 patients in our analysis. Of these, 538 followed up in PDC (10.6%), 1848 followed up with their PCP (36.3%), and 2699 followed up in UC (53.1%). Table 1 presents predischarge characteristics of these patients. Patients seen in PDC were older and had a more significant comorbidity burden.
PDC, N=538 | UC, N=2699 | PCP, N=1848 | P Value | P Value After Propensity Adjustment | |
---|---|---|---|---|---|
| |||||
Age, years (SD) | 67.8 (12.6) | 67.1 (13.0) | 64.8 (13.0) | <0.01 | 0.86 |
Male sex, % | 95.0 | 95.4 | 94.4 | 0.33 | |
Marital status, % | |||||
Divorced | 40.2 | 36.2 | 35.0 | 0.09 | |
Married | 35.9 | 37.3 | 39.8 | 0.13 | |
Never married | 12.3 | 13.7 | 14.3 | 0.48 | |
LOS, days (SD) | 3.8 (3.6) | 5.0 (11.7) | 6.2 (10.8) | 0.04 | |
Elixhauser score (SD) | 0.80 (1.1) | 0.69 (1.0) | 0.75 (1.0) | 0.02 | 0.06 |
Admitted to ICU, % | 19.0 | 19.9 | 23.0 | 0.12 | |
ICU LOS, days (SD) | 2.8 (4.4) | 2.8 (3.4) | 2.3 (1.5) | 0.15 | |
Discharge medications, mean (SD) | 10.0 (6.7) | 10.4 (7.4) | 10.4 (8.2) | 0.37 | |
Admissions per patient in prior year, mean (SD) | 0.18 (0.5) | 0.21 (0.6) | 0.23 (0.6) | 0.08 | 0.78 |
Patients seen in PDC had a mean 2.4‐day shorter LOS than those seen by their PCPs (PDC: 3.8 days, UC: 5.0 days, PCP: 6.2 days; P=0.04 for comparison). Neither the percentage of patients admitted to the ICU during their index hospitalization nor the ICU LOS was different between groups. Patients were seen earlier postdischarge in PDC than in other types of follow‐up (PDC: 5.0 days, UC: 9.4 days, PCP: 13.7 days; P<0.01 for comparison). In univariate analysis, there was no difference between groups in the composite 30‐day outcome (Table 2). Analysis of the individual components of the primary outcome revealed significant differences in readmission rates, with PDC having the highest rate.
PDC | UC | PCP | P Value | P Value After Propensity Adjustment | |
---|---|---|---|---|---|
| |||||
Composite outcome, % | 19.9 | 18.3 | 17.5 | 0.42 | 0.30 |
Hospital readmission | 13.0 | 11.1 | 9.4 | 0.03 | 0.03 |
ED visit | 10.2 | 9.9 | 10.5 | 0.78 | 0.93 |
Mortality | 1.1 | 0.7 | 0.7 | 0.58 | 0.65 |
LOS if readmitted, days (SD) | 6.9 (18.1) | 4.9 (7.8) | 4.8 (6.5) | 0.28 | 0.23 |
Time to first visit after discharge, days (SD) | 5.0 (3.0) | 9.4 (6.1) | 13.8 (8.5) | <0.01 | <0.01 |
Univariate analyses conducted on predischarge characteristics after multinomial propensity scoring revealed significant differences between groups no longer existed for the variables that were included in the propensity score (age, Elixhauser score, and inpatient stays prior to visit; Table 1).
In multivariate analysis comparing PDC to PCP follow‐up, there was no difference in the composite outcome after controlling for propensity score and time to outpatient visit (odds ratio [OR]: 1.07, 95% confidence interval [CI]: 0.81‐1.40). Similar results were obtained in comparing PDC with UC (OR: 1.05, 95% CI: 0.82‐1.34) and in multinomial logistic regression comparing PDC with other types of follow‐up (PDC vs PCP: OR: 1.01, 95% CI: 0.78‐1.31; PDC vs UC: OR: 0.99, 95% CI: 0.78‐1.26).
Restricting the multivariate analysis to those patients discharged with one of the 5 discharge DRGs most associated with readmission did not alter our findings regarding the primary outcome. We also found no change in the composite outcome or any subcomponent of the composite outcome when restricting the analysis to 7‐day outcomes or when excluding scheduled readmissions (which represented <5% of all readmission).
DISCUSSION
A hospitalist‐run postdischarge clinic did not reduce a composite of 30‐day postdischarge adverse outcomes in our study when compared with primary‐care or urgent‐care follow‐up. In fact, patients who followed up in PDC had a small increase in 30‐day readmissions. However, they also were sicker at baseline, considered higher risk by the discharging physician, were able to be seen significantly earlier, and had an associated 2.4‐day shorter hospital LOS than patients seen by their PCPs.
Our findings do not confirm those of prior research in this area, which indicated outpatient follow‐up by the same physician who was the treating inpatient physician was linked to lower mortality rates, hospital readmissions, and ED utilization.[19, 20, 21] In fact, in our study, there was a significant (albeit small) increase in 30‐day readmissions in patients seen in PDC. There are significant challenges to the generalizability and validity of these prior studies. In one study, inpatient care was provided by outpatient primary‐care doctors in Canada,[19] a payer and care model rare in the United States.[25] In a second, usual care was not specified, and it is likely the reduction in ED visits resulted from provision of follow‐up care of any kind compared with those who did not follow up after discharge.[20] In a third, the PDC was part of a larger bundle of postdischarge interventions, and it only reduced ED visits when compared with patients who did not have follow‐up; rates of ED visits were similar in a comparison with PCP follow‐up.[21]
There are several possible explanations for the lack of improvement in 30‐day adverse outcomes with a hospitalist‐run PDC. First, although early access to early postdischarge care was improved and evidence suggests this is important in reducing readmissions,[11, 12] in populations similar to that studied, more postdischarge care has also been linked to increased readmissions.[22] This may be due to more frequent re‐evaluation of fragile, chronically ill patients, presenting more options for readmission. Second, the intervention currently only addresses some components of the Ideal Transition of Care[1] (Figure 1) and may benefit from an enhanced visit structure using a multidisciplinary approach. Third, the intervention took place in the context of a robust primary‐care system with a universal electronic medical record; the effects of improved access and continuity may be magnified in a system without these advantages. Fourth, there was a low readmission rate overall, and it is unclear how many of these readmissions were preventable. Finally, it may be that although the initial postdischarge care was adequate, readmissions occurred after the first visit, suggesting subsequent care during the 30 days postdischarge could have been improved.

The most likely explanation for the substantially decreased LOS associated with follow‐up in PDC is that inpatient physicians who knew they could see their own patients early in the postdischarge process were more tolerant of uncertainty surrounding the patients' clinical course.
For example, a frequent clinical conundrum for hospitalists is when to discharge patients improving on diuretic therapy for a heart failure exacerbation or antibiotics for cellulitis. Provided a PDC, these hospitalists may choose to discharge a patient still actively being treated, because they may feel they have access to early follow‐up to change course if needed as well as the ability to see the patient themselves, allowing precise evaluation of the change in their condition. Without this clinic, the hospitalist may wonder when postdischarge follow‐up will occur. They may be more hesitant to discharge a patient who has not fully completed treatment for fear he or she will still appear decompensated to the postdischarge provider (though greatly improved from admission), or will not have timely‐enough follow‐up to change treatment if the condition worsens.
Our finding that the LOS was still shorter when comparing PDC with UC suggests continuity may be a significant component of this effect. It seems unlikely that patients following up in PDC had less complex hospitalizations given similar ICU exposure and LOS, as well as older age and larger baseline comorbidity burden.
The LOS seen in patients who followed up in PDC was lower than Medicare rates[26] but similar to reported rates at other VA acute‐care hospitals.[27] It is consistent with prior findings that hospitalist care reduces LOS,[26] though the magnitude in our study was much larger than that in prior reports. Prior studies have suggested this decreased LOS is linked to increased adverse postdischarge outcomes, such as ED visits and readmissions, as well as increased costs and decreased discharges to home.[28] The PDC was not associated with increased postdischarge adverse events measured, though a formal cost analysis and analysis of other postdischarge outcomes, such as placement in a skilled nursing or rehabilitation facility after return home, could be assessed in future work.
The findings of our study should be interpreted in the context of the study design. Our study was retrospective, observational, and single‐center. There may have been additional baseline differences between groups predisposing to bias we did not capture in the propensity score. For example, we could not measure rates of attendance at the different clinics and cannot rule out that outcomes associated with PDC were also associated with increased attendance rates. However, none of the clinics had mechanisms in place to improve follow‐up rates; patients referred to PDC were those considered highest risk for readmission and were sicker at baseline, making it very unlikely that they were predisposed to attend clinic more frequently and/or to have better outcomes; and even if PDC improved follow‐up rates, this would be a significant contribution given the limitations of primary‐care access. Our propensity score could not perfectly mimic randomization to a treatment assignment, but rather to treatment received, because of this limitation.
We did not ascertain ED visits or readmissions outside the VA system; it is possible these differentially affected one group more than another, though this seems unlikely. Our patient population was representative of veteran populations elsewhere who are at high risk of adverse postdischarge outcomes, but our findings may not be generalizable to younger, more ethnically diverse populations or to women.
CONCLUSIONS
Provision of postdischarge care by hospitalists may reduce LOS without increasing postdischarge adverse events. Further work is required to evaluate the role of hospitalist‐run PDCs in healthcare systems with more limited postdischarge access to care, to formally evaluate the costs associated with extending hospitalists to the outpatient setting, and to prospectively evaluate the role of a PDC compared with other kinds of hospital follow‐up.
Acknowledgments
The authors thank Melver Anderson, MD, for editorial assistance with the manuscript.
Disclosures: Dr. Burke had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Dr. Burke was supported by grant funding from the Colorado Research Enhancement Award Program to Improve Care Coordination for Veterans. Initial results of this study were presented at the Society of General Internal Medicine National Meeting in Denver, Colorado, April 24, 2013.
- Moving beyond readmission penalties: creating an ideal process to improve transitional care. J Hosp Med. 2013;8(2):102–109. , , , .
- Continuity of outpatient and inpatient care by primary care physicians for hospitalized older adults. JAMA. 2009;301(16):1671–1680. , , , , , .
- Trends in inpatient continuity of care for a cohort of Medicare patients 1996–2006. J Hosp Med. 2011;6(8):438–444. , , , , .
- Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831–841. , , , , , .
- Posthospital care transitions: patterns, complications, and risk identification. Health Serv Res. 2004;39(5):1449–1465. , , , .
- Patient experiences of transitioning from hospital to home: an ethnographic quality improvement project. J Hosp Med. 2012;7(5):382–387. , , , , .
- Understanding and execution of discharge instructions. Am J Med Qual. 2013;28(5):383–391. , , , et al.
- The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167. , , , , .
- Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428. , , .
- Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100–102. .
- Relationship between early physician follow‐up and 30‐day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303(17):1716–1722. , , , et al.
- Post‐hospitalization transitions: examining the effects of timing of primary care provider follow‐up. J Hosp Med. 2010;5(7):392–397. , , .
- Will generalist physician supply meet demands of an increasing and aging population? Health Aff (Millwood). 2008;27(3):w232–w241. , , .
- Association of American Medical Colleges. The Impact of Health Care Reform on the Future Supply and Demand for Physicians: Updated Projections Through 2025. Available at: http://www.aamc.org/download/158076/data/updated_projections_through_2025.pdf. Published June 2010. Accessed May 1, 2012.
- Effect of discharge summary availability during post‐discharge visits on hospital readmission. J Gen Intern Med. 2002;17(3):186–192. , , , .
- Comprehensive quality of discharge summaries at an academic medical center. J Hosp Med. 2013;8(8):436–443. , , , et al.
- Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381–386. , , , et al.
- Is a post‐discharge clinic in your hospital's future? Available at: http://www.the‐hospitalist.org/details/article/1409011/Is_a_Post‐Discharge_Clinic_in_Your_Hospitals_Future.html. Published December 2011. Accessed May 1, 2013. .
- Continuity of care and patient outcomes after hospital discharge. J Gen Intern Med. 2004;19(6):624–631. , , , .
- Effects of a postdischarge clinic on housestaff satisfaction and utilization of hospital services. J Gen Intern Med. 1996;11(3):179–181. , , , .
- Integrated postdischarge transitional care in a hospitalist system to improve discharge outcome: an experimental study. BMC Med. 2011;9:96. , , , , , .
- Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission. N Engl J Med. 1996;334(22):1441–1447. , , .
- An overview of the objectives of and the approaches to propensity score analyses. Eur Heart J. 2011;32(14):1704–1708. , .
- Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care. 2004;42(4):355–360. , , .
- Growth in the care of older patients by hospitalists in the United States. N Engl J Med. 2009;360(11):1102–1112. , , , .
- Association of hospitalist care with medical utilization after discharge: evidence of cost shift from a cohort study. Ann Intern Med. 2011;155(3):152–159. , .
- Associations between reduced hospital length of stay and 30‐day readmission rate and mortality: 14‐year experience in 129 Veterans Affairs hospitals. Ann Intern Med. 2012;157(12):837–845. , , , et al.
- Variation in length of stay and outcomes among hospitalized patients attributable to hospitals and hospitalists. J Gen Intern Med. 2013;28(3):370–376. , , , .
Currently, healthcare systems rarely provide ideal transitions of care for discharged patients,[1] resulting in fragmented care,[2, 3, 4, 5] significant patient uncertainty about how to manage at home,[6, 7] and frequent adverse events.[8, 9] These factors are so commonly experienced by discharged patients that they are recognizable as a postdischarge syndrome.[10]
One element important for reducing the postdischarge risk of adverse events is provision of adequate follow‐up.[11, 12] However, supplying this care is challenging in the modern era, and it will become progressively more difficult to achieve. In 2004, 50% of readmitted Medicare fee‐for‐service patients had no postdischarge visit within 30 days of their discharge,[9] likely due in part to difficulty arranging such care. Changes in insurance coverage and demographics are expected to result in more than 100 million newly insured patients by 2019, yet the primary‐care workforce is projected to begin shrinking by 2016.[13, 14] In the increasingly uncommon situation that a primary‐care clinician is available promptly after discharge, information transfer is often inadequate[4, 15, 16, 17] and can be exacerbated by the growing discontinuity between inpatient and outpatient care.[2, 3, 4] Efforts to increase the supply of primary‐care clinicians and thereby improve early access to postdischarge care are important for the future, but hospitals, particularly those penalized for high risk‐adjusted readmission rates, are seeking novel solutions now.
One increasingly common innovation is to extend the role of inpatient providers (usually hospitalists) into the postdischarge period.[18] Preliminary evidence suggests improved continuity[19] and access[20] achieved by providing this care may decrease postdischarge adverse events,[19, 20, 21] though evidence is conflicting.[22]
As a closed, multilevel healthcare system, the Denver VA Medical Center is uniquely positioned to evaluate the influence of alternative postdischarge‐care strategies on subsequent adverse events. Discharged patients are seen in a well‐established hospitalist‐run postdischarge clinic (PDC), a robust urgent‐care system (UC), or by a large primary‐care provider (PCP) practice. The purpose of this study was to evaluate whether patients seen in a hospitalist‐run PDC have reduced adverse outcomes in the 30 days following hospital discharge compared with follow‐up with the patient's PCP or in an UC clinic.
METHODS
Patients
This was a retrospective cohort study of consecutive adult patients discharged from the general medical services of the Denver VA Medical Center after a nonelective admission between January 2005 and August 2012. This time range was chosen because all 3 clinics were fully operational during this period. The Denver VA Medical Center is an academically‐affiliated 128‐bed hospital that provides a full range of tertiary services. All medical patients, including intensive care unit (ICU) patients, are cared for on general medical teams by University of Colorado housestaff with hospitalists or subspecialty attendings. Patients who lived in the Denver metropolitan area, were discharged home, and who followed up with a PCP, UC clinic, or PDC within 30 days of discharge were included. Patients discharged to subacute facilities, hospice, or this tends to be capitalized as a special program at our VA were excluded. For patients with multiple admissions, only the first was included.
Clinics
Primary Care
Primary‐care clinics in the VA system are organized into Patient‐Aligned Care Teams (PACTs) and are available for appointments 5 days per week. Patients discharged from the medical service who have PCPs are called within 48 hours of discharge by PACT nurses to evaluate their postdischarge state. Primary‐care physicians could be resident housestaff or ambulatory attending physicians. Seventy‐two percent of patients seen at the Denver VA have an assigned PCP.
Urgent Care
The Office‐based Medical Team provides UC and short‐term regular appointments for recently discharged medical patients or patients who require frequent follow‐up (such as those that require serial paracenteses). It is a separate clinic from an emergency department (ED)‐based walk‐in clinic. It is also available 5 days per week; patients are seen by resident housestaff unfamiliar with the patient, and the clinic is staffed with an ambulatory attending physician. Patients are commonly seen multiple times in the same clinic, though usually with different providers.
Postdischarge Clinic
The hospitalist‐run PDC is scheduled 2 afternoons per week. Patients are always seen by housestaff and medical students from the team that cared for them as an inpatient, then staffed with a rotating hospitalist attending who may have been the supervising inpatient attending during the patient's inpatient stay. Thus, continuity is preserved with the housestaff team in all cases, although attending continuity is variable. This is added to the daily responsibility of the resident and hospitalist physicians who are providing care on the inpatient service at the time of the clinic. Capabilities of the clinic are similar to UC and PCP clinics. Patients are usually seen once postdischarge with referral to the PCP for further follow‐up; however, patients can be seen multiple times by the same provider team.
If a patient followed up with multiple clinics, the first clinic visited determined the group to which that patient was allocated for the purpose of analysis. If a patient was scheduled for clinic follow‐up but did not attend within 30 days of discharge, he or she was excluded. We did not collect data on visits outside of these 3 clinics, as pilot data demonstrated they accounted for nearly all (>90%) of posthospitalization follow‐up visits. During the study period, there were no guidelines for discharging physicians about which clinic to have the patient follow up in. The UC and PDC were known to have better early access to follow‐up appointments and thus tended to see patients requiring early follow‐up in the judgment of the discharging clinician.
Statistical Analysis
The VA's Computing and Informatics Infrastructure (VINCI) was used to collect predischarge patient data for descriptive and analytic purposes. Pertinent potential confounders included patient age, sex, marital status, comorbidities, number of prescribed medications on discharge, previous hospital admissions in the last year, ICU admission (as a dichotomous variable), ICU length of stay (LOS), and hospital LOS. Postdischarge variables included time to first follow‐up appointment and hospital LOS if readmitted.
The primary outcome was a composite of ED visits, hospital readmissions, and mortality in the 30 days following hospital discharge. These outcomes were captured in the VA system; we did not measure outside utilization. A power analysis indicated that the sample has >90% power to detect small differences (4%) in the composite outcome between types of outpatient care. We also evaluated the effect of different types of follow‐up on the 3 individual components of the primary outcome. To compare baseline categorical variables across 3 groups, 2 trend tests were used; analysis of variance (ANOVA) or Kruskal‐Wallis test was used for continuous variables in univariate analysis.
We then used propensity scoring to adjust for baseline differences between groups in an attempt to adjust for referral bias, using multivariate logistic regression to calculate a propensity score for each patient in 2‐way comparisons, and a single score for every patient in a multinomial comparison.[23] Our final propensity score incorporated age, number of hospital admissions in the past year, and Elixhauser comorbidity score,[24] with excellent overlap in propensity scores between groups. Although hospital LOS was different between groups, inclusion in the propensity score did not reduce this significant difference, and its inclusion in the propensity model decreased model fit. Limitations of the accessible data prevented high‐dimensional propensity scoring and limited the outcome of the propensity score to attendance at the clinic assigned, rather than referral to the clinic assigned. The propensity score, hospital LOS, time to the first outpatient visit, and group assignment (PDC, PCP, UC) were entered into a multivariate logistic regression model.
To find a subgroup who may benefit most from follow‐up in the PDC, we a priori identified patients with one of the 5 discharge diagnosis‐related groups (DRGs) most commonly associated with subsequent readmission[9] and examined outcomes between the 3 different kinds of follow‐up, restricted to patients discharged with one of these diagnoses. All analyses were conducted using SAS 9.3 (SAS Institute, Inc., Cary, NC).
RESULTS
9952 patients who met criteria were discharged during this time period; however, 48.9% did not follow up with one of these clinics within 30 days, leaving 5085 patients in our analysis. Of these, 538 followed up in PDC (10.6%), 1848 followed up with their PCP (36.3%), and 2699 followed up in UC (53.1%). Table 1 presents predischarge characteristics of these patients. Patients seen in PDC were older and had a more significant comorbidity burden.
PDC, N=538 | UC, N=2699 | PCP, N=1848 | P Value | P Value After Propensity Adjustment | |
---|---|---|---|---|---|
| |||||
Age, years (SD) | 67.8 (12.6) | 67.1 (13.0) | 64.8 (13.0) | <0.01 | 0.86 |
Male sex, % | 95.0 | 95.4 | 94.4 | 0.33 | |
Marital status, % | |||||
Divorced | 40.2 | 36.2 | 35.0 | 0.09 | |
Married | 35.9 | 37.3 | 39.8 | 0.13 | |
Never married | 12.3 | 13.7 | 14.3 | 0.48 | |
LOS, days (SD) | 3.8 (3.6) | 5.0 (11.7) | 6.2 (10.8) | 0.04 | |
Elixhauser score (SD) | 0.80 (1.1) | 0.69 (1.0) | 0.75 (1.0) | 0.02 | 0.06 |
Admitted to ICU, % | 19.0 | 19.9 | 23.0 | 0.12 | |
ICU LOS, days (SD) | 2.8 (4.4) | 2.8 (3.4) | 2.3 (1.5) | 0.15 | |
Discharge medications, mean (SD) | 10.0 (6.7) | 10.4 (7.4) | 10.4 (8.2) | 0.37 | |
Admissions per patient in prior year, mean (SD) | 0.18 (0.5) | 0.21 (0.6) | 0.23 (0.6) | 0.08 | 0.78 |
Patients seen in PDC had a mean 2.4‐day shorter LOS than those seen by their PCPs (PDC: 3.8 days, UC: 5.0 days, PCP: 6.2 days; P=0.04 for comparison). Neither the percentage of patients admitted to the ICU during their index hospitalization nor the ICU LOS was different between groups. Patients were seen earlier postdischarge in PDC than in other types of follow‐up (PDC: 5.0 days, UC: 9.4 days, PCP: 13.7 days; P<0.01 for comparison). In univariate analysis, there was no difference between groups in the composite 30‐day outcome (Table 2). Analysis of the individual components of the primary outcome revealed significant differences in readmission rates, with PDC having the highest rate.
PDC | UC | PCP | P Value | P Value After Propensity Adjustment | |
---|---|---|---|---|---|
| |||||
Composite outcome, % | 19.9 | 18.3 | 17.5 | 0.42 | 0.30 |
Hospital readmission | 13.0 | 11.1 | 9.4 | 0.03 | 0.03 |
ED visit | 10.2 | 9.9 | 10.5 | 0.78 | 0.93 |
Mortality | 1.1 | 0.7 | 0.7 | 0.58 | 0.65 |
LOS if readmitted, days (SD) | 6.9 (18.1) | 4.9 (7.8) | 4.8 (6.5) | 0.28 | 0.23 |
Time to first visit after discharge, days (SD) | 5.0 (3.0) | 9.4 (6.1) | 13.8 (8.5) | <0.01 | <0.01 |
Univariate analyses conducted on predischarge characteristics after multinomial propensity scoring revealed significant differences between groups no longer existed for the variables that were included in the propensity score (age, Elixhauser score, and inpatient stays prior to visit; Table 1).
In multivariate analysis comparing PDC to PCP follow‐up, there was no difference in the composite outcome after controlling for propensity score and time to outpatient visit (odds ratio [OR]: 1.07, 95% confidence interval [CI]: 0.81‐1.40). Similar results were obtained in comparing PDC with UC (OR: 1.05, 95% CI: 0.82‐1.34) and in multinomial logistic regression comparing PDC with other types of follow‐up (PDC vs PCP: OR: 1.01, 95% CI: 0.78‐1.31; PDC vs UC: OR: 0.99, 95% CI: 0.78‐1.26).
Restricting the multivariate analysis to those patients discharged with one of the 5 discharge DRGs most associated with readmission did not alter our findings regarding the primary outcome. We also found no change in the composite outcome or any subcomponent of the composite outcome when restricting the analysis to 7‐day outcomes or when excluding scheduled readmissions (which represented <5% of all readmission).
DISCUSSION
A hospitalist‐run postdischarge clinic did not reduce a composite of 30‐day postdischarge adverse outcomes in our study when compared with primary‐care or urgent‐care follow‐up. In fact, patients who followed up in PDC had a small increase in 30‐day readmissions. However, they also were sicker at baseline, considered higher risk by the discharging physician, were able to be seen significantly earlier, and had an associated 2.4‐day shorter hospital LOS than patients seen by their PCPs.
Our findings do not confirm those of prior research in this area, which indicated outpatient follow‐up by the same physician who was the treating inpatient physician was linked to lower mortality rates, hospital readmissions, and ED utilization.[19, 20, 21] In fact, in our study, there was a significant (albeit small) increase in 30‐day readmissions in patients seen in PDC. There are significant challenges to the generalizability and validity of these prior studies. In one study, inpatient care was provided by outpatient primary‐care doctors in Canada,[19] a payer and care model rare in the United States.[25] In a second, usual care was not specified, and it is likely the reduction in ED visits resulted from provision of follow‐up care of any kind compared with those who did not follow up after discharge.[20] In a third, the PDC was part of a larger bundle of postdischarge interventions, and it only reduced ED visits when compared with patients who did not have follow‐up; rates of ED visits were similar in a comparison with PCP follow‐up.[21]
There are several possible explanations for the lack of improvement in 30‐day adverse outcomes with a hospitalist‐run PDC. First, although early access to early postdischarge care was improved and evidence suggests this is important in reducing readmissions,[11, 12] in populations similar to that studied, more postdischarge care has also been linked to increased readmissions.[22] This may be due to more frequent re‐evaluation of fragile, chronically ill patients, presenting more options for readmission. Second, the intervention currently only addresses some components of the Ideal Transition of Care[1] (Figure 1) and may benefit from an enhanced visit structure using a multidisciplinary approach. Third, the intervention took place in the context of a robust primary‐care system with a universal electronic medical record; the effects of improved access and continuity may be magnified in a system without these advantages. Fourth, there was a low readmission rate overall, and it is unclear how many of these readmissions were preventable. Finally, it may be that although the initial postdischarge care was adequate, readmissions occurred after the first visit, suggesting subsequent care during the 30 days postdischarge could have been improved.

The most likely explanation for the substantially decreased LOS associated with follow‐up in PDC is that inpatient physicians who knew they could see their own patients early in the postdischarge process were more tolerant of uncertainty surrounding the patients' clinical course.
For example, a frequent clinical conundrum for hospitalists is when to discharge patients improving on diuretic therapy for a heart failure exacerbation or antibiotics for cellulitis. Provided a PDC, these hospitalists may choose to discharge a patient still actively being treated, because they may feel they have access to early follow‐up to change course if needed as well as the ability to see the patient themselves, allowing precise evaluation of the change in their condition. Without this clinic, the hospitalist may wonder when postdischarge follow‐up will occur. They may be more hesitant to discharge a patient who has not fully completed treatment for fear he or she will still appear decompensated to the postdischarge provider (though greatly improved from admission), or will not have timely‐enough follow‐up to change treatment if the condition worsens.
Our finding that the LOS was still shorter when comparing PDC with UC suggests continuity may be a significant component of this effect. It seems unlikely that patients following up in PDC had less complex hospitalizations given similar ICU exposure and LOS, as well as older age and larger baseline comorbidity burden.
The LOS seen in patients who followed up in PDC was lower than Medicare rates[26] but similar to reported rates at other VA acute‐care hospitals.[27] It is consistent with prior findings that hospitalist care reduces LOS,[26] though the magnitude in our study was much larger than that in prior reports. Prior studies have suggested this decreased LOS is linked to increased adverse postdischarge outcomes, such as ED visits and readmissions, as well as increased costs and decreased discharges to home.[28] The PDC was not associated with increased postdischarge adverse events measured, though a formal cost analysis and analysis of other postdischarge outcomes, such as placement in a skilled nursing or rehabilitation facility after return home, could be assessed in future work.
The findings of our study should be interpreted in the context of the study design. Our study was retrospective, observational, and single‐center. There may have been additional baseline differences between groups predisposing to bias we did not capture in the propensity score. For example, we could not measure rates of attendance at the different clinics and cannot rule out that outcomes associated with PDC were also associated with increased attendance rates. However, none of the clinics had mechanisms in place to improve follow‐up rates; patients referred to PDC were those considered highest risk for readmission and were sicker at baseline, making it very unlikely that they were predisposed to attend clinic more frequently and/or to have better outcomes; and even if PDC improved follow‐up rates, this would be a significant contribution given the limitations of primary‐care access. Our propensity score could not perfectly mimic randomization to a treatment assignment, but rather to treatment received, because of this limitation.
We did not ascertain ED visits or readmissions outside the VA system; it is possible these differentially affected one group more than another, though this seems unlikely. Our patient population was representative of veteran populations elsewhere who are at high risk of adverse postdischarge outcomes, but our findings may not be generalizable to younger, more ethnically diverse populations or to women.
CONCLUSIONS
Provision of postdischarge care by hospitalists may reduce LOS without increasing postdischarge adverse events. Further work is required to evaluate the role of hospitalist‐run PDCs in healthcare systems with more limited postdischarge access to care, to formally evaluate the costs associated with extending hospitalists to the outpatient setting, and to prospectively evaluate the role of a PDC compared with other kinds of hospital follow‐up.
Acknowledgments
The authors thank Melver Anderson, MD, for editorial assistance with the manuscript.
Disclosures: Dr. Burke had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Dr. Burke was supported by grant funding from the Colorado Research Enhancement Award Program to Improve Care Coordination for Veterans. Initial results of this study were presented at the Society of General Internal Medicine National Meeting in Denver, Colorado, April 24, 2013.
Currently, healthcare systems rarely provide ideal transitions of care for discharged patients,[1] resulting in fragmented care,[2, 3, 4, 5] significant patient uncertainty about how to manage at home,[6, 7] and frequent adverse events.[8, 9] These factors are so commonly experienced by discharged patients that they are recognizable as a postdischarge syndrome.[10]
One element important for reducing the postdischarge risk of adverse events is provision of adequate follow‐up.[11, 12] However, supplying this care is challenging in the modern era, and it will become progressively more difficult to achieve. In 2004, 50% of readmitted Medicare fee‐for‐service patients had no postdischarge visit within 30 days of their discharge,[9] likely due in part to difficulty arranging such care. Changes in insurance coverage and demographics are expected to result in more than 100 million newly insured patients by 2019, yet the primary‐care workforce is projected to begin shrinking by 2016.[13, 14] In the increasingly uncommon situation that a primary‐care clinician is available promptly after discharge, information transfer is often inadequate[4, 15, 16, 17] and can be exacerbated by the growing discontinuity between inpatient and outpatient care.[2, 3, 4] Efforts to increase the supply of primary‐care clinicians and thereby improve early access to postdischarge care are important for the future, but hospitals, particularly those penalized for high risk‐adjusted readmission rates, are seeking novel solutions now.
One increasingly common innovation is to extend the role of inpatient providers (usually hospitalists) into the postdischarge period.[18] Preliminary evidence suggests improved continuity[19] and access[20] achieved by providing this care may decrease postdischarge adverse events,[19, 20, 21] though evidence is conflicting.[22]
As a closed, multilevel healthcare system, the Denver VA Medical Center is uniquely positioned to evaluate the influence of alternative postdischarge‐care strategies on subsequent adverse events. Discharged patients are seen in a well‐established hospitalist‐run postdischarge clinic (PDC), a robust urgent‐care system (UC), or by a large primary‐care provider (PCP) practice. The purpose of this study was to evaluate whether patients seen in a hospitalist‐run PDC have reduced adverse outcomes in the 30 days following hospital discharge compared with follow‐up with the patient's PCP or in an UC clinic.
METHODS
Patients
This was a retrospective cohort study of consecutive adult patients discharged from the general medical services of the Denver VA Medical Center after a nonelective admission between January 2005 and August 2012. This time range was chosen because all 3 clinics were fully operational during this period. The Denver VA Medical Center is an academically‐affiliated 128‐bed hospital that provides a full range of tertiary services. All medical patients, including intensive care unit (ICU) patients, are cared for on general medical teams by University of Colorado housestaff with hospitalists or subspecialty attendings. Patients who lived in the Denver metropolitan area, were discharged home, and who followed up with a PCP, UC clinic, or PDC within 30 days of discharge were included. Patients discharged to subacute facilities, hospice, or this tends to be capitalized as a special program at our VA were excluded. For patients with multiple admissions, only the first was included.
Clinics
Primary Care
Primary‐care clinics in the VA system are organized into Patient‐Aligned Care Teams (PACTs) and are available for appointments 5 days per week. Patients discharged from the medical service who have PCPs are called within 48 hours of discharge by PACT nurses to evaluate their postdischarge state. Primary‐care physicians could be resident housestaff or ambulatory attending physicians. Seventy‐two percent of patients seen at the Denver VA have an assigned PCP.
Urgent Care
The Office‐based Medical Team provides UC and short‐term regular appointments for recently discharged medical patients or patients who require frequent follow‐up (such as those that require serial paracenteses). It is a separate clinic from an emergency department (ED)‐based walk‐in clinic. It is also available 5 days per week; patients are seen by resident housestaff unfamiliar with the patient, and the clinic is staffed with an ambulatory attending physician. Patients are commonly seen multiple times in the same clinic, though usually with different providers.
Postdischarge Clinic
The hospitalist‐run PDC is scheduled 2 afternoons per week. Patients are always seen by housestaff and medical students from the team that cared for them as an inpatient, then staffed with a rotating hospitalist attending who may have been the supervising inpatient attending during the patient's inpatient stay. Thus, continuity is preserved with the housestaff team in all cases, although attending continuity is variable. This is added to the daily responsibility of the resident and hospitalist physicians who are providing care on the inpatient service at the time of the clinic. Capabilities of the clinic are similar to UC and PCP clinics. Patients are usually seen once postdischarge with referral to the PCP for further follow‐up; however, patients can be seen multiple times by the same provider team.
If a patient followed up with multiple clinics, the first clinic visited determined the group to which that patient was allocated for the purpose of analysis. If a patient was scheduled for clinic follow‐up but did not attend within 30 days of discharge, he or she was excluded. We did not collect data on visits outside of these 3 clinics, as pilot data demonstrated they accounted for nearly all (>90%) of posthospitalization follow‐up visits. During the study period, there were no guidelines for discharging physicians about which clinic to have the patient follow up in. The UC and PDC were known to have better early access to follow‐up appointments and thus tended to see patients requiring early follow‐up in the judgment of the discharging clinician.
Statistical Analysis
The VA's Computing and Informatics Infrastructure (VINCI) was used to collect predischarge patient data for descriptive and analytic purposes. Pertinent potential confounders included patient age, sex, marital status, comorbidities, number of prescribed medications on discharge, previous hospital admissions in the last year, ICU admission (as a dichotomous variable), ICU length of stay (LOS), and hospital LOS. Postdischarge variables included time to first follow‐up appointment and hospital LOS if readmitted.
The primary outcome was a composite of ED visits, hospital readmissions, and mortality in the 30 days following hospital discharge. These outcomes were captured in the VA system; we did not measure outside utilization. A power analysis indicated that the sample has >90% power to detect small differences (4%) in the composite outcome between types of outpatient care. We also evaluated the effect of different types of follow‐up on the 3 individual components of the primary outcome. To compare baseline categorical variables across 3 groups, 2 trend tests were used; analysis of variance (ANOVA) or Kruskal‐Wallis test was used for continuous variables in univariate analysis.
We then used propensity scoring to adjust for baseline differences between groups in an attempt to adjust for referral bias, using multivariate logistic regression to calculate a propensity score for each patient in 2‐way comparisons, and a single score for every patient in a multinomial comparison.[23] Our final propensity score incorporated age, number of hospital admissions in the past year, and Elixhauser comorbidity score,[24] with excellent overlap in propensity scores between groups. Although hospital LOS was different between groups, inclusion in the propensity score did not reduce this significant difference, and its inclusion in the propensity model decreased model fit. Limitations of the accessible data prevented high‐dimensional propensity scoring and limited the outcome of the propensity score to attendance at the clinic assigned, rather than referral to the clinic assigned. The propensity score, hospital LOS, time to the first outpatient visit, and group assignment (PDC, PCP, UC) were entered into a multivariate logistic regression model.
To find a subgroup who may benefit most from follow‐up in the PDC, we a priori identified patients with one of the 5 discharge diagnosis‐related groups (DRGs) most commonly associated with subsequent readmission[9] and examined outcomes between the 3 different kinds of follow‐up, restricted to patients discharged with one of these diagnoses. All analyses were conducted using SAS 9.3 (SAS Institute, Inc., Cary, NC).
RESULTS
9952 patients who met criteria were discharged during this time period; however, 48.9% did not follow up with one of these clinics within 30 days, leaving 5085 patients in our analysis. Of these, 538 followed up in PDC (10.6%), 1848 followed up with their PCP (36.3%), and 2699 followed up in UC (53.1%). Table 1 presents predischarge characteristics of these patients. Patients seen in PDC were older and had a more significant comorbidity burden.
PDC, N=538 | UC, N=2699 | PCP, N=1848 | P Value | P Value After Propensity Adjustment | |
---|---|---|---|---|---|
| |||||
Age, years (SD) | 67.8 (12.6) | 67.1 (13.0) | 64.8 (13.0) | <0.01 | 0.86 |
Male sex, % | 95.0 | 95.4 | 94.4 | 0.33 | |
Marital status, % | |||||
Divorced | 40.2 | 36.2 | 35.0 | 0.09 | |
Married | 35.9 | 37.3 | 39.8 | 0.13 | |
Never married | 12.3 | 13.7 | 14.3 | 0.48 | |
LOS, days (SD) | 3.8 (3.6) | 5.0 (11.7) | 6.2 (10.8) | 0.04 | |
Elixhauser score (SD) | 0.80 (1.1) | 0.69 (1.0) | 0.75 (1.0) | 0.02 | 0.06 |
Admitted to ICU, % | 19.0 | 19.9 | 23.0 | 0.12 | |
ICU LOS, days (SD) | 2.8 (4.4) | 2.8 (3.4) | 2.3 (1.5) | 0.15 | |
Discharge medications, mean (SD) | 10.0 (6.7) | 10.4 (7.4) | 10.4 (8.2) | 0.37 | |
Admissions per patient in prior year, mean (SD) | 0.18 (0.5) | 0.21 (0.6) | 0.23 (0.6) | 0.08 | 0.78 |
Patients seen in PDC had a mean 2.4‐day shorter LOS than those seen by their PCPs (PDC: 3.8 days, UC: 5.0 days, PCP: 6.2 days; P=0.04 for comparison). Neither the percentage of patients admitted to the ICU during their index hospitalization nor the ICU LOS was different between groups. Patients were seen earlier postdischarge in PDC than in other types of follow‐up (PDC: 5.0 days, UC: 9.4 days, PCP: 13.7 days; P<0.01 for comparison). In univariate analysis, there was no difference between groups in the composite 30‐day outcome (Table 2). Analysis of the individual components of the primary outcome revealed significant differences in readmission rates, with PDC having the highest rate.
PDC | UC | PCP | P Value | P Value After Propensity Adjustment | |
---|---|---|---|---|---|
| |||||
Composite outcome, % | 19.9 | 18.3 | 17.5 | 0.42 | 0.30 |
Hospital readmission | 13.0 | 11.1 | 9.4 | 0.03 | 0.03 |
ED visit | 10.2 | 9.9 | 10.5 | 0.78 | 0.93 |
Mortality | 1.1 | 0.7 | 0.7 | 0.58 | 0.65 |
LOS if readmitted, days (SD) | 6.9 (18.1) | 4.9 (7.8) | 4.8 (6.5) | 0.28 | 0.23 |
Time to first visit after discharge, days (SD) | 5.0 (3.0) | 9.4 (6.1) | 13.8 (8.5) | <0.01 | <0.01 |
Univariate analyses conducted on predischarge characteristics after multinomial propensity scoring revealed significant differences between groups no longer existed for the variables that were included in the propensity score (age, Elixhauser score, and inpatient stays prior to visit; Table 1).
In multivariate analysis comparing PDC to PCP follow‐up, there was no difference in the composite outcome after controlling for propensity score and time to outpatient visit (odds ratio [OR]: 1.07, 95% confidence interval [CI]: 0.81‐1.40). Similar results were obtained in comparing PDC with UC (OR: 1.05, 95% CI: 0.82‐1.34) and in multinomial logistic regression comparing PDC with other types of follow‐up (PDC vs PCP: OR: 1.01, 95% CI: 0.78‐1.31; PDC vs UC: OR: 0.99, 95% CI: 0.78‐1.26).
Restricting the multivariate analysis to those patients discharged with one of the 5 discharge DRGs most associated with readmission did not alter our findings regarding the primary outcome. We also found no change in the composite outcome or any subcomponent of the composite outcome when restricting the analysis to 7‐day outcomes or when excluding scheduled readmissions (which represented <5% of all readmission).
DISCUSSION
A hospitalist‐run postdischarge clinic did not reduce a composite of 30‐day postdischarge adverse outcomes in our study when compared with primary‐care or urgent‐care follow‐up. In fact, patients who followed up in PDC had a small increase in 30‐day readmissions. However, they also were sicker at baseline, considered higher risk by the discharging physician, were able to be seen significantly earlier, and had an associated 2.4‐day shorter hospital LOS than patients seen by their PCPs.
Our findings do not confirm those of prior research in this area, which indicated outpatient follow‐up by the same physician who was the treating inpatient physician was linked to lower mortality rates, hospital readmissions, and ED utilization.[19, 20, 21] In fact, in our study, there was a significant (albeit small) increase in 30‐day readmissions in patients seen in PDC. There are significant challenges to the generalizability and validity of these prior studies. In one study, inpatient care was provided by outpatient primary‐care doctors in Canada,[19] a payer and care model rare in the United States.[25] In a second, usual care was not specified, and it is likely the reduction in ED visits resulted from provision of follow‐up care of any kind compared with those who did not follow up after discharge.[20] In a third, the PDC was part of a larger bundle of postdischarge interventions, and it only reduced ED visits when compared with patients who did not have follow‐up; rates of ED visits were similar in a comparison with PCP follow‐up.[21]
There are several possible explanations for the lack of improvement in 30‐day adverse outcomes with a hospitalist‐run PDC. First, although early access to early postdischarge care was improved and evidence suggests this is important in reducing readmissions,[11, 12] in populations similar to that studied, more postdischarge care has also been linked to increased readmissions.[22] This may be due to more frequent re‐evaluation of fragile, chronically ill patients, presenting more options for readmission. Second, the intervention currently only addresses some components of the Ideal Transition of Care[1] (Figure 1) and may benefit from an enhanced visit structure using a multidisciplinary approach. Third, the intervention took place in the context of a robust primary‐care system with a universal electronic medical record; the effects of improved access and continuity may be magnified in a system without these advantages. Fourth, there was a low readmission rate overall, and it is unclear how many of these readmissions were preventable. Finally, it may be that although the initial postdischarge care was adequate, readmissions occurred after the first visit, suggesting subsequent care during the 30 days postdischarge could have been improved.

The most likely explanation for the substantially decreased LOS associated with follow‐up in PDC is that inpatient physicians who knew they could see their own patients early in the postdischarge process were more tolerant of uncertainty surrounding the patients' clinical course.
For example, a frequent clinical conundrum for hospitalists is when to discharge patients improving on diuretic therapy for a heart failure exacerbation or antibiotics for cellulitis. Provided a PDC, these hospitalists may choose to discharge a patient still actively being treated, because they may feel they have access to early follow‐up to change course if needed as well as the ability to see the patient themselves, allowing precise evaluation of the change in their condition. Without this clinic, the hospitalist may wonder when postdischarge follow‐up will occur. They may be more hesitant to discharge a patient who has not fully completed treatment for fear he or she will still appear decompensated to the postdischarge provider (though greatly improved from admission), or will not have timely‐enough follow‐up to change treatment if the condition worsens.
Our finding that the LOS was still shorter when comparing PDC with UC suggests continuity may be a significant component of this effect. It seems unlikely that patients following up in PDC had less complex hospitalizations given similar ICU exposure and LOS, as well as older age and larger baseline comorbidity burden.
The LOS seen in patients who followed up in PDC was lower than Medicare rates[26] but similar to reported rates at other VA acute‐care hospitals.[27] It is consistent with prior findings that hospitalist care reduces LOS,[26] though the magnitude in our study was much larger than that in prior reports. Prior studies have suggested this decreased LOS is linked to increased adverse postdischarge outcomes, such as ED visits and readmissions, as well as increased costs and decreased discharges to home.[28] The PDC was not associated with increased postdischarge adverse events measured, though a formal cost analysis and analysis of other postdischarge outcomes, such as placement in a skilled nursing or rehabilitation facility after return home, could be assessed in future work.
The findings of our study should be interpreted in the context of the study design. Our study was retrospective, observational, and single‐center. There may have been additional baseline differences between groups predisposing to bias we did not capture in the propensity score. For example, we could not measure rates of attendance at the different clinics and cannot rule out that outcomes associated with PDC were also associated with increased attendance rates. However, none of the clinics had mechanisms in place to improve follow‐up rates; patients referred to PDC were those considered highest risk for readmission and were sicker at baseline, making it very unlikely that they were predisposed to attend clinic more frequently and/or to have better outcomes; and even if PDC improved follow‐up rates, this would be a significant contribution given the limitations of primary‐care access. Our propensity score could not perfectly mimic randomization to a treatment assignment, but rather to treatment received, because of this limitation.
We did not ascertain ED visits or readmissions outside the VA system; it is possible these differentially affected one group more than another, though this seems unlikely. Our patient population was representative of veteran populations elsewhere who are at high risk of adverse postdischarge outcomes, but our findings may not be generalizable to younger, more ethnically diverse populations or to women.
CONCLUSIONS
Provision of postdischarge care by hospitalists may reduce LOS without increasing postdischarge adverse events. Further work is required to evaluate the role of hospitalist‐run PDCs in healthcare systems with more limited postdischarge access to care, to formally evaluate the costs associated with extending hospitalists to the outpatient setting, and to prospectively evaluate the role of a PDC compared with other kinds of hospital follow‐up.
Acknowledgments
The authors thank Melver Anderson, MD, for editorial assistance with the manuscript.
Disclosures: Dr. Burke had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. Dr. Burke was supported by grant funding from the Colorado Research Enhancement Award Program to Improve Care Coordination for Veterans. Initial results of this study were presented at the Society of General Internal Medicine National Meeting in Denver, Colorado, April 24, 2013.
- Moving beyond readmission penalties: creating an ideal process to improve transitional care. J Hosp Med. 2013;8(2):102–109. , , , .
- Continuity of outpatient and inpatient care by primary care physicians for hospitalized older adults. JAMA. 2009;301(16):1671–1680. , , , , , .
- Trends in inpatient continuity of care for a cohort of Medicare patients 1996–2006. J Hosp Med. 2011;6(8):438–444. , , , , .
- Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831–841. , , , , , .
- Posthospital care transitions: patterns, complications, and risk identification. Health Serv Res. 2004;39(5):1449–1465. , , , .
- Patient experiences of transitioning from hospital to home: an ethnographic quality improvement project. J Hosp Med. 2012;7(5):382–387. , , , , .
- Understanding and execution of discharge instructions. Am J Med Qual. 2013;28(5):383–391. , , , et al.
- The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167. , , , , .
- Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428. , , .
- Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100–102. .
- Relationship between early physician follow‐up and 30‐day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303(17):1716–1722. , , , et al.
- Post‐hospitalization transitions: examining the effects of timing of primary care provider follow‐up. J Hosp Med. 2010;5(7):392–397. , , .
- Will generalist physician supply meet demands of an increasing and aging population? Health Aff (Millwood). 2008;27(3):w232–w241. , , .
- Association of American Medical Colleges. The Impact of Health Care Reform on the Future Supply and Demand for Physicians: Updated Projections Through 2025. Available at: http://www.aamc.org/download/158076/data/updated_projections_through_2025.pdf. Published June 2010. Accessed May 1, 2012.
- Effect of discharge summary availability during post‐discharge visits on hospital readmission. J Gen Intern Med. 2002;17(3):186–192. , , , .
- Comprehensive quality of discharge summaries at an academic medical center. J Hosp Med. 2013;8(8):436–443. , , , et al.
- Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381–386. , , , et al.
- Is a post‐discharge clinic in your hospital's future? Available at: http://www.the‐hospitalist.org/details/article/1409011/Is_a_Post‐Discharge_Clinic_in_Your_Hospitals_Future.html. Published December 2011. Accessed May 1, 2013. .
- Continuity of care and patient outcomes after hospital discharge. J Gen Intern Med. 2004;19(6):624–631. , , , .
- Effects of a postdischarge clinic on housestaff satisfaction and utilization of hospital services. J Gen Intern Med. 1996;11(3):179–181. , , , .
- Integrated postdischarge transitional care in a hospitalist system to improve discharge outcome: an experimental study. BMC Med. 2011;9:96. , , , , , .
- Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission. N Engl J Med. 1996;334(22):1441–1447. , , .
- An overview of the objectives of and the approaches to propensity score analyses. Eur Heart J. 2011;32(14):1704–1708. , .
- Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care. 2004;42(4):355–360. , , .
- Growth in the care of older patients by hospitalists in the United States. N Engl J Med. 2009;360(11):1102–1112. , , , .
- Association of hospitalist care with medical utilization after discharge: evidence of cost shift from a cohort study. Ann Intern Med. 2011;155(3):152–159. , .
- Associations between reduced hospital length of stay and 30‐day readmission rate and mortality: 14‐year experience in 129 Veterans Affairs hospitals. Ann Intern Med. 2012;157(12):837–845. , , , et al.
- Variation in length of stay and outcomes among hospitalized patients attributable to hospitals and hospitalists. J Gen Intern Med. 2013;28(3):370–376. , , , .
- Moving beyond readmission penalties: creating an ideal process to improve transitional care. J Hosp Med. 2013;8(2):102–109. , , , .
- Continuity of outpatient and inpatient care by primary care physicians for hospitalized older adults. JAMA. 2009;301(16):1671–1680. , , , , , .
- Trends in inpatient continuity of care for a cohort of Medicare patients 1996–2006. J Hosp Med. 2011;6(8):438–444. , , , , .
- Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831–841. , , , , , .
- Posthospital care transitions: patterns, complications, and risk identification. Health Serv Res. 2004;39(5):1449–1465. , , , .
- Patient experiences of transitioning from hospital to home: an ethnographic quality improvement project. J Hosp Med. 2012;7(5):382–387. , , , , .
- Understanding and execution of discharge instructions. Am J Med Qual. 2013;28(5):383–391. , , , et al.
- The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167. , , , , .
- Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):1418–1428. , , .
- Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100–102. .
- Relationship between early physician follow‐up and 30‐day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303(17):1716–1722. , , , et al.
- Post‐hospitalization transitions: examining the effects of timing of primary care provider follow‐up. J Hosp Med. 2010;5(7):392–397. , , .
- Will generalist physician supply meet demands of an increasing and aging population? Health Aff (Millwood). 2008;27(3):w232–w241. , , .
- Association of American Medical Colleges. The Impact of Health Care Reform on the Future Supply and Demand for Physicians: Updated Projections Through 2025. Available at: http://www.aamc.org/download/158076/data/updated_projections_through_2025.pdf. Published June 2010. Accessed May 1, 2012.
- Effect of discharge summary availability during post‐discharge visits on hospital readmission. J Gen Intern Med. 2002;17(3):186–192. , , , .
- Comprehensive quality of discharge summaries at an academic medical center. J Hosp Med. 2013;8(8):436–443. , , , et al.
- Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381–386. , , , et al.
- Is a post‐discharge clinic in your hospital's future? Available at: http://www.the‐hospitalist.org/details/article/1409011/Is_a_Post‐Discharge_Clinic_in_Your_Hospitals_Future.html. Published December 2011. Accessed May 1, 2013. .
- Continuity of care and patient outcomes after hospital discharge. J Gen Intern Med. 2004;19(6):624–631. , , , .
- Effects of a postdischarge clinic on housestaff satisfaction and utilization of hospital services. J Gen Intern Med. 1996;11(3):179–181. , , , .
- Integrated postdischarge transitional care in a hospitalist system to improve discharge outcome: an experimental study. BMC Med. 2011;9:96. , , , , , .
- Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission. N Engl J Med. 1996;334(22):1441–1447. , , .
- An overview of the objectives of and the approaches to propensity score analyses. Eur Heart J. 2011;32(14):1704–1708. , .
- Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care. 2004;42(4):355–360. , , .
- Growth in the care of older patients by hospitalists in the United States. N Engl J Med. 2009;360(11):1102–1112. , , , .
- Association of hospitalist care with medical utilization after discharge: evidence of cost shift from a cohort study. Ann Intern Med. 2011;155(3):152–159. , .
- Associations between reduced hospital length of stay and 30‐day readmission rate and mortality: 14‐year experience in 129 Veterans Affairs hospitals. Ann Intern Med. 2012;157(12):837–845. , , , et al.
- Variation in length of stay and outcomes among hospitalized patients attributable to hospitals and hospitalists. J Gen Intern Med. 2013;28(3):370–376. , , , .
© 2013 Society of Hospital Medicine
PCP Communication at Hospital Discharge
Medication errors occur frequently when patients transition care between providers, such as the transition from hospital to home.[1] Approximately 50% of about 3 million adults per year over 65 years of age transitioning from hospital to home experience a medication discrepancy.[1] At hospital discharge, patients with complex medical problems are prescribed multiple medications with complex dosing and schedules. To add to this complexity, many patients also have cognitive impairment, variable health literacy, and limited social and financial support. Patients with medication discrepancies have significantly higher rates of rehospitalization compared to those without medication discrepancies.[2] Thus, interventions that focus on patients at greater risk of medication discrepancies may identify those at greater risk of subsequent rehospitalization and potentially reduce the rates of readmissions.[3]
There is limited evidence to date on the effectiveness of interventions to decrease post‐hospitalization medication discrepancies.[3, 4, 5, 6] Previous transitional care interventions have been expensive and difficult to sustain due to the need for multiple additional transitional care personnel (e.g., care managers, nurses, pharmacists, transitional care coaches).[7, 8, 9, 10] Moreover, these additional personnel may further fragment the care of hospitalized patients with additional handoffs.
As both hospitalists and outpatient primary care physicians (PCPs) are being expected to care for larger numbers of increasingly sicker patients, the communication handoff of patient information at discharge remains a challenge.[11] Both patients and PCPs often obtain incomplete or inaccurate information and instructions at discharge. Follow‐up appointments are often missed or delayed.[12] Many hospital‐based interventions do not directly involve the PCP or take advantage of the already established PCPpatient relationship.
We conducted a pilot study examining whether enhancing PCP communication with patients at hospital discharge would impact medication discrepancies and improve the safety of the patient as they transitioned home from the hospital.
METHODS
Recruitment of Subjects
The institutional review board of Northwestern University approved this study. Accessing the hospital electronic health record, research staff queried admission information and contacted the hospital physician to determine potential subject eligibility. Research staff recruited consecutive community‐dwelling adults aged 18 years and older who were hospitalized to the acute medicine services at Northwestern Memorial Hospital and being discharged to their home. The patients recruited had PCPs from practices in the Research and Education for Academic Achievement (REACH) Practice‐Based Research Network. The REACH Practice‐Based Research Network consists of 8 academic, private, and community‐based provider groups affiliated with Northwestern Memorial Hospital and the Northwestern University Feinberg School of Medicine. Subjects were excluded if they (1) were unable to consent to their own procedures while hospitalized, (2) were severely vision impaired that could not be corrected with glasses (because they would be administered tests requiring adequate vision), (3) were reliant on a caregiver or home aide services 8 hours or greater per day, (4) were enrolled in hospice, (5) spoke a language other than English or Spanish, (6) were expected to have a hospital length of stay of <24 hours, or (7) were on <5 outpatient medications prior to hospitalization.
Initial Patient InterviewHospital
Research staff conducted structured in‐person surveys of eligible hospitalized subjects in the private hospital rooms of subjects to maintain confidentiality. Subjects received $20 compensation for their participation. After written informed consent was obtained, research staff obtained demographic information from the subject as well as ascertained their availability in 48 hours, support system, medications, and PCP. Research staff then administered the Short Test of Functional Health Literacy in Adults (sTOFHLA) to determine health literacy.[13] All research staff received the same training on administering the cognitive testing. The survey lasted between 20 and 30 minutes. At hospital discharge, the medication list was obtained from the discharge instructions listed in the patient's electronic medical record.
The PCP‐Enhanced Discharge Communication Intervention
Research staff and the principal investigator met with hospitalists and REACH outpatient physicians in groups to inform them of the study prior to beginning. When the patient was nearing discharge from the hospital, hospitalists were asked to phone the PCP to discuss the patient's discharge plan and facilitate clinical handoffs to the outpatient setting. Research staff paged the hospitalists with the PCP's contact information and reminded them to contact the PCP. Following the hospitalistPCP phone call, the PCP contacted the patient within 24 hours of discharge, either in person while in the hospital or by phone at home. The PCP confirmed medications and clarified any posthospital confusion. The contact flexibility (phone or by person and within 24 hours) was planned as PCPs had other responsibilities that would not allow them to be present at the actual discharge. Physicians were asked to review medications and follow‐up plans, but phone conversations were not recorded. PCPs were given a laminated card that contained points to discuss on discharge (Figure 1). PCPs were compensated with a $5 coffee shop gift card for their time to call patients. This intervention did not involve any additional healthcare personnel.

Postdischarge Phone Interview
During the initial interview, research staff set up a time to contact the subject by phone at 48 hours postdischarge. Research staff contacted the subject at the scheduled time and attempted contact 3 times within 24 hours of scheduled time. During the phone interview, subjects were asked whether they had contact with their PCP to discuss hospital discharge instructions (if so, when) and how satisfied they were with the contact. The subject was also asked whether they were confused with any aspect of the discharge, and if they were, whether the PCP alleviated the confusion. The subjects were asked whether anyone has assisted with their medications, whether they had medication changes, and which medications they were currently taking in the outpatient setting. Both prescription and over‐the‐counter medications were included. Over‐the‐counter medications were included in the study to determine the severity of medication discrepancies if they existed.
The medication list given by the subject was compared with the medical record for the medication list at discharge. Research staff would determine if a discrepancy was present. A discrepancy was considered (1) omission of a medication prescribed at discharge, (2) addition of a medication that was not prescribed at discharge, (3) different dose, (4) different frequency, or (5) duplication of a medication. If discrepancies existed, the subjects were asked for the reason that the discrepancies may have occurred. If discrepancies were identified, subjects were asked to contact their PCP regarding any medication discrepancies and to clarify any issues about medications they had.
Data Analysis
Subjects were categorized into whether they were contacted or not contacted by the PCP within 48 hours of discharge from the hospital. Those who were contacted were defined as those who saw a PCP in person at the time of discharge or within 48 hours of discharge or were called within 48 hours. Those who had an appointment with a PCP after 48 hours of discharge, saw a PCP while hospitalized but did not discuss discharge plan, saw a specialist (such as allergist, urologist, wound care specialist), or who spoke to their PCP but did not discuss their discharge plan were categorized as not contacted.
Reasons for medication discrepancies were categorized into patient‐associated factors (adverse drug effect, intentional nonadherence, unintentional nonadherence) and system‐associated factors (confusion between brand and generic names, discharge instruction incomplete or inaccurate, duplication, incorrect dosage) according to a published medication discrepancy tool.[2] Discrepancies were categorized as intentional nonadherence if patients knew the regimen but decided not to adhere. On the other hand, unintentional nonadherence was used for discrepancies in which patients were unaware of the regimen and thus the discrepancy. Medication discrepancies were classified as mild, moderate, or severe depending on the medication involved. Mild discrepancies were over‐the‐counter medications (eg, acetaminophen, laxatives, multivitamins) and topical creams. Severe discrepancies included medications for heart disease (eg, ‐blockers, calcium channel blockers, angiotensin receptor blockers, diuretics), pulmonary disease (eg, inhalers), diabetes (eg, insulin, glyburide), and antibiotics. Moderate discrepancies were those that did not fit the mild or severe categories (eg, prescription pain medication such as narcotics, anxiety medications, bisphosphonates, muscle relaxants).
Statistical analysis was performed with the SPSS 18.0 (SPSS Inc., Chicago, IL). We analyzed data on study patients to estimate the effect of contact with the PCP within 48 hours of discharge on the frequency of any medication discrepancy. We first examined differences between patients who were contacted or not contacted by patient sociodemographic characteristics. [2] tests were used to analyze the significance of differences in the proportion of medication discrepancies between patients who were contacted and not contacted. Logistic regression analysis was used to test the effect of being contacted on the likelihood of having any prescription medication discrepancy after controlling for patient characteristics (eg, race and ethnicity, age, number of medications, living alone, sex, and TOFHLA score.)
RESULTS
Sample Characteristics
Of the 225 patients who met inclusion criteria, 114 subjects were recruited and interviewed by research staff during the hospital stay and 48 hours after discharge. Due to early discharge and staffing reasons, 27 subjects were not able to be approached during discharge. Of the 84 patients who declined the study, the reasons included: not interested in study (n=58), did not feel well enough to complete or participate (n=16), did not wish study personnel to have access to personal records (n=5), and no reason given (n=5). Of the 114 subjects enrolled in the hospital, 77 subjects completed 48‐hour postdischarge phone interviews with research staff. Two patients had missing data, leaving 75 patients who were included in the analysis.
Study patients' age, race and ethnicity, sex, living situation (alone vs not alone), number of medications, mean sTOFHLA score, and medication discrepancy are summarized in Table 1. Thirty‐six percent of patients (n=27) were contacted by the PCP within 48 hours of discharge. Age, living situation (alone vs not alone), number of medications, and mean sTOFHLA score were similar in both groups of contacted versus noncontacted patients. Of those who were contacted, males made up 48.1% versus 27.1% for those not contacted (P=0.06). Similarly, 44.4% of those who were not contacted were black versus 37.5% among the contacted (P=0.035).
All Subjects, N=77 | Subject Without PCP Contact, n=50 | Subjects With PCP Contact, n=27 | P Value | |
---|---|---|---|---|
| ||||
Mean ageSD, y | 63.012.2 | 63.311.9 | 62.313.1 | 0.74 |
Race and ethnicity, n (%) | 0.35 | |||
White/other | 40 (53.5) | 28 (58.3) | 12 (44.4) | |
Black | 30 (40.0) | 18 (37.5) | 12 (44.4) | |
Hispanic | 5 (6.7) | 2 (4.2) | 3 (11.1) | |
Male, n (%) | 26 (34.7) | 13 (27.1) | 13 (48.1) | 0.06 |
Lives alone, n (%) | 30 (40.0) | 20 (41.7) | 10 (37.0) | 0.69 |
Mean sTOFHLA scoreSD | 29.67.9 | 29.47.7 | 29.97.8 | 0.75 |
Mean number of medications | 9.224.9 | 9.064.7 | 9.633.5 | 0.67 |
Experienced medication discrepancy, n (%) | 39 (52) | 28 (59.3) | 11 (40.7) | 0.14 |
Medication Discrepancies
Of the 75 study patients, 39 patients (50.6%) experienced a total of 84 medication discrepancies. Fifty‐eight medication discrepancies were prescription medications, whereas 25 were over‐the‐counter medications. Of those who had discrepancies, 46.2% (n=18) had 1 discrepancy, 23.1% (n=9) had 2 discrepancies, 12.8% (n=5) had 3 discrepancies, 10.2% (n=4) had 4 discrepancies, and 7.7% (n=3) 5 or more discrepancies. The mean number of discrepancies per patient was 2.15 per patient. Medication discrepancies were categorized by severity based on the safety profile of the medication involved and type discrepancy (Table 2).
Frequency, n (%) | |
---|---|
| |
Type of medication discrepancy | |
Over the counter | 26 (30.9) |
Prescription medication | 58 (69.0) |
Severity of medication discrepancy | |
Milda | 28 (33.3) |
Moderateb | 24 (28.6) |
Severec | 32 (38.1) |
Reasons for Medication Discrepancies
The subject‐provided reasons for medication discrepancies are listed in Table 3 and divided into patient‐ and system‐associated factors. The overall most frequent reason for a discrepancy was the patient's intentional nonadherence. Examples of intentional nonadherence include not sure of purpose of medication, did not recognize drug, did not fill prescription, did not need prescription, and wanted to wait longer, so not taking diuretic daily. The second most frequent reason was inaccurate discharge instructions (e.g., discharge instructions with medication changes denoting no change but incorrect outpatient thyroid medication dosage listed (Table 3).
Factor | Frequency, n (%) |
---|---|
Patient‐associated factors | |
Adverse drug effects | 8 (9.5) |
Intentional nonadherence | 50 (59.4) |
Unintentional nonadherence | 1 (1.2) |
Subtotal | 59 |
System‐associated factors | |
Confusion between brand and generic names | 3 (3.5) |
Discharge Instructions incomplete or inaccurate | 12 (14.2) |
Duplication | 3 (3.5) |
Incorrect dosage | 3 (3.5) |
Incorrect frequency | 1 (1.2) |
Conflicting information from different sources | 3 (3.5) |
Subtotal | 25 |
Total | 84 |
Logistic Regression Results for the Likelihood of Any Medication Discrepancy
Logistical regression results are shown in Table 4. Patients who were contacted by their PCP at discharge were 70% less likely to have a discrepancy when compared with those who were not contacted (P=0.03). This result was controlled for other possible factors including patient sex. Of interest, men were 3.94 times more likely to have a discrepancy when compared with women (P=0.02). There was also a nonsignificant but potentially important association between higher health literacy, measured continuously (0X) and being more likely to have a discrepancy (P=0.07). Including variables for age, ethnicity, and living alone were nonsignificant and did not change the regression results for contacted patients.
Odds Ratio | 95% Confidence Interval | |
---|---|---|
| ||
Subject contacted by PCP at Discharge | 0.33 | 0.110.97 |
Male | 3.98 | 1.2712.49 |
Number of medications | 1.09 | 0.961.23 |
TOHFLA score | 1.09 | 1.011.18 |
DISCUSSION
Our results provide evidence that contact with PCPs within 24 hours of hospital discharge can be effective in decreasing medication discrepancies. The PCP‐Enhanced Discharge Communication Intervention was designed to investigate the value of improving existing lines of communication at discharge without involving any additional healthcare personnel. As a lean discharge intervention, the PCP, the hospitalist, and the patient were the main components to this intervention.
This study was limited in that the sample size was small and that we enrolled consecutive patients. Due to the small sample size, we did not examine hospital readmissions. Further studies are needed to examine whether primary care involvement at discharge would affect hospital readmissions. Another limitation of this study was that the control group was not randomized or preselected. Our study compared those subjects who received a phone call from their PCP to those subjects who did not. Although we instructed PCPs with a standardized script, we did not record or ensure that the phone call‐up occurred as such. There is potential variability in how the PCPs conducted their follow‐up with patients, and we are unable to measure what was effective and ineffective in reducing medication discrepancies. Another limitation was that the determination of the severity of the medication discrepancy was done by medication involved as opposed to by physician review and adjudication. The study would have been strengthened by interviewing the outpatient physicians on the amount of harm each discrepancy would or did cause the patient.
The most frequent reason for discrepancy was intentional nonadherence. Prior research has shown that intentional nonadherence of medications at hospital discharge is linked to health literacy.[14] One may postulate that patients with adequate health literacy feel enabled to go against medical advice and chose to not take medications as prescribed.
In our study, patients had the most medication discrepancies in the severe medications category, which involved cardiac, pulmonary, and diabetic medications, compared with the mild and moderate category. This finding may reflect the frequency that these medications are prescribed but are consistent with findings of Coleman et al.[2] The finding highlights the need to ensure adequate education and understanding of medication regimens for these complex patients. Patients with cardiac, pulmonary, and diabetic disease may benefit from personalized discharge instructions and a more structured and organized medication reconciliation process.
Our study found that males were more likely to have a medication discrepancy than females, which has not been found in previous studies on medication discrepancies. One study on Medicare beneficiaries with congestive heart failure found that men were much more likely to be readmitted than woman within 6 months of discharge.[15] The reason for the increased risk of medication discrepancy in males is unknown. Gender differences in health have frequently been reported, with men having higher rates of morbidity and mortality than women.[16, 17] The differences are thought to be due to the reluctance of men to seek medical help and consult medical practitioners when needed. It has been known that women use health services more than men, and are more likely than men to report a chronic illness.[18] When men do present with symptoms, it is often later in the stage of a disease than women and when treatment is less likely to be successful.[19] It may be that men in this study population had more medication discrepancies as they were reluctant to seek help or ask for clarification regarding medications at discharge.
Of those enrolled, 36% of patients were contacted by their PCP within 48 hours of discharge. It is unclear if the PCP attempted but was unable to reach the patient or did not attempt to call the patient. Although PCPs were compensated with a $5 coffee shop gift card, a larger compensation may insure completion of the patient contact. Further research is needed to determine the reasons why PCPs were not able to complete the phone call.
From a policy standpoint, hospitals that focus solely on hospital‐based transition interventions are potentially missing half the problem. The hospital acts as a sender or pitcher, and the PCP acts as a receiver or catcher. The receiver needs to be included in the discharge process for a successful patient transition to home. With recent billing changes for transition coding, the Center for Medicare and Medicaid Services recognizes this relationship.[20] Outpatient PCPs are able to bill for bundled follow‐up phone calls and appointments. Instead of paying additional staff to make 48‐hour postdischarge phone calls, hospitals should consider partnering with PCPs to ensure a more organized discharge.
Our results showed that PCP communication with patients within 24 hours of discharge was associated with decreased medication discrepancies. The PCP is vital to ensuring a safe transition home from the hospital. Because many patients have an established relationship with their PCP, a bond of trust exists that is often missing with hospital‐employed transitional staff. Patients pay attention when a known physician contacts them directly. In our study, patients may have felt comfortable addressing their concerns and questions with their trusted PCP. Subsequently, patients may have been more attuned to the answers their PCP gave and avoided medication errors. Our results further demonstrate the importance of PCP involvement in the hospital discharge process to improve the care of our patients.
- The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167. , , , , .
- Posthospital medication discrepancies—prevalence and contributing factors. Arch Intern Med. 2005;165(16):1842–1847. , , , .
- Comprehensive discharge planning for the hospitalized elderly. A randomized clinical trial. Ann Intern Med. 1994;120(12):999–1006. , , , et al.
- Further application of the care transitions intervention: results of a randomized controlled trial conducted in a fee‐for‐service setting. Home Health Care Serv Q. 2009;28(2‐3):84–99. , , , , .
- Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission. N Engl J Med. 1996;334(22):1441–1446. , , .
- Reduction of 30‐day postdischarge hospital readmission or emergency department (ED) visit rates in high‐risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4(4):211–218. , , , et al.
- A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178–187. , , , et al.
- A case manager intervention to reduce readmissions. Arch Intern Med. 1994;154(15):1721–1729. , , , , .
- Preparing patients and caregivers to participate in care delivered across settings: the Care Transitions Intervention. J Am Geriatr Soc. 2004;52(11):1817–1825. , , , , , .
- Effect of a nurse team coordinator on outcomes for hospitalized medicine patients. Am J Med. 2005;118(10):1148–1153. , , , et al.
- Problems after discharge and understanding of communication with their primary care physicians among hospitalized seniors: a mixed methods study. J Hosp Med. 2010;5(7):385–391. , , , et al.
- Hospital readmissions in the Medicare population. N Engl J Med. 1984;311(21):1349–1353. , .
- Development of a brief test to measure functional health literacy. Patient Educ Couns. 1999;38(1):33–42. , , , , .
- Relationship of Health Literacy to Intentional and Unintentional Non‐Adherence of Hospital Discharge Medications. J Gen Intern Med. 2012;27(2):173–178. , , , , , .
- Readmission after hospitalization for congestive heart failure among Medicare beneficiaries. Arch Intern Med. 1997;157(1):99–104. , , , et al.
- College men's health: an overview and a call to action. J Am Coll Health. 1998;46(6):279–290. .
- Gender and the Social Construction of Illness. Thousand Oaks, CA: Sage; 1997.
- Inequalities in Health: The Black Report and the Health Divide. London, UK: Penguin; 1988.
- Decision making process in people with symptoms of acute myocardial infarction: qualitative study. BMJ. 2002;332:1006–1017. , , , .
- Centers for Medicare and Medicaid Services. Transitional Care Management Services. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/PhysicianFeeSched/Downloads/FAQ‐TCMS.pdf. Accessed June 28, 2013.
Medication errors occur frequently when patients transition care between providers, such as the transition from hospital to home.[1] Approximately 50% of about 3 million adults per year over 65 years of age transitioning from hospital to home experience a medication discrepancy.[1] At hospital discharge, patients with complex medical problems are prescribed multiple medications with complex dosing and schedules. To add to this complexity, many patients also have cognitive impairment, variable health literacy, and limited social and financial support. Patients with medication discrepancies have significantly higher rates of rehospitalization compared to those without medication discrepancies.[2] Thus, interventions that focus on patients at greater risk of medication discrepancies may identify those at greater risk of subsequent rehospitalization and potentially reduce the rates of readmissions.[3]
There is limited evidence to date on the effectiveness of interventions to decrease post‐hospitalization medication discrepancies.[3, 4, 5, 6] Previous transitional care interventions have been expensive and difficult to sustain due to the need for multiple additional transitional care personnel (e.g., care managers, nurses, pharmacists, transitional care coaches).[7, 8, 9, 10] Moreover, these additional personnel may further fragment the care of hospitalized patients with additional handoffs.
As both hospitalists and outpatient primary care physicians (PCPs) are being expected to care for larger numbers of increasingly sicker patients, the communication handoff of patient information at discharge remains a challenge.[11] Both patients and PCPs often obtain incomplete or inaccurate information and instructions at discharge. Follow‐up appointments are often missed or delayed.[12] Many hospital‐based interventions do not directly involve the PCP or take advantage of the already established PCPpatient relationship.
We conducted a pilot study examining whether enhancing PCP communication with patients at hospital discharge would impact medication discrepancies and improve the safety of the patient as they transitioned home from the hospital.
METHODS
Recruitment of Subjects
The institutional review board of Northwestern University approved this study. Accessing the hospital electronic health record, research staff queried admission information and contacted the hospital physician to determine potential subject eligibility. Research staff recruited consecutive community‐dwelling adults aged 18 years and older who were hospitalized to the acute medicine services at Northwestern Memorial Hospital and being discharged to their home. The patients recruited had PCPs from practices in the Research and Education for Academic Achievement (REACH) Practice‐Based Research Network. The REACH Practice‐Based Research Network consists of 8 academic, private, and community‐based provider groups affiliated with Northwestern Memorial Hospital and the Northwestern University Feinberg School of Medicine. Subjects were excluded if they (1) were unable to consent to their own procedures while hospitalized, (2) were severely vision impaired that could not be corrected with glasses (because they would be administered tests requiring adequate vision), (3) were reliant on a caregiver or home aide services 8 hours or greater per day, (4) were enrolled in hospice, (5) spoke a language other than English or Spanish, (6) were expected to have a hospital length of stay of <24 hours, or (7) were on <5 outpatient medications prior to hospitalization.
Initial Patient InterviewHospital
Research staff conducted structured in‐person surveys of eligible hospitalized subjects in the private hospital rooms of subjects to maintain confidentiality. Subjects received $20 compensation for their participation. After written informed consent was obtained, research staff obtained demographic information from the subject as well as ascertained their availability in 48 hours, support system, medications, and PCP. Research staff then administered the Short Test of Functional Health Literacy in Adults (sTOFHLA) to determine health literacy.[13] All research staff received the same training on administering the cognitive testing. The survey lasted between 20 and 30 minutes. At hospital discharge, the medication list was obtained from the discharge instructions listed in the patient's electronic medical record.
The PCP‐Enhanced Discharge Communication Intervention
Research staff and the principal investigator met with hospitalists and REACH outpatient physicians in groups to inform them of the study prior to beginning. When the patient was nearing discharge from the hospital, hospitalists were asked to phone the PCP to discuss the patient's discharge plan and facilitate clinical handoffs to the outpatient setting. Research staff paged the hospitalists with the PCP's contact information and reminded them to contact the PCP. Following the hospitalistPCP phone call, the PCP contacted the patient within 24 hours of discharge, either in person while in the hospital or by phone at home. The PCP confirmed medications and clarified any posthospital confusion. The contact flexibility (phone or by person and within 24 hours) was planned as PCPs had other responsibilities that would not allow them to be present at the actual discharge. Physicians were asked to review medications and follow‐up plans, but phone conversations were not recorded. PCPs were given a laminated card that contained points to discuss on discharge (Figure 1). PCPs were compensated with a $5 coffee shop gift card for their time to call patients. This intervention did not involve any additional healthcare personnel.

Postdischarge Phone Interview
During the initial interview, research staff set up a time to contact the subject by phone at 48 hours postdischarge. Research staff contacted the subject at the scheduled time and attempted contact 3 times within 24 hours of scheduled time. During the phone interview, subjects were asked whether they had contact with their PCP to discuss hospital discharge instructions (if so, when) and how satisfied they were with the contact. The subject was also asked whether they were confused with any aspect of the discharge, and if they were, whether the PCP alleviated the confusion. The subjects were asked whether anyone has assisted with their medications, whether they had medication changes, and which medications they were currently taking in the outpatient setting. Both prescription and over‐the‐counter medications were included. Over‐the‐counter medications were included in the study to determine the severity of medication discrepancies if they existed.
The medication list given by the subject was compared with the medical record for the medication list at discharge. Research staff would determine if a discrepancy was present. A discrepancy was considered (1) omission of a medication prescribed at discharge, (2) addition of a medication that was not prescribed at discharge, (3) different dose, (4) different frequency, or (5) duplication of a medication. If discrepancies existed, the subjects were asked for the reason that the discrepancies may have occurred. If discrepancies were identified, subjects were asked to contact their PCP regarding any medication discrepancies and to clarify any issues about medications they had.
Data Analysis
Subjects were categorized into whether they were contacted or not contacted by the PCP within 48 hours of discharge from the hospital. Those who were contacted were defined as those who saw a PCP in person at the time of discharge or within 48 hours of discharge or were called within 48 hours. Those who had an appointment with a PCP after 48 hours of discharge, saw a PCP while hospitalized but did not discuss discharge plan, saw a specialist (such as allergist, urologist, wound care specialist), or who spoke to their PCP but did not discuss their discharge plan were categorized as not contacted.
Reasons for medication discrepancies were categorized into patient‐associated factors (adverse drug effect, intentional nonadherence, unintentional nonadherence) and system‐associated factors (confusion between brand and generic names, discharge instruction incomplete or inaccurate, duplication, incorrect dosage) according to a published medication discrepancy tool.[2] Discrepancies were categorized as intentional nonadherence if patients knew the regimen but decided not to adhere. On the other hand, unintentional nonadherence was used for discrepancies in which patients were unaware of the regimen and thus the discrepancy. Medication discrepancies were classified as mild, moderate, or severe depending on the medication involved. Mild discrepancies were over‐the‐counter medications (eg, acetaminophen, laxatives, multivitamins) and topical creams. Severe discrepancies included medications for heart disease (eg, ‐blockers, calcium channel blockers, angiotensin receptor blockers, diuretics), pulmonary disease (eg, inhalers), diabetes (eg, insulin, glyburide), and antibiotics. Moderate discrepancies were those that did not fit the mild or severe categories (eg, prescription pain medication such as narcotics, anxiety medications, bisphosphonates, muscle relaxants).
Statistical analysis was performed with the SPSS 18.0 (SPSS Inc., Chicago, IL). We analyzed data on study patients to estimate the effect of contact with the PCP within 48 hours of discharge on the frequency of any medication discrepancy. We first examined differences between patients who were contacted or not contacted by patient sociodemographic characteristics. [2] tests were used to analyze the significance of differences in the proportion of medication discrepancies between patients who were contacted and not contacted. Logistic regression analysis was used to test the effect of being contacted on the likelihood of having any prescription medication discrepancy after controlling for patient characteristics (eg, race and ethnicity, age, number of medications, living alone, sex, and TOFHLA score.)
RESULTS
Sample Characteristics
Of the 225 patients who met inclusion criteria, 114 subjects were recruited and interviewed by research staff during the hospital stay and 48 hours after discharge. Due to early discharge and staffing reasons, 27 subjects were not able to be approached during discharge. Of the 84 patients who declined the study, the reasons included: not interested in study (n=58), did not feel well enough to complete or participate (n=16), did not wish study personnel to have access to personal records (n=5), and no reason given (n=5). Of the 114 subjects enrolled in the hospital, 77 subjects completed 48‐hour postdischarge phone interviews with research staff. Two patients had missing data, leaving 75 patients who were included in the analysis.
Study patients' age, race and ethnicity, sex, living situation (alone vs not alone), number of medications, mean sTOFHLA score, and medication discrepancy are summarized in Table 1. Thirty‐six percent of patients (n=27) were contacted by the PCP within 48 hours of discharge. Age, living situation (alone vs not alone), number of medications, and mean sTOFHLA score were similar in both groups of contacted versus noncontacted patients. Of those who were contacted, males made up 48.1% versus 27.1% for those not contacted (P=0.06). Similarly, 44.4% of those who were not contacted were black versus 37.5% among the contacted (P=0.035).
All Subjects, N=77 | Subject Without PCP Contact, n=50 | Subjects With PCP Contact, n=27 | P Value | |
---|---|---|---|---|
| ||||
Mean ageSD, y | 63.012.2 | 63.311.9 | 62.313.1 | 0.74 |
Race and ethnicity, n (%) | 0.35 | |||
White/other | 40 (53.5) | 28 (58.3) | 12 (44.4) | |
Black | 30 (40.0) | 18 (37.5) | 12 (44.4) | |
Hispanic | 5 (6.7) | 2 (4.2) | 3 (11.1) | |
Male, n (%) | 26 (34.7) | 13 (27.1) | 13 (48.1) | 0.06 |
Lives alone, n (%) | 30 (40.0) | 20 (41.7) | 10 (37.0) | 0.69 |
Mean sTOFHLA scoreSD | 29.67.9 | 29.47.7 | 29.97.8 | 0.75 |
Mean number of medications | 9.224.9 | 9.064.7 | 9.633.5 | 0.67 |
Experienced medication discrepancy, n (%) | 39 (52) | 28 (59.3) | 11 (40.7) | 0.14 |
Medication Discrepancies
Of the 75 study patients, 39 patients (50.6%) experienced a total of 84 medication discrepancies. Fifty‐eight medication discrepancies were prescription medications, whereas 25 were over‐the‐counter medications. Of those who had discrepancies, 46.2% (n=18) had 1 discrepancy, 23.1% (n=9) had 2 discrepancies, 12.8% (n=5) had 3 discrepancies, 10.2% (n=4) had 4 discrepancies, and 7.7% (n=3) 5 or more discrepancies. The mean number of discrepancies per patient was 2.15 per patient. Medication discrepancies were categorized by severity based on the safety profile of the medication involved and type discrepancy (Table 2).
Frequency, n (%) | |
---|---|
| |
Type of medication discrepancy | |
Over the counter | 26 (30.9) |
Prescription medication | 58 (69.0) |
Severity of medication discrepancy | |
Milda | 28 (33.3) |
Moderateb | 24 (28.6) |
Severec | 32 (38.1) |
Reasons for Medication Discrepancies
The subject‐provided reasons for medication discrepancies are listed in Table 3 and divided into patient‐ and system‐associated factors. The overall most frequent reason for a discrepancy was the patient's intentional nonadherence. Examples of intentional nonadherence include not sure of purpose of medication, did not recognize drug, did not fill prescription, did not need prescription, and wanted to wait longer, so not taking diuretic daily. The second most frequent reason was inaccurate discharge instructions (e.g., discharge instructions with medication changes denoting no change but incorrect outpatient thyroid medication dosage listed (Table 3).
Factor | Frequency, n (%) |
---|---|
Patient‐associated factors | |
Adverse drug effects | 8 (9.5) |
Intentional nonadherence | 50 (59.4) |
Unintentional nonadherence | 1 (1.2) |
Subtotal | 59 |
System‐associated factors | |
Confusion between brand and generic names | 3 (3.5) |
Discharge Instructions incomplete or inaccurate | 12 (14.2) |
Duplication | 3 (3.5) |
Incorrect dosage | 3 (3.5) |
Incorrect frequency | 1 (1.2) |
Conflicting information from different sources | 3 (3.5) |
Subtotal | 25 |
Total | 84 |
Logistic Regression Results for the Likelihood of Any Medication Discrepancy
Logistical regression results are shown in Table 4. Patients who were contacted by their PCP at discharge were 70% less likely to have a discrepancy when compared with those who were not contacted (P=0.03). This result was controlled for other possible factors including patient sex. Of interest, men were 3.94 times more likely to have a discrepancy when compared with women (P=0.02). There was also a nonsignificant but potentially important association between higher health literacy, measured continuously (0X) and being more likely to have a discrepancy (P=0.07). Including variables for age, ethnicity, and living alone were nonsignificant and did not change the regression results for contacted patients.
Odds Ratio | 95% Confidence Interval | |
---|---|---|
| ||
Subject contacted by PCP at Discharge | 0.33 | 0.110.97 |
Male | 3.98 | 1.2712.49 |
Number of medications | 1.09 | 0.961.23 |
TOHFLA score | 1.09 | 1.011.18 |
DISCUSSION
Our results provide evidence that contact with PCPs within 24 hours of hospital discharge can be effective in decreasing medication discrepancies. The PCP‐Enhanced Discharge Communication Intervention was designed to investigate the value of improving existing lines of communication at discharge without involving any additional healthcare personnel. As a lean discharge intervention, the PCP, the hospitalist, and the patient were the main components to this intervention.
This study was limited in that the sample size was small and that we enrolled consecutive patients. Due to the small sample size, we did not examine hospital readmissions. Further studies are needed to examine whether primary care involvement at discharge would affect hospital readmissions. Another limitation of this study was that the control group was not randomized or preselected. Our study compared those subjects who received a phone call from their PCP to those subjects who did not. Although we instructed PCPs with a standardized script, we did not record or ensure that the phone call‐up occurred as such. There is potential variability in how the PCPs conducted their follow‐up with patients, and we are unable to measure what was effective and ineffective in reducing medication discrepancies. Another limitation was that the determination of the severity of the medication discrepancy was done by medication involved as opposed to by physician review and adjudication. The study would have been strengthened by interviewing the outpatient physicians on the amount of harm each discrepancy would or did cause the patient.
The most frequent reason for discrepancy was intentional nonadherence. Prior research has shown that intentional nonadherence of medications at hospital discharge is linked to health literacy.[14] One may postulate that patients with adequate health literacy feel enabled to go against medical advice and chose to not take medications as prescribed.
In our study, patients had the most medication discrepancies in the severe medications category, which involved cardiac, pulmonary, and diabetic medications, compared with the mild and moderate category. This finding may reflect the frequency that these medications are prescribed but are consistent with findings of Coleman et al.[2] The finding highlights the need to ensure adequate education and understanding of medication regimens for these complex patients. Patients with cardiac, pulmonary, and diabetic disease may benefit from personalized discharge instructions and a more structured and organized medication reconciliation process.
Our study found that males were more likely to have a medication discrepancy than females, which has not been found in previous studies on medication discrepancies. One study on Medicare beneficiaries with congestive heart failure found that men were much more likely to be readmitted than woman within 6 months of discharge.[15] The reason for the increased risk of medication discrepancy in males is unknown. Gender differences in health have frequently been reported, with men having higher rates of morbidity and mortality than women.[16, 17] The differences are thought to be due to the reluctance of men to seek medical help and consult medical practitioners when needed. It has been known that women use health services more than men, and are more likely than men to report a chronic illness.[18] When men do present with symptoms, it is often later in the stage of a disease than women and when treatment is less likely to be successful.[19] It may be that men in this study population had more medication discrepancies as they were reluctant to seek help or ask for clarification regarding medications at discharge.
Of those enrolled, 36% of patients were contacted by their PCP within 48 hours of discharge. It is unclear if the PCP attempted but was unable to reach the patient or did not attempt to call the patient. Although PCPs were compensated with a $5 coffee shop gift card, a larger compensation may insure completion of the patient contact. Further research is needed to determine the reasons why PCPs were not able to complete the phone call.
From a policy standpoint, hospitals that focus solely on hospital‐based transition interventions are potentially missing half the problem. The hospital acts as a sender or pitcher, and the PCP acts as a receiver or catcher. The receiver needs to be included in the discharge process for a successful patient transition to home. With recent billing changes for transition coding, the Center for Medicare and Medicaid Services recognizes this relationship.[20] Outpatient PCPs are able to bill for bundled follow‐up phone calls and appointments. Instead of paying additional staff to make 48‐hour postdischarge phone calls, hospitals should consider partnering with PCPs to ensure a more organized discharge.
Our results showed that PCP communication with patients within 24 hours of discharge was associated with decreased medication discrepancies. The PCP is vital to ensuring a safe transition home from the hospital. Because many patients have an established relationship with their PCP, a bond of trust exists that is often missing with hospital‐employed transitional staff. Patients pay attention when a known physician contacts them directly. In our study, patients may have felt comfortable addressing their concerns and questions with their trusted PCP. Subsequently, patients may have been more attuned to the answers their PCP gave and avoided medication errors. Our results further demonstrate the importance of PCP involvement in the hospital discharge process to improve the care of our patients.
Medication errors occur frequently when patients transition care between providers, such as the transition from hospital to home.[1] Approximately 50% of about 3 million adults per year over 65 years of age transitioning from hospital to home experience a medication discrepancy.[1] At hospital discharge, patients with complex medical problems are prescribed multiple medications with complex dosing and schedules. To add to this complexity, many patients also have cognitive impairment, variable health literacy, and limited social and financial support. Patients with medication discrepancies have significantly higher rates of rehospitalization compared to those without medication discrepancies.[2] Thus, interventions that focus on patients at greater risk of medication discrepancies may identify those at greater risk of subsequent rehospitalization and potentially reduce the rates of readmissions.[3]
There is limited evidence to date on the effectiveness of interventions to decrease post‐hospitalization medication discrepancies.[3, 4, 5, 6] Previous transitional care interventions have been expensive and difficult to sustain due to the need for multiple additional transitional care personnel (e.g., care managers, nurses, pharmacists, transitional care coaches).[7, 8, 9, 10] Moreover, these additional personnel may further fragment the care of hospitalized patients with additional handoffs.
As both hospitalists and outpatient primary care physicians (PCPs) are being expected to care for larger numbers of increasingly sicker patients, the communication handoff of patient information at discharge remains a challenge.[11] Both patients and PCPs often obtain incomplete or inaccurate information and instructions at discharge. Follow‐up appointments are often missed or delayed.[12] Many hospital‐based interventions do not directly involve the PCP or take advantage of the already established PCPpatient relationship.
We conducted a pilot study examining whether enhancing PCP communication with patients at hospital discharge would impact medication discrepancies and improve the safety of the patient as they transitioned home from the hospital.
METHODS
Recruitment of Subjects
The institutional review board of Northwestern University approved this study. Accessing the hospital electronic health record, research staff queried admission information and contacted the hospital physician to determine potential subject eligibility. Research staff recruited consecutive community‐dwelling adults aged 18 years and older who were hospitalized to the acute medicine services at Northwestern Memorial Hospital and being discharged to their home. The patients recruited had PCPs from practices in the Research and Education for Academic Achievement (REACH) Practice‐Based Research Network. The REACH Practice‐Based Research Network consists of 8 academic, private, and community‐based provider groups affiliated with Northwestern Memorial Hospital and the Northwestern University Feinberg School of Medicine. Subjects were excluded if they (1) were unable to consent to their own procedures while hospitalized, (2) were severely vision impaired that could not be corrected with glasses (because they would be administered tests requiring adequate vision), (3) were reliant on a caregiver or home aide services 8 hours or greater per day, (4) were enrolled in hospice, (5) spoke a language other than English or Spanish, (6) were expected to have a hospital length of stay of <24 hours, or (7) were on <5 outpatient medications prior to hospitalization.
Initial Patient InterviewHospital
Research staff conducted structured in‐person surveys of eligible hospitalized subjects in the private hospital rooms of subjects to maintain confidentiality. Subjects received $20 compensation for their participation. After written informed consent was obtained, research staff obtained demographic information from the subject as well as ascertained their availability in 48 hours, support system, medications, and PCP. Research staff then administered the Short Test of Functional Health Literacy in Adults (sTOFHLA) to determine health literacy.[13] All research staff received the same training on administering the cognitive testing. The survey lasted between 20 and 30 minutes. At hospital discharge, the medication list was obtained from the discharge instructions listed in the patient's electronic medical record.
The PCP‐Enhanced Discharge Communication Intervention
Research staff and the principal investigator met with hospitalists and REACH outpatient physicians in groups to inform them of the study prior to beginning. When the patient was nearing discharge from the hospital, hospitalists were asked to phone the PCP to discuss the patient's discharge plan and facilitate clinical handoffs to the outpatient setting. Research staff paged the hospitalists with the PCP's contact information and reminded them to contact the PCP. Following the hospitalistPCP phone call, the PCP contacted the patient within 24 hours of discharge, either in person while in the hospital or by phone at home. The PCP confirmed medications and clarified any posthospital confusion. The contact flexibility (phone or by person and within 24 hours) was planned as PCPs had other responsibilities that would not allow them to be present at the actual discharge. Physicians were asked to review medications and follow‐up plans, but phone conversations were not recorded. PCPs were given a laminated card that contained points to discuss on discharge (Figure 1). PCPs were compensated with a $5 coffee shop gift card for their time to call patients. This intervention did not involve any additional healthcare personnel.

Postdischarge Phone Interview
During the initial interview, research staff set up a time to contact the subject by phone at 48 hours postdischarge. Research staff contacted the subject at the scheduled time and attempted contact 3 times within 24 hours of scheduled time. During the phone interview, subjects were asked whether they had contact with their PCP to discuss hospital discharge instructions (if so, when) and how satisfied they were with the contact. The subject was also asked whether they were confused with any aspect of the discharge, and if they were, whether the PCP alleviated the confusion. The subjects were asked whether anyone has assisted with their medications, whether they had medication changes, and which medications they were currently taking in the outpatient setting. Both prescription and over‐the‐counter medications were included. Over‐the‐counter medications were included in the study to determine the severity of medication discrepancies if they existed.
The medication list given by the subject was compared with the medical record for the medication list at discharge. Research staff would determine if a discrepancy was present. A discrepancy was considered (1) omission of a medication prescribed at discharge, (2) addition of a medication that was not prescribed at discharge, (3) different dose, (4) different frequency, or (5) duplication of a medication. If discrepancies existed, the subjects were asked for the reason that the discrepancies may have occurred. If discrepancies were identified, subjects were asked to contact their PCP regarding any medication discrepancies and to clarify any issues about medications they had.
Data Analysis
Subjects were categorized into whether they were contacted or not contacted by the PCP within 48 hours of discharge from the hospital. Those who were contacted were defined as those who saw a PCP in person at the time of discharge or within 48 hours of discharge or were called within 48 hours. Those who had an appointment with a PCP after 48 hours of discharge, saw a PCP while hospitalized but did not discuss discharge plan, saw a specialist (such as allergist, urologist, wound care specialist), or who spoke to their PCP but did not discuss their discharge plan were categorized as not contacted.
Reasons for medication discrepancies were categorized into patient‐associated factors (adverse drug effect, intentional nonadherence, unintentional nonadherence) and system‐associated factors (confusion between brand and generic names, discharge instruction incomplete or inaccurate, duplication, incorrect dosage) according to a published medication discrepancy tool.[2] Discrepancies were categorized as intentional nonadherence if patients knew the regimen but decided not to adhere. On the other hand, unintentional nonadherence was used for discrepancies in which patients were unaware of the regimen and thus the discrepancy. Medication discrepancies were classified as mild, moderate, or severe depending on the medication involved. Mild discrepancies were over‐the‐counter medications (eg, acetaminophen, laxatives, multivitamins) and topical creams. Severe discrepancies included medications for heart disease (eg, ‐blockers, calcium channel blockers, angiotensin receptor blockers, diuretics), pulmonary disease (eg, inhalers), diabetes (eg, insulin, glyburide), and antibiotics. Moderate discrepancies were those that did not fit the mild or severe categories (eg, prescription pain medication such as narcotics, anxiety medications, bisphosphonates, muscle relaxants).
Statistical analysis was performed with the SPSS 18.0 (SPSS Inc., Chicago, IL). We analyzed data on study patients to estimate the effect of contact with the PCP within 48 hours of discharge on the frequency of any medication discrepancy. We first examined differences between patients who were contacted or not contacted by patient sociodemographic characteristics. [2] tests were used to analyze the significance of differences in the proportion of medication discrepancies between patients who were contacted and not contacted. Logistic regression analysis was used to test the effect of being contacted on the likelihood of having any prescription medication discrepancy after controlling for patient characteristics (eg, race and ethnicity, age, number of medications, living alone, sex, and TOFHLA score.)
RESULTS
Sample Characteristics
Of the 225 patients who met inclusion criteria, 114 subjects were recruited and interviewed by research staff during the hospital stay and 48 hours after discharge. Due to early discharge and staffing reasons, 27 subjects were not able to be approached during discharge. Of the 84 patients who declined the study, the reasons included: not interested in study (n=58), did not feel well enough to complete or participate (n=16), did not wish study personnel to have access to personal records (n=5), and no reason given (n=5). Of the 114 subjects enrolled in the hospital, 77 subjects completed 48‐hour postdischarge phone interviews with research staff. Two patients had missing data, leaving 75 patients who were included in the analysis.
Study patients' age, race and ethnicity, sex, living situation (alone vs not alone), number of medications, mean sTOFHLA score, and medication discrepancy are summarized in Table 1. Thirty‐six percent of patients (n=27) were contacted by the PCP within 48 hours of discharge. Age, living situation (alone vs not alone), number of medications, and mean sTOFHLA score were similar in both groups of contacted versus noncontacted patients. Of those who were contacted, males made up 48.1% versus 27.1% for those not contacted (P=0.06). Similarly, 44.4% of those who were not contacted were black versus 37.5% among the contacted (P=0.035).
All Subjects, N=77 | Subject Without PCP Contact, n=50 | Subjects With PCP Contact, n=27 | P Value | |
---|---|---|---|---|
| ||||
Mean ageSD, y | 63.012.2 | 63.311.9 | 62.313.1 | 0.74 |
Race and ethnicity, n (%) | 0.35 | |||
White/other | 40 (53.5) | 28 (58.3) | 12 (44.4) | |
Black | 30 (40.0) | 18 (37.5) | 12 (44.4) | |
Hispanic | 5 (6.7) | 2 (4.2) | 3 (11.1) | |
Male, n (%) | 26 (34.7) | 13 (27.1) | 13 (48.1) | 0.06 |
Lives alone, n (%) | 30 (40.0) | 20 (41.7) | 10 (37.0) | 0.69 |
Mean sTOFHLA scoreSD | 29.67.9 | 29.47.7 | 29.97.8 | 0.75 |
Mean number of medications | 9.224.9 | 9.064.7 | 9.633.5 | 0.67 |
Experienced medication discrepancy, n (%) | 39 (52) | 28 (59.3) | 11 (40.7) | 0.14 |
Medication Discrepancies
Of the 75 study patients, 39 patients (50.6%) experienced a total of 84 medication discrepancies. Fifty‐eight medication discrepancies were prescription medications, whereas 25 were over‐the‐counter medications. Of those who had discrepancies, 46.2% (n=18) had 1 discrepancy, 23.1% (n=9) had 2 discrepancies, 12.8% (n=5) had 3 discrepancies, 10.2% (n=4) had 4 discrepancies, and 7.7% (n=3) 5 or more discrepancies. The mean number of discrepancies per patient was 2.15 per patient. Medication discrepancies were categorized by severity based on the safety profile of the medication involved and type discrepancy (Table 2).
Frequency, n (%) | |
---|---|
| |
Type of medication discrepancy | |
Over the counter | 26 (30.9) |
Prescription medication | 58 (69.0) |
Severity of medication discrepancy | |
Milda | 28 (33.3) |
Moderateb | 24 (28.6) |
Severec | 32 (38.1) |
Reasons for Medication Discrepancies
The subject‐provided reasons for medication discrepancies are listed in Table 3 and divided into patient‐ and system‐associated factors. The overall most frequent reason for a discrepancy was the patient's intentional nonadherence. Examples of intentional nonadherence include not sure of purpose of medication, did not recognize drug, did not fill prescription, did not need prescription, and wanted to wait longer, so not taking diuretic daily. The second most frequent reason was inaccurate discharge instructions (e.g., discharge instructions with medication changes denoting no change but incorrect outpatient thyroid medication dosage listed (Table 3).
Factor | Frequency, n (%) |
---|---|
Patient‐associated factors | |
Adverse drug effects | 8 (9.5) |
Intentional nonadherence | 50 (59.4) |
Unintentional nonadherence | 1 (1.2) |
Subtotal | 59 |
System‐associated factors | |
Confusion between brand and generic names | 3 (3.5) |
Discharge Instructions incomplete or inaccurate | 12 (14.2) |
Duplication | 3 (3.5) |
Incorrect dosage | 3 (3.5) |
Incorrect frequency | 1 (1.2) |
Conflicting information from different sources | 3 (3.5) |
Subtotal | 25 |
Total | 84 |
Logistic Regression Results for the Likelihood of Any Medication Discrepancy
Logistical regression results are shown in Table 4. Patients who were contacted by their PCP at discharge were 70% less likely to have a discrepancy when compared with those who were not contacted (P=0.03). This result was controlled for other possible factors including patient sex. Of interest, men were 3.94 times more likely to have a discrepancy when compared with women (P=0.02). There was also a nonsignificant but potentially important association between higher health literacy, measured continuously (0X) and being more likely to have a discrepancy (P=0.07). Including variables for age, ethnicity, and living alone were nonsignificant and did not change the regression results for contacted patients.
Odds Ratio | 95% Confidence Interval | |
---|---|---|
| ||
Subject contacted by PCP at Discharge | 0.33 | 0.110.97 |
Male | 3.98 | 1.2712.49 |
Number of medications | 1.09 | 0.961.23 |
TOHFLA score | 1.09 | 1.011.18 |
DISCUSSION
Our results provide evidence that contact with PCPs within 24 hours of hospital discharge can be effective in decreasing medication discrepancies. The PCP‐Enhanced Discharge Communication Intervention was designed to investigate the value of improving existing lines of communication at discharge without involving any additional healthcare personnel. As a lean discharge intervention, the PCP, the hospitalist, and the patient were the main components to this intervention.
This study was limited in that the sample size was small and that we enrolled consecutive patients. Due to the small sample size, we did not examine hospital readmissions. Further studies are needed to examine whether primary care involvement at discharge would affect hospital readmissions. Another limitation of this study was that the control group was not randomized or preselected. Our study compared those subjects who received a phone call from their PCP to those subjects who did not. Although we instructed PCPs with a standardized script, we did not record or ensure that the phone call‐up occurred as such. There is potential variability in how the PCPs conducted their follow‐up with patients, and we are unable to measure what was effective and ineffective in reducing medication discrepancies. Another limitation was that the determination of the severity of the medication discrepancy was done by medication involved as opposed to by physician review and adjudication. The study would have been strengthened by interviewing the outpatient physicians on the amount of harm each discrepancy would or did cause the patient.
The most frequent reason for discrepancy was intentional nonadherence. Prior research has shown that intentional nonadherence of medications at hospital discharge is linked to health literacy.[14] One may postulate that patients with adequate health literacy feel enabled to go against medical advice and chose to not take medications as prescribed.
In our study, patients had the most medication discrepancies in the severe medications category, which involved cardiac, pulmonary, and diabetic medications, compared with the mild and moderate category. This finding may reflect the frequency that these medications are prescribed but are consistent with findings of Coleman et al.[2] The finding highlights the need to ensure adequate education and understanding of medication regimens for these complex patients. Patients with cardiac, pulmonary, and diabetic disease may benefit from personalized discharge instructions and a more structured and organized medication reconciliation process.
Our study found that males were more likely to have a medication discrepancy than females, which has not been found in previous studies on medication discrepancies. One study on Medicare beneficiaries with congestive heart failure found that men were much more likely to be readmitted than woman within 6 months of discharge.[15] The reason for the increased risk of medication discrepancy in males is unknown. Gender differences in health have frequently been reported, with men having higher rates of morbidity and mortality than women.[16, 17] The differences are thought to be due to the reluctance of men to seek medical help and consult medical practitioners when needed. It has been known that women use health services more than men, and are more likely than men to report a chronic illness.[18] When men do present with symptoms, it is often later in the stage of a disease than women and when treatment is less likely to be successful.[19] It may be that men in this study population had more medication discrepancies as they were reluctant to seek help or ask for clarification regarding medications at discharge.
Of those enrolled, 36% of patients were contacted by their PCP within 48 hours of discharge. It is unclear if the PCP attempted but was unable to reach the patient or did not attempt to call the patient. Although PCPs were compensated with a $5 coffee shop gift card, a larger compensation may insure completion of the patient contact. Further research is needed to determine the reasons why PCPs were not able to complete the phone call.
From a policy standpoint, hospitals that focus solely on hospital‐based transition interventions are potentially missing half the problem. The hospital acts as a sender or pitcher, and the PCP acts as a receiver or catcher. The receiver needs to be included in the discharge process for a successful patient transition to home. With recent billing changes for transition coding, the Center for Medicare and Medicaid Services recognizes this relationship.[20] Outpatient PCPs are able to bill for bundled follow‐up phone calls and appointments. Instead of paying additional staff to make 48‐hour postdischarge phone calls, hospitals should consider partnering with PCPs to ensure a more organized discharge.
Our results showed that PCP communication with patients within 24 hours of discharge was associated with decreased medication discrepancies. The PCP is vital to ensuring a safe transition home from the hospital. Because many patients have an established relationship with their PCP, a bond of trust exists that is often missing with hospital‐employed transitional staff. Patients pay attention when a known physician contacts them directly. In our study, patients may have felt comfortable addressing their concerns and questions with their trusted PCP. Subsequently, patients may have been more attuned to the answers their PCP gave and avoided medication errors. Our results further demonstrate the importance of PCP involvement in the hospital discharge process to improve the care of our patients.
- The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167. , , , , .
- Posthospital medication discrepancies—prevalence and contributing factors. Arch Intern Med. 2005;165(16):1842–1847. , , , .
- Comprehensive discharge planning for the hospitalized elderly. A randomized clinical trial. Ann Intern Med. 1994;120(12):999–1006. , , , et al.
- Further application of the care transitions intervention: results of a randomized controlled trial conducted in a fee‐for‐service setting. Home Health Care Serv Q. 2009;28(2‐3):84–99. , , , , .
- Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission. N Engl J Med. 1996;334(22):1441–1446. , , .
- Reduction of 30‐day postdischarge hospital readmission or emergency department (ED) visit rates in high‐risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4(4):211–218. , , , et al.
- A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178–187. , , , et al.
- A case manager intervention to reduce readmissions. Arch Intern Med. 1994;154(15):1721–1729. , , , , .
- Preparing patients and caregivers to participate in care delivered across settings: the Care Transitions Intervention. J Am Geriatr Soc. 2004;52(11):1817–1825. , , , , , .
- Effect of a nurse team coordinator on outcomes for hospitalized medicine patients. Am J Med. 2005;118(10):1148–1153. , , , et al.
- Problems after discharge and understanding of communication with their primary care physicians among hospitalized seniors: a mixed methods study. J Hosp Med. 2010;5(7):385–391. , , , et al.
- Hospital readmissions in the Medicare population. N Engl J Med. 1984;311(21):1349–1353. , .
- Development of a brief test to measure functional health literacy. Patient Educ Couns. 1999;38(1):33–42. , , , , .
- Relationship of Health Literacy to Intentional and Unintentional Non‐Adherence of Hospital Discharge Medications. J Gen Intern Med. 2012;27(2):173–178. , , , , , .
- Readmission after hospitalization for congestive heart failure among Medicare beneficiaries. Arch Intern Med. 1997;157(1):99–104. , , , et al.
- College men's health: an overview and a call to action. J Am Coll Health. 1998;46(6):279–290. .
- Gender and the Social Construction of Illness. Thousand Oaks, CA: Sage; 1997.
- Inequalities in Health: The Black Report and the Health Divide. London, UK: Penguin; 1988.
- Decision making process in people with symptoms of acute myocardial infarction: qualitative study. BMJ. 2002;332:1006–1017. , , , .
- Centers for Medicare and Medicaid Services. Transitional Care Management Services. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/PhysicianFeeSched/Downloads/FAQ‐TCMS.pdf. Accessed June 28, 2013.
- The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167. , , , , .
- Posthospital medication discrepancies—prevalence and contributing factors. Arch Intern Med. 2005;165(16):1842–1847. , , , .
- Comprehensive discharge planning for the hospitalized elderly. A randomized clinical trial. Ann Intern Med. 1994;120(12):999–1006. , , , et al.
- Further application of the care transitions intervention: results of a randomized controlled trial conducted in a fee‐for‐service setting. Home Health Care Serv Q. 2009;28(2‐3):84–99. , , , , .
- Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission. N Engl J Med. 1996;334(22):1441–1446. , , .
- Reduction of 30‐day postdischarge hospital readmission or emergency department (ED) visit rates in high‐risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4(4):211–218. , , , et al.
- A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178–187. , , , et al.
- A case manager intervention to reduce readmissions. Arch Intern Med. 1994;154(15):1721–1729. , , , , .
- Preparing patients and caregivers to participate in care delivered across settings: the Care Transitions Intervention. J Am Geriatr Soc. 2004;52(11):1817–1825. , , , , , .
- Effect of a nurse team coordinator on outcomes for hospitalized medicine patients. Am J Med. 2005;118(10):1148–1153. , , , et al.
- Problems after discharge and understanding of communication with their primary care physicians among hospitalized seniors: a mixed methods study. J Hosp Med. 2010;5(7):385–391. , , , et al.
- Hospital readmissions in the Medicare population. N Engl J Med. 1984;311(21):1349–1353. , .
- Development of a brief test to measure functional health literacy. Patient Educ Couns. 1999;38(1):33–42. , , , , .
- Relationship of Health Literacy to Intentional and Unintentional Non‐Adherence of Hospital Discharge Medications. J Gen Intern Med. 2012;27(2):173–178. , , , , , .
- Readmission after hospitalization for congestive heart failure among Medicare beneficiaries. Arch Intern Med. 1997;157(1):99–104. , , , et al.
- College men's health: an overview and a call to action. J Am Coll Health. 1998;46(6):279–290. .
- Gender and the Social Construction of Illness. Thousand Oaks, CA: Sage; 1997.
- Inequalities in Health: The Black Report and the Health Divide. London, UK: Penguin; 1988.
- Decision making process in people with symptoms of acute myocardial infarction: qualitative study. BMJ. 2002;332:1006–1017. , , , .
- Centers for Medicare and Medicaid Services. Transitional Care Management Services. Available at: http://www.cms.gov/Medicare/Medicare‐Fee‐for‐Service‐Payment/PhysicianFeeSched/Downloads/FAQ‐TCMS.pdf. Accessed June 28, 2013.
© 2013 Society of Hospital Medicine
Studying Documentation
In 1968, Weed highlighted the importance of medical documentation when he proposed a single format for notes.[1, 2] Since then, sweeping changes in the technology, the purposes, and the requirements of clinical record keeping have encouraged steady growth of a literature devoted to the chart. Specifically, over the past half century, computers, lawsuits, regulations, and the use of documentation as a tool of billing have all contributed to the transformation of hospital records. In addition, mounting pressure to shorten inpatient stays, the vastly increased complexity of care, and a growing number of diagnostic possibilities have combined to make medical documentation far more prolific and far less leisurely. All these changes have stimulated a boom in documentation research coinciding, productively, with an era of rapid advances in the conduct of clinical trials and statistical rigor. However, in important respects research into medical documentation today is not asking the right questions, either in the formulation of hypotheses or in the choice of methodology. Forms of clinical communication that do not involve order sets or notes are widespread, growing in sophistication, and increasingly relevant to new concepts of healthcare as a team enterprise; but documentation research has not embraced this development. At the same time, methodologically, the field suffers from a persistent professional bias in the choice of research outcomes, a bias that limits the interpretation of results by neglecting what happens to the patient.
In assessing the chart as a communication device and the effect of changes in documentation, it is increasingly necessary to study direct interpersonal communication as an alternative and partner to writing notes. In particular, 3 recent developments in healthcare emphasize the importance of broadening our concepts of clinical communication. First, the need for discussion in the medical record has become less pressing because of technical improvements in person‐to‐person communication. Second, the electronic health record, by creating discipline‐defined chart views, has helped equalize the stature of different healthcare disciplines but also Balkanized the chart, making direct interdisciplinary communication more necessary. Third, changes in reimbursement are redefining medical goals in such a way that only teams of healthcare providers in close and constant personal communication can achieve them.
Rapid adoption of electronic health records has encouraged researchers studying documentation or information technology to focus on computer formats as defining the range of all possible communication strategies. And certainly there is a broad range of formats: electronic progress notes may be free text or multiple choice, typed or dictated, copy forwarded or composed daily, institutionally templated or self‐templated, furnished with or free from prompts and pop‐ups. However, it is not only, and perhaps not even principally, the electronic record that has changed how clinicians communicate with each other. The technology of discussion over the last 2 decades has become instant, utterly mobile, device independent, and capable of connecting all the patient's caregivers at once to each other and to the medical record in text, picture, and sound. That the same communications upheaval has visited practically every other aspect of our lives diminishes perhaps the visibility of this new virtual team in healthcare but not its importance.
The electronic record certainly plays a role in facilitating communication, through simultaneous chart access and in many other ways, but even more significant is the effect that computerization has had on equalizing the roles of different disciplines and by doing so in fragmenting the medical record. A computerized record expands and reorganizes the chart, changing it from a single authoritative book read by all to an almost limitless array of chart views, each read by some. All viewers (patient, clinician or researcher, administrator, reviewer or coder) can, with equal claim to consulting the chart, categorize, compare, combine, and format data elements from 1 or many encounters, whether inpatient or ambulatory. Typically, an electronic item of patient information may have several authors and uses but has no owner. Data are entered by protocol and in different guises into many aspects of patient care as components of notes, flow sheets, summaries, pop‐ups, and order sets unique to each of a number of disciplines. As the electronic record equalizes but also separates members of the healthcare team, interdisciplinary personal communication becomes more, not less, important.
Recent and impending reimbursement reform proves also to be a means of democratizing medical care and enforcing better interdisciplinary communication. The basis for hospital reimbursement has evolved over decades from day rates to payments for specific diseases, a system under which profit margins are in theory determined by the interdisciplinary efficiency with which diseases are managed by all care givers and the accuracy with which that management is documented. The next, seemingly inexorable, step in the evolution of reimbursement will result in further democratization of care givers: a single combined disease episode payment will be divided among all those involved in a course of treatment that may span many months and require many disciplines and many types of intervention. Payment reform makes the success of a visiting nurse as important to the net reimbursement of a disease episode as the success of an orthopedic surgeon; for if the visiting nurse does not do well the patient will be readmitted or require more office services. In this sense, payment reform, like the electronic record, tends both to equalize the importance of different healthcare roles and to require their enhanced communication.
As these changes in technology and reimbursement evolve, the study of medical documentation must increasingly address medical communication more generally. It is entirely possible, for example, that an individual daily progress note, whose preparation consumes so many hours and removes caretakers from patients, will no longer serve any demonstrable purpose.[3, 4] It may be that consensus summaries will prove more useful in clarifying one's own thinking and incorporating that of others than will a daily, solo chart soliloquy in free or imported text. It is conceivable that contrasting views will be best presented not as a debate in the progress notes but as a plan mutually agreed upon earlier in the decision‐making process. These are the kind of broader questions that investigators in medical documentation should be pursuing.
Another problem in studies of documentation is a pervasive professional bias in the choice of end points. Studies tend to evaluate documentary practices not by their effect on patients but by their impact on physicians or nurses. Success is measured by clinician satisfaction, percent adoption, and note length or timing; note quality is judged using a checklist derived from professional surveys.[5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] End points like these will often make 1 document look better than another in a results section, but it is the relation between communication strategies and healthcare outcomes that determines whether 1 approach or another is of benefit to the patient.
For example, an important current debate is whether free text adds essential nuance to a note or is simply nostalgia, a relic of the 3‐ring binder.[16, 17, 18] This debate can be resolved convincingly only if improvement with the use or abolition of free text is measured in terms of patient outcomes or resource consumption. Again, if it is important to know whether progress notes of a particular length or structure create less handover confusion, then changes in medical error rates is a more persuasive way to evaluate this issue than a change in physician opinion. It may be a good question whether briefer notes will free nurses and doctors to spend more time at the bedside, but along with recording bedside time that study should also measure improvement in reacting to important changes of clinical status. With today's technology, group phone discussions could perhaps successfully replace examining each other's notes, but the measure of success should be improved hospital efficiency or a decline in errors and readmissions.
The questions we ask in our research today create the treatments and policies of tomorrow. Our studies must address communications in a larger sense, must encompass all the settings in which an episode of care occurs, and must focus on patient outcomes and use of resources. The measured end points of an intervention should of course be sensitive to the particular setting where the intervention takes place, or else small and location‐specific gains will be missed. However, real health effects and robust measures of efficiency must take the place of word counts, inclusion checklists, and clinician adoption or satisfaction in the design of documentation studies.
A great national experiment is underway involving the deployment of information technology, the expansion and empowerment of healthcare teams, and the retargeting of economic incentives. The experimental hypothesis is that technology will increase medical efficiency and will benefit patient well‐being only if these are in fact the purposes, and if teamwork is the principal means, of providing medical care. We should seize this time of change as an opportunity to measure and demonstrably improve the contribution of medical documentation and communication to the efficient and long‐term remission of disease.
- Medical records that guide and teach. N Engl J Med. 1968;278(12):593–600. .
- Medical records that guide and teach. N Engl J Med. 1968;278(12):652–257. .
- Use of electronic clinical documentation: time spent and team interactions. J Am Med Inform Assoc. 2011;18(2):112–117. , , , .
- The influence of integrated electronic medical records and computerized nursing notes on nurses' time spent in documentation. Comput Inform Nurs. 2012;30(6):287–292. , , , , , .
- The hybrid progress note: semiautomating daily progress notes to achieve high‐quality documentation and improve provider efficiency. Am J Med Qual. 2013;28(1):25–32. , , , , .
- Preliminary development of the physician documentation quality instrument. J Am Med Inform Assoc. 2008;15(4):534–541. , , , .
- Evaluation of residents' delivery notes after a simulated shoulder dystocia. Obstet Gynecol. 2004;104(4):667–670. , , , , .
- Validity evidence for a patient note scoring rubric based on the new patient note format of the United States Medical Licensing Examination. Acad Med. 2013;88(10):1552–1557. , , , , , .
- Quality of outpatient clinical notes: a stakeholder definition derived through qualitative research. BMC Health Serv Res. 2012;12:407. , , , .
- Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform. 2008;77(5):291–304. , , .
- Randomised trial comparing the recording ability of a novel, electronic emergency documentation system with the AHA paper cardiac arrest record [published online ahead of print July 29, 2013]. Emerg Med J. doi: 10.1136/emermed‐2013‐202512. , , , et al.
- Generating clinical notes for electronic health record systems. Appl Clin Inform. 2010;1(3):232–243. , , , et al.
- The effects of EMR deployment on doctors' work practices: a qualitative study in the emergency department of a teaching hospital. Int J Med Inform. 2012;81(3):204–217. , , .
- Comparison of handheld computer‐assisted and conventional paper chart documentation of medical records. A randomized, controlled trial. J Bone Joint Surg Am. 2004;86A(3):553–560. , , , , .
- Assessing quality and efficiency of discharge summaries. Am J Med Qual. 2005;20(6):337–343. , , , , .
- Physicians' attitudes towards copy and pasting in electronic note writing. J Gen Intern Med. 2009;24(1):63–68. , , , , , .
- Association of medical directors of information systems consensus on inpatient electronic health record documentation. Appl Clin Inform. 2013;4(2):293–303. , , , .
- Method of electronic health record documentation and quality of primary care. Am Med Inform Assoc. 2012;19(6):1019–1024. , , .
In 1968, Weed highlighted the importance of medical documentation when he proposed a single format for notes.[1, 2] Since then, sweeping changes in the technology, the purposes, and the requirements of clinical record keeping have encouraged steady growth of a literature devoted to the chart. Specifically, over the past half century, computers, lawsuits, regulations, and the use of documentation as a tool of billing have all contributed to the transformation of hospital records. In addition, mounting pressure to shorten inpatient stays, the vastly increased complexity of care, and a growing number of diagnostic possibilities have combined to make medical documentation far more prolific and far less leisurely. All these changes have stimulated a boom in documentation research coinciding, productively, with an era of rapid advances in the conduct of clinical trials and statistical rigor. However, in important respects research into medical documentation today is not asking the right questions, either in the formulation of hypotheses or in the choice of methodology. Forms of clinical communication that do not involve order sets or notes are widespread, growing in sophistication, and increasingly relevant to new concepts of healthcare as a team enterprise; but documentation research has not embraced this development. At the same time, methodologically, the field suffers from a persistent professional bias in the choice of research outcomes, a bias that limits the interpretation of results by neglecting what happens to the patient.
In assessing the chart as a communication device and the effect of changes in documentation, it is increasingly necessary to study direct interpersonal communication as an alternative and partner to writing notes. In particular, 3 recent developments in healthcare emphasize the importance of broadening our concepts of clinical communication. First, the need for discussion in the medical record has become less pressing because of technical improvements in person‐to‐person communication. Second, the electronic health record, by creating discipline‐defined chart views, has helped equalize the stature of different healthcare disciplines but also Balkanized the chart, making direct interdisciplinary communication more necessary. Third, changes in reimbursement are redefining medical goals in such a way that only teams of healthcare providers in close and constant personal communication can achieve them.
Rapid adoption of electronic health records has encouraged researchers studying documentation or information technology to focus on computer formats as defining the range of all possible communication strategies. And certainly there is a broad range of formats: electronic progress notes may be free text or multiple choice, typed or dictated, copy forwarded or composed daily, institutionally templated or self‐templated, furnished with or free from prompts and pop‐ups. However, it is not only, and perhaps not even principally, the electronic record that has changed how clinicians communicate with each other. The technology of discussion over the last 2 decades has become instant, utterly mobile, device independent, and capable of connecting all the patient's caregivers at once to each other and to the medical record in text, picture, and sound. That the same communications upheaval has visited practically every other aspect of our lives diminishes perhaps the visibility of this new virtual team in healthcare but not its importance.
The electronic record certainly plays a role in facilitating communication, through simultaneous chart access and in many other ways, but even more significant is the effect that computerization has had on equalizing the roles of different disciplines and by doing so in fragmenting the medical record. A computerized record expands and reorganizes the chart, changing it from a single authoritative book read by all to an almost limitless array of chart views, each read by some. All viewers (patient, clinician or researcher, administrator, reviewer or coder) can, with equal claim to consulting the chart, categorize, compare, combine, and format data elements from 1 or many encounters, whether inpatient or ambulatory. Typically, an electronic item of patient information may have several authors and uses but has no owner. Data are entered by protocol and in different guises into many aspects of patient care as components of notes, flow sheets, summaries, pop‐ups, and order sets unique to each of a number of disciplines. As the electronic record equalizes but also separates members of the healthcare team, interdisciplinary personal communication becomes more, not less, important.
Recent and impending reimbursement reform proves also to be a means of democratizing medical care and enforcing better interdisciplinary communication. The basis for hospital reimbursement has evolved over decades from day rates to payments for specific diseases, a system under which profit margins are in theory determined by the interdisciplinary efficiency with which diseases are managed by all care givers and the accuracy with which that management is documented. The next, seemingly inexorable, step in the evolution of reimbursement will result in further democratization of care givers: a single combined disease episode payment will be divided among all those involved in a course of treatment that may span many months and require many disciplines and many types of intervention. Payment reform makes the success of a visiting nurse as important to the net reimbursement of a disease episode as the success of an orthopedic surgeon; for if the visiting nurse does not do well the patient will be readmitted or require more office services. In this sense, payment reform, like the electronic record, tends both to equalize the importance of different healthcare roles and to require their enhanced communication.
As these changes in technology and reimbursement evolve, the study of medical documentation must increasingly address medical communication more generally. It is entirely possible, for example, that an individual daily progress note, whose preparation consumes so many hours and removes caretakers from patients, will no longer serve any demonstrable purpose.[3, 4] It may be that consensus summaries will prove more useful in clarifying one's own thinking and incorporating that of others than will a daily, solo chart soliloquy in free or imported text. It is conceivable that contrasting views will be best presented not as a debate in the progress notes but as a plan mutually agreed upon earlier in the decision‐making process. These are the kind of broader questions that investigators in medical documentation should be pursuing.
Another problem in studies of documentation is a pervasive professional bias in the choice of end points. Studies tend to evaluate documentary practices not by their effect on patients but by their impact on physicians or nurses. Success is measured by clinician satisfaction, percent adoption, and note length or timing; note quality is judged using a checklist derived from professional surveys.[5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] End points like these will often make 1 document look better than another in a results section, but it is the relation between communication strategies and healthcare outcomes that determines whether 1 approach or another is of benefit to the patient.
For example, an important current debate is whether free text adds essential nuance to a note or is simply nostalgia, a relic of the 3‐ring binder.[16, 17, 18] This debate can be resolved convincingly only if improvement with the use or abolition of free text is measured in terms of patient outcomes or resource consumption. Again, if it is important to know whether progress notes of a particular length or structure create less handover confusion, then changes in medical error rates is a more persuasive way to evaluate this issue than a change in physician opinion. It may be a good question whether briefer notes will free nurses and doctors to spend more time at the bedside, but along with recording bedside time that study should also measure improvement in reacting to important changes of clinical status. With today's technology, group phone discussions could perhaps successfully replace examining each other's notes, but the measure of success should be improved hospital efficiency or a decline in errors and readmissions.
The questions we ask in our research today create the treatments and policies of tomorrow. Our studies must address communications in a larger sense, must encompass all the settings in which an episode of care occurs, and must focus on patient outcomes and use of resources. The measured end points of an intervention should of course be sensitive to the particular setting where the intervention takes place, or else small and location‐specific gains will be missed. However, real health effects and robust measures of efficiency must take the place of word counts, inclusion checklists, and clinician adoption or satisfaction in the design of documentation studies.
A great national experiment is underway involving the deployment of information technology, the expansion and empowerment of healthcare teams, and the retargeting of economic incentives. The experimental hypothesis is that technology will increase medical efficiency and will benefit patient well‐being only if these are in fact the purposes, and if teamwork is the principal means, of providing medical care. We should seize this time of change as an opportunity to measure and demonstrably improve the contribution of medical documentation and communication to the efficient and long‐term remission of disease.
In 1968, Weed highlighted the importance of medical documentation when he proposed a single format for notes.[1, 2] Since then, sweeping changes in the technology, the purposes, and the requirements of clinical record keeping have encouraged steady growth of a literature devoted to the chart. Specifically, over the past half century, computers, lawsuits, regulations, and the use of documentation as a tool of billing have all contributed to the transformation of hospital records. In addition, mounting pressure to shorten inpatient stays, the vastly increased complexity of care, and a growing number of diagnostic possibilities have combined to make medical documentation far more prolific and far less leisurely. All these changes have stimulated a boom in documentation research coinciding, productively, with an era of rapid advances in the conduct of clinical trials and statistical rigor. However, in important respects research into medical documentation today is not asking the right questions, either in the formulation of hypotheses or in the choice of methodology. Forms of clinical communication that do not involve order sets or notes are widespread, growing in sophistication, and increasingly relevant to new concepts of healthcare as a team enterprise; but documentation research has not embraced this development. At the same time, methodologically, the field suffers from a persistent professional bias in the choice of research outcomes, a bias that limits the interpretation of results by neglecting what happens to the patient.
In assessing the chart as a communication device and the effect of changes in documentation, it is increasingly necessary to study direct interpersonal communication as an alternative and partner to writing notes. In particular, 3 recent developments in healthcare emphasize the importance of broadening our concepts of clinical communication. First, the need for discussion in the medical record has become less pressing because of technical improvements in person‐to‐person communication. Second, the electronic health record, by creating discipline‐defined chart views, has helped equalize the stature of different healthcare disciplines but also Balkanized the chart, making direct interdisciplinary communication more necessary. Third, changes in reimbursement are redefining medical goals in such a way that only teams of healthcare providers in close and constant personal communication can achieve them.
Rapid adoption of electronic health records has encouraged researchers studying documentation or information technology to focus on computer formats as defining the range of all possible communication strategies. And certainly there is a broad range of formats: electronic progress notes may be free text or multiple choice, typed or dictated, copy forwarded or composed daily, institutionally templated or self‐templated, furnished with or free from prompts and pop‐ups. However, it is not only, and perhaps not even principally, the electronic record that has changed how clinicians communicate with each other. The technology of discussion over the last 2 decades has become instant, utterly mobile, device independent, and capable of connecting all the patient's caregivers at once to each other and to the medical record in text, picture, and sound. That the same communications upheaval has visited practically every other aspect of our lives diminishes perhaps the visibility of this new virtual team in healthcare but not its importance.
The electronic record certainly plays a role in facilitating communication, through simultaneous chart access and in many other ways, but even more significant is the effect that computerization has had on equalizing the roles of different disciplines and by doing so in fragmenting the medical record. A computerized record expands and reorganizes the chart, changing it from a single authoritative book read by all to an almost limitless array of chart views, each read by some. All viewers (patient, clinician or researcher, administrator, reviewer or coder) can, with equal claim to consulting the chart, categorize, compare, combine, and format data elements from 1 or many encounters, whether inpatient or ambulatory. Typically, an electronic item of patient information may have several authors and uses but has no owner. Data are entered by protocol and in different guises into many aspects of patient care as components of notes, flow sheets, summaries, pop‐ups, and order sets unique to each of a number of disciplines. As the electronic record equalizes but also separates members of the healthcare team, interdisciplinary personal communication becomes more, not less, important.
Recent and impending reimbursement reform proves also to be a means of democratizing medical care and enforcing better interdisciplinary communication. The basis for hospital reimbursement has evolved over decades from day rates to payments for specific diseases, a system under which profit margins are in theory determined by the interdisciplinary efficiency with which diseases are managed by all care givers and the accuracy with which that management is documented. The next, seemingly inexorable, step in the evolution of reimbursement will result in further democratization of care givers: a single combined disease episode payment will be divided among all those involved in a course of treatment that may span many months and require many disciplines and many types of intervention. Payment reform makes the success of a visiting nurse as important to the net reimbursement of a disease episode as the success of an orthopedic surgeon; for if the visiting nurse does not do well the patient will be readmitted or require more office services. In this sense, payment reform, like the electronic record, tends both to equalize the importance of different healthcare roles and to require their enhanced communication.
As these changes in technology and reimbursement evolve, the study of medical documentation must increasingly address medical communication more generally. It is entirely possible, for example, that an individual daily progress note, whose preparation consumes so many hours and removes caretakers from patients, will no longer serve any demonstrable purpose.[3, 4] It may be that consensus summaries will prove more useful in clarifying one's own thinking and incorporating that of others than will a daily, solo chart soliloquy in free or imported text. It is conceivable that contrasting views will be best presented not as a debate in the progress notes but as a plan mutually agreed upon earlier in the decision‐making process. These are the kind of broader questions that investigators in medical documentation should be pursuing.
Another problem in studies of documentation is a pervasive professional bias in the choice of end points. Studies tend to evaluate documentary practices not by their effect on patients but by their impact on physicians or nurses. Success is measured by clinician satisfaction, percent adoption, and note length or timing; note quality is judged using a checklist derived from professional surveys.[5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] End points like these will often make 1 document look better than another in a results section, but it is the relation between communication strategies and healthcare outcomes that determines whether 1 approach or another is of benefit to the patient.
For example, an important current debate is whether free text adds essential nuance to a note or is simply nostalgia, a relic of the 3‐ring binder.[16, 17, 18] This debate can be resolved convincingly only if improvement with the use or abolition of free text is measured in terms of patient outcomes or resource consumption. Again, if it is important to know whether progress notes of a particular length or structure create less handover confusion, then changes in medical error rates is a more persuasive way to evaluate this issue than a change in physician opinion. It may be a good question whether briefer notes will free nurses and doctors to spend more time at the bedside, but along with recording bedside time that study should also measure improvement in reacting to important changes of clinical status. With today's technology, group phone discussions could perhaps successfully replace examining each other's notes, but the measure of success should be improved hospital efficiency or a decline in errors and readmissions.
The questions we ask in our research today create the treatments and policies of tomorrow. Our studies must address communications in a larger sense, must encompass all the settings in which an episode of care occurs, and must focus on patient outcomes and use of resources. The measured end points of an intervention should of course be sensitive to the particular setting where the intervention takes place, or else small and location‐specific gains will be missed. However, real health effects and robust measures of efficiency must take the place of word counts, inclusion checklists, and clinician adoption or satisfaction in the design of documentation studies.
A great national experiment is underway involving the deployment of information technology, the expansion and empowerment of healthcare teams, and the retargeting of economic incentives. The experimental hypothesis is that technology will increase medical efficiency and will benefit patient well‐being only if these are in fact the purposes, and if teamwork is the principal means, of providing medical care. We should seize this time of change as an opportunity to measure and demonstrably improve the contribution of medical documentation and communication to the efficient and long‐term remission of disease.
- Medical records that guide and teach. N Engl J Med. 1968;278(12):593–600. .
- Medical records that guide and teach. N Engl J Med. 1968;278(12):652–257. .
- Use of electronic clinical documentation: time spent and team interactions. J Am Med Inform Assoc. 2011;18(2):112–117. , , , .
- The influence of integrated electronic medical records and computerized nursing notes on nurses' time spent in documentation. Comput Inform Nurs. 2012;30(6):287–292. , , , , , .
- The hybrid progress note: semiautomating daily progress notes to achieve high‐quality documentation and improve provider efficiency. Am J Med Qual. 2013;28(1):25–32. , , , , .
- Preliminary development of the physician documentation quality instrument. J Am Med Inform Assoc. 2008;15(4):534–541. , , , .
- Evaluation of residents' delivery notes after a simulated shoulder dystocia. Obstet Gynecol. 2004;104(4):667–670. , , , , .
- Validity evidence for a patient note scoring rubric based on the new patient note format of the United States Medical Licensing Examination. Acad Med. 2013;88(10):1552–1557. , , , , , .
- Quality of outpatient clinical notes: a stakeholder definition derived through qualitative research. BMC Health Serv Res. 2012;12:407. , , , .
- Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform. 2008;77(5):291–304. , , .
- Randomised trial comparing the recording ability of a novel, electronic emergency documentation system with the AHA paper cardiac arrest record [published online ahead of print July 29, 2013]. Emerg Med J. doi: 10.1136/emermed‐2013‐202512. , , , et al.
- Generating clinical notes for electronic health record systems. Appl Clin Inform. 2010;1(3):232–243. , , , et al.
- The effects of EMR deployment on doctors' work practices: a qualitative study in the emergency department of a teaching hospital. Int J Med Inform. 2012;81(3):204–217. , , .
- Comparison of handheld computer‐assisted and conventional paper chart documentation of medical records. A randomized, controlled trial. J Bone Joint Surg Am. 2004;86A(3):553–560. , , , , .
- Assessing quality and efficiency of discharge summaries. Am J Med Qual. 2005;20(6):337–343. , , , , .
- Physicians' attitudes towards copy and pasting in electronic note writing. J Gen Intern Med. 2009;24(1):63–68. , , , , , .
- Association of medical directors of information systems consensus on inpatient electronic health record documentation. Appl Clin Inform. 2013;4(2):293–303. , , , .
- Method of electronic health record documentation and quality of primary care. Am Med Inform Assoc. 2012;19(6):1019–1024. , , .
- Medical records that guide and teach. N Engl J Med. 1968;278(12):593–600. .
- Medical records that guide and teach. N Engl J Med. 1968;278(12):652–257. .
- Use of electronic clinical documentation: time spent and team interactions. J Am Med Inform Assoc. 2011;18(2):112–117. , , , .
- The influence of integrated electronic medical records and computerized nursing notes on nurses' time spent in documentation. Comput Inform Nurs. 2012;30(6):287–292. , , , , , .
- The hybrid progress note: semiautomating daily progress notes to achieve high‐quality documentation and improve provider efficiency. Am J Med Qual. 2013;28(1):25–32. , , , , .
- Preliminary development of the physician documentation quality instrument. J Am Med Inform Assoc. 2008;15(4):534–541. , , , .
- Evaluation of residents' delivery notes after a simulated shoulder dystocia. Obstet Gynecol. 2004;104(4):667–670. , , , , .
- Validity evidence for a patient note scoring rubric based on the new patient note format of the United States Medical Licensing Examination. Acad Med. 2013;88(10):1552–1557. , , , , , .
- Quality of outpatient clinical notes: a stakeholder definition derived through qualitative research. BMC Health Serv Res. 2012;12:407. , , , .
- Definition, structure, content, use and impacts of electronic health records: a review of the research literature. Int J Med Inform. 2008;77(5):291–304. , , .
- Randomised trial comparing the recording ability of a novel, electronic emergency documentation system with the AHA paper cardiac arrest record [published online ahead of print July 29, 2013]. Emerg Med J. doi: 10.1136/emermed‐2013‐202512. , , , et al.
- Generating clinical notes for electronic health record systems. Appl Clin Inform. 2010;1(3):232–243. , , , et al.
- The effects of EMR deployment on doctors' work practices: a qualitative study in the emergency department of a teaching hospital. Int J Med Inform. 2012;81(3):204–217. , , .
- Comparison of handheld computer‐assisted and conventional paper chart documentation of medical records. A randomized, controlled trial. J Bone Joint Surg Am. 2004;86A(3):553–560. , , , , .
- Assessing quality and efficiency of discharge summaries. Am J Med Qual. 2005;20(6):337–343. , , , , .
- Physicians' attitudes towards copy and pasting in electronic note writing. J Gen Intern Med. 2009;24(1):63–68. , , , , , .
- Association of medical directors of information systems consensus on inpatient electronic health record documentation. Appl Clin Inform. 2013;4(2):293–303. , , , .
- Method of electronic health record documentation and quality of primary care. Am Med Inform Assoc. 2012;19(6):1019–1024. , , .
Sleep disturbances in cancer patients: Underrecognized and undertreated
Many cancer patients don't sleep well, for a variety of reasons. It is an important problem: not only does poor sleep worsen quality of life, it may affect prognosis. Moreover, treatment is available.
Yet many physicians caring for cancer patients do not ask about sleep problems, underestimating their impact or focusing on more urgent problems. Also, patients may not want to bring up the topic because they consider poor sleep to be unavoidable and untreatable and because they fear that reporting it may shift the focus of their treatment from trying to cure the cancer to easing its symptoms.
This practical review will help health care professionals avoid the common barriers to diagnosis and treatment of poor sleep in cancer patients. Because there are few data on other sleep disorders such as sleep apnea and restless leg syndrome, we will focus on the most common one in cancer patients—insomnia—and its effects on other symptoms and quality of life.
MORE PATIENTS SURVIVE CANCER NOW
Today, more patients are surviving cancer, but cancer symptoms and the side effects of surgery, chemotherapy, and radiation therapy may persist for years.1,2 The most common complaints include cancer-related fatigue, leg restlessness, anxiety, insomnia, and excessive sleepiness.3
Sleep disturbances appear to contribute to the other problems and are relatively easier to quantify. Most studies of sleep disorders in cancer patients have looked specifically at insomnia,4 although a few have explored the prevalence of other sleep disorders, such as sleep-disordered breathing and limb movements during sleep.5
The International Classification of Sleep Disorders, 2nd edition,6 defines insomnia as difficulty going to sleep or staying asleep (the latter defined as waking up in the middle of the night, with wakeful episodes lasting more than 30 minutes), early-morning awakenings (waking 30 minutes or more before the intended time), or nonrestorative sleep, causing significant distress or impairment of day-time functioning.
INSOMNIA WORSENS QUALITY OF LIFE
Insomnia significantly worsens quality of life in cancer patients, and if it can be detected and effectively treated, quality of life is likely to improve. Studies in cancer patients have found that those with insomnia:
- Were less able to cope with stress and carry on their activities of daily living3
- Were much less able to function and reported more pain, less energy, and greater difficulty in dealing with emotional problems7
- Had poor quality of life, both physically and emotionally.3,8
PERHAPS MORE THAN HALF OF CANCER PATIENTS HAVE INSOMNIA
Depending on the methods used and populations studied, at least 30% and perhaps more than half of patients with cancer have insomnia (Table 1).3,4,8–14 It is one of the most commonly reported complaints in this group,15–17 and it occurs before, during, and after treatment of cancer.
Although the prevalence may differ in various cancers, it is still higher than in the general population. In a study of about 450 patients with cancer or depression and 300 healthy volunteers, 62% of the cancer patients reported moderate to severe sleep disturbance, compared with 52% of the depressed patients and 30% of the healthy volunteers.18
When Davidson et al3 surveyed nearly 1,000 cancer patients, one-third said they had insomnia. The problem was most prevalent in lung and breast cancer patients.
In a longitudinal study by Savard et al,13 the prevalence of insomnia declined over time but remained high even at the end of 18 months. It was more prevalent in patients with gynecologic and breast cancer than in those with prostate cancer.13,19
SLEEP PROBLEMS ARE UNDERREPORTED
Sleep problems in cancer patients often go unrecognized because patients do not report them. In a survey of 150 patients,20 44% reported having had sleep problems during the preceding month. However, only one-third of those with sleep problems told their health care providers. This highlights the need for physicians to address sleep complaints in cancer patients at every visit and, if needed, to refer them to a sleep specialist for further evaluation and management.
INSOMNIA IS OFTEN ASSOCIATED WITH OTHER PROBLEMS
Many things can interfere with sleep in cancer patients: the cancer itself (eg, pain due to tumor invasion), medical treatments (eg, narcotics, chemotherapy, neuroleptics, sympathomimetics, steroids, sedative hypnotics), psychosocial disturbances (eg, depression, anxiety, stress), and comorbid medical issues.
In this population, insomnia is often part of a cluster of symptoms that includes pain, fatigue, depression, and anxiety. These act synergistically, worsening quality of life.21–24
Cancer-related fatigue and insomnia
Cancer-related fatigue is a distressing, persistent, subjective sense of tiredness or exhaustion that is related to cancer or cancer treatment, that is not proportional to recent activity and that interferes with usual functioning.25 It has been reported by up to 90% of cancer patients in some studies.26–28
Cancer-related fatigue worsens quality of life and is one of the most distressing and persistent symptoms experienced before, during, and after cancer treatment.29,30 Furthermore, it can lead to sleep disturbances and daytime somnolence and further aggravate insomnia.31,32 The two conditions are often reported as part of a cluster of interrelated symptoms that include pain, depression, and loss of concentration and other cognitive functions, suggesting that they may share a common etiology.33–35
Åhsberg et al36 examined different aspects of perceived cancer-related fatigue in patients undergoing radiotherapy and found correlations between lack of energy, sleepiness, and cancer-related fatigue.
Current understanding of the possible link between cancer-related fatigue and insomnia suggests that interventions targeting the insomnia and daytime sleepiness could decrease the fatigue as well.31
Pain and insomnia in cancer patients
Pain is reported by 60% to 90% of patients with advanced cancer,37,38 its intensity usually varying with the extent of disease. Too often, it is inadequately controlled.39 Furthermore, it is thought to contribute to insomnia.40
In a study of more than 1,600 cancer patients, nearly 60% reported insomnia in addition to pain.41 The severity of pain directly correlated with the probability of insomnia.
Conversely, research suggests that sleep disturbances, primarily insomnia, can increase cancer patients’ sensitivity to pain.42 One hypothesis is that adequate sleep is needed to promote processes relevant to recovery from pain, both physiologic (ie, tissue repair) and psychological (ie, transient cessation of the perception of pain signals).43
Paradoxically, opioids can worsen insomnia
Cancer pain is often treated with opioids, which, paradoxically, can cause or worsen insomnia.
Although opioids induce sleep, they also depress respiration, and at night, they can cause or worsen sleep-disordered breathing (obstructive or central sleep apnea or ataxic breathing), leading to episodes of hypoxia, arousals, and fragmented sleep.44 Moreover, opioids can lead to daytime sedation. Further, psychostimulants such as methylphenidate, given to counteract opioid-induced sedation, can cause anxiety and insomnia. Thus, the interaction between cancer-related pain, insomnia, and pain management leads to a vicious cycle. Understanding this process, we can try to break the cycle and help patients with cancer sleep better.
However, how best to treat sleep-disordered breathing in patients taking opioids long-term is not well established.
In general, the primary intervention is to reduce the opioid dose. Practitioners should continually assess the need for these drugs and consider referral to a drug-behavior treatment center to help with discontinuation of opioid use when deemed medically appropriate.45 Other strategies include positive airway pressure ventilation including continuous positive airway pressure, bilevel pressure devices with backup rate, or adaptive servoventilators. In some cases oxygen supplementation may be required.
Sleep-disordered breathing, when recognized and diagnosed, should be managed in partnership with a sleep specialist.
Depression and insomnia in cancer patients
By some estimates, up to half of cancer patients suffer from depression at some point in their illness.28 And not without reason: these patients face uncertainty about their life, and this often results in depression or anxiety.46
Many cancer patients with depression also have insomnia.28 Indeed, patients with persistent insomnia are at greater risk of developing psychological disorders such as depression and anxiety.47
In a survey of cancer patients, insomnia symptoms were more often attributed to thoughts or concerns about health, family, friends, the cancer diagnosis, and finances than to the actual physical effects of cancer.48
CANCER TREATMENT AND INSOMNIA
Many cancer patients experience sleep disturbances even before starting treatment.49 Liu et al50 showed that, in 76 women about to undergo chemotherapy for breast cancer, those who already had sleep disturbances, fatigue, and depression had more problems, and more severe problems, during chemotherapy.
Radiation therapy and chemotherapy have been reported to cause or precipitate insomnia (Table 2).8,13
Hormonal therapy and biological therapy can also cause or worsen preexisting insomnia.51,52 For example, androgen deprivation therapy for prostate cancer and hormonal therapy for breast cancer are often associated with sleep problems.49,50 Possible mechanisms of insomnia include hot flashes, night sweats, and anxiety caused by such treatments. Biological agents such as interferons, interleukins, and tumor necrosis factor (TNF) alpha, which are often used to treat malignant melanoma, can affect the sleep-wake cycle, leading to insomnia.53
Corticosteroids sharply raise serum cortisol levels, which can lead to insomnia. Cancer patients receiving dexamethasone to prevent radiation-induced emesis experienced more insomnia than patients who did not receive dexamethasone.54
IMMUNOLOGIC BASIS OF INSOMNIA IN CANCER PATIENTS
Cancer cells produce inflammatory cytokines such as interleukin 1 (IL-1), interleukin 6 (IL-6), and TNF alpha, and inflammation plays a role in tumor progression and possibly tumorigenesis.55
Specific cytokines also help regulate the sleep-wake cycle. Levels of IL-6 and TNF alpha peak during sleep, and daytime IL-6 levels are inversely related to the amount of nocturnal sleep.56 Vgontzas et al57 showed that although mean levels of 24-hour IL-6 and TNF alpha secretion were not significantly different in patients with insomnia vs healthy controls, chronic insomnia was associated with a shift in IL-6 and TNF alpha secretion from nighttime to daytime.57
Cancer and its treatment can affect secretion of the cytokines that play a role in the sleep-wake cycle. Thus, the sleep disturbances associated with cancer may also be related to the abnormalities in cytokine levels caused by either cancer or its treatment.
Mills et al58 found that inflammatory markers such as vascular endothelial growth factor and soluble intercellular adhesion molecule-1 were significantly elevated during chemotherapy in breast cancer patients, and the elevated vascular endothelial growth factor levels were associated with poorer sleep during treatment.
Further research is warranted to establish causality, to help us understand the mechanisms of insomnia and other cancer symptoms, and to develop new treatments for these complaints.
POOR SLEEP AND CANCER RISK AND OUTCOMES
Sleep disturbances have negative health consequences in cancer. Their impact ranges from plausible carcinogenesis to affecting the course of the disease and cancer survival.
Poor sleep and risk of cancer
Epidemiologic studies have examined a possible link between circadian rhythm disruption and breast cancer risk, using both direct measures such as melatonin levels and indirect measures such as sleep duration and shift work. (Melatonin production is related to sleep duration, and night-shift work leads to disruption of sleep pattern and quality of sleep, thus lowering melatonin levels.59)
The findings were mixed. Breast cancer risk was significantly and inversely associated with urinary melatonin levels (6-sulfatoxymelatonin) in the Nurses’ Health Study II,60 but not in the Guernsey III study in the United Kingdom.61 Breast cancer risk was significantly lower with longer sleep duration in Finnish women62 and in Chinese women in Singapore,63 but not in American women.64,65 Results of three cohort studies66–68 and two case-control studies69,70 suggested a higher breast cancer risk in women who work evening or overnight shifts. Shorter sleep duration was associated with a higher risk of colorectal adenomas.71
These studies make a strong case for an association of cancer with circadian rhythm disruption and shorter sleep duration, possibly from an effect on melatonin levels. However, one should be cautious in interpreting epidemiologic studies: although they show sleep disturbances to be associated with cancer risk, they do not establish causality.
Insomnia and cancer outcomes
Evidence is growing that sleep disturbances may affect compliance with treatment, immune function, and outcomes—including survival—in cancer patients.23,24
In patients newly diagnosed with various types of cancer, Degner and Sloan72 showed that those who suffered from insomnia, nausea, poor appetite, and pain had a lower survival rate at 5 years, independent of the cancer stage. However, no separate analyses were performed to examine the specific influence of insomnia on cancer survival.
Thompson and Li73 analyzed data from 101 breast cancer patients with available Oncotype DX recurrence scores (a proprietary genetic test performed on tumor tissue that predicts the likelihood of recurrence). The scores were strongly correlated with average hours of sleep per night before breast cancer diagnosis, with fewer hours of sleep associated with a higher (worse) score.
Since these studies were retrospective and merely suggest associations, prospective studies, using more standardized questionnaires and objective measures, are needed to establish causality and to further our understanding of the mechanisms involved.
HELPING CANCER PATIENTS SLEEP BETTER
Insomnia is generally diagnosed with a thorough history that includes sleep, medical issues, substance use, and psychiatric issues. The sleep history should include specific insomniarelated complaints, presleep conditions and habits, sleep-wake habits, other sleep-related symptoms, and daytime consequences. To obtain the information, one can use questionnaires, sleep logs, psychological screening tests, and bed-partner interviews.74
Managing insomnia involves both pharmacologic and nonpharmacologic treatment. It is also important to treat the associated disorders such as depression and anxiety disorders that often accompany insomnia. Long-term management of cancer patients should not be limited to surveillance of cancer but should also involve aggressive treatment of clusters of symptoms such as insomnia, cancer-related fatigue, and pain to yield better long-term quality of life.75–77
Nonpharmacologic treatment: Cognitive-behavioral therapy
Nonpharmacologic interventions use psychological and behavioral therapies. The American Academy of Sleep Medicine guidelines recommend cognitive behavioral therapy for all patients with insomnia, either alone or in combination with hypnotic medications.
Cognitive-behavioral therapy for insomnia includes various components that help the patient learn coping skills and ways to prevent or mitigate the severity of future episodes (Table 3). Various randomized controlled trials found it to be effective for treating insomnia in the general population.77–79
Several studies found that cognitive-behavioral therapy for insomnia was effective in cancer patients, not only improving sleep quality but also decreasing psychological distress, resulting in better overall quality of life.80,81
Savard et al81 conducted a randomized controlled trial of cognitive-behavioral therapy for insomnia in 57 patients with breast cancer, examining subjective and objective sleep measures, psychological functioning, quality of life, and immunologic responses. They found significant improvements in sleep efficiency, mood, quality of life, depression, anxiety, and need for sleep medications. Improvements in subjective sleep measures persisted on 12-month follow-up.
Berger et al,82 in another randomized controlled trial, assessed behavioral therapy using stimulus control, modified sleep restriction, relaxation therapy, and sleep hygiene in breast cancer patients receiving adjuvant chemotherapy. Behavioral therapy improved sleep quality over time, as measured by the Pittsburgh Sleep Quality Index.
Espie et al83 evaluated the effect of cognitive-behavioral therapy on prostate, colorectal, gynecologic, and breast cancer patients, with similar results.83
Cognitive-behavioral therapy is at least as effective as drug therapy for insomnia in the general population. In the limited studies done in cancer patients, it has been shown to be effective irrespective of the type of cancer and is associated with better long-term outcomes. It diminishes the distress associated with early insomnia, can reduce anxiety, and can promote sleep.
A National Institutes of Health conference on insomnia concluded that cognitivebehavioral therapy is at least as effective as medications for brief treatment of chronic insomnia and that its beneficial effects, in contrast to those produced by medications, may last beyond the termination of treatment.84
It is important to think about numerous factors when considering options such as cognitive-behavioral therapy, as patients with cancer have different complications that may affect sleep quality, such as cancer-related fatigue, cancer-related depression, psychological reactions to the disease, side effects of treatment, and cancer-related pain. These need to be addressed as well.
If cognitive-behavioral therapy is not available, self-help interventions (eg, written material, videos, television and Internet resources) can be used. These have several advantages over professionally administered interventions, including greater accessibility, less burden for the patient, and lower cost. Research is under way evaluating this approach in cancer patients.85
Drug therapy
The focus of therapy should be to treat underlying disorders that may be causing or contributing to insomnia. However, a substantial number of patients may need to be assessed for pharmacotherapy for insomnia.
Sleep problems in the general population are commonly treated with drugs, and most of the recommendations in cancer patients are based on experience in the general population. However, sleep medications should be used cautiously in cancer patients, since to our knowledge there have been no studies of these agents in patients with cancer.
Side effects also need to be considered. For example, sleep medications can profoundly worsen cancer-related fatigue.
Hypnotics are often prescribed for cancer patients.86,87 A study in five major oncology centers showed that about half of the 1,500 patients were prescribed at least one psychotropic drug.86 In this study, hypnotics were the most frequently prescribed drugs, accounting for 48% of total prescriptions, and 44% of the psychotropic prescriptions were written for sleep.
Benzodiazepine receptor agonists such as zaleplon, zolpidem, and eszopiclone can be used for problems with falling asleep and staying asleep.88,89 They are better tolerated than older, long-acting benzodiazepines,90 which can cause alterations in sleep-cycle architecture or rebound insomnia. The earlier agents can also cause adverse effects such as tolerance, drowsiness, and cognitive impairment.
A National Institutes of Health conference stated that benzodiazepine receptor agonists are efficacious in the short-term management of insomnia and that their adverse effects are much less frequent and severe than those of the benzodiazepines or other sedating drugs.84 It also stated that all antidepressants, antihistamines (H1 receptor antagonists), and anti-psychotics have potentially significant adverse effects that raise concerns about their risk-to-benefit ratio and their suitability as treatment for chronic insomnia.
Benzodiazepines are commonly prescribed for insomnia. They increase sleep efficiency, decrease arousals, and increase stage 2 sleep.
Melatonin receptor agonists have been approved by the US Food and Drug Administration for treating insomnia. A recent meta-analysis of eight studies in healthy patients showed improvements in subjective and objective sleep outcomes with the use of ramelteon.91 The dosages primarily used were 4 to 32 mg. However, most of the studies used a dosage of 4 to 8 mg.
Antidepressants. Some of the antidepressants are also used for insomnia, but they can cause daytime fatigue.
Mirtazapine was shown to be effective for insomnia and coexistent mood disorder in cancer patients, but larger trials are needed.92
A recent clinical trial with secondary data analyses evaluated the effect of paroxetine on insomnia, depression, and fatigue in patients with cancer. Paroxetine significantly reduced insomnia in both depressed and nondepressed patients after 2 to 3 weeks of treatment.93
Table 4 summarizes classes of drugs used for insomnia and their additional therapeutic properties.
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- Palesh OG, Mustian KM, Peppone LJ, et al. Impact of paroxetine on sleep problems in 426 cancer patients receiving chemotherapy: a trial from the University of Rochester Cancer Center Community Clinical Oncology Program. Sleep Med 2012; 13:1184–1190.
Many cancer patients don't sleep well, for a variety of reasons. It is an important problem: not only does poor sleep worsen quality of life, it may affect prognosis. Moreover, treatment is available.
Yet many physicians caring for cancer patients do not ask about sleep problems, underestimating their impact or focusing on more urgent problems. Also, patients may not want to bring up the topic because they consider poor sleep to be unavoidable and untreatable and because they fear that reporting it may shift the focus of their treatment from trying to cure the cancer to easing its symptoms.
This practical review will help health care professionals avoid the common barriers to diagnosis and treatment of poor sleep in cancer patients. Because there are few data on other sleep disorders such as sleep apnea and restless leg syndrome, we will focus on the most common one in cancer patients—insomnia—and its effects on other symptoms and quality of life.
MORE PATIENTS SURVIVE CANCER NOW
Today, more patients are surviving cancer, but cancer symptoms and the side effects of surgery, chemotherapy, and radiation therapy may persist for years.1,2 The most common complaints include cancer-related fatigue, leg restlessness, anxiety, insomnia, and excessive sleepiness.3
Sleep disturbances appear to contribute to the other problems and are relatively easier to quantify. Most studies of sleep disorders in cancer patients have looked specifically at insomnia,4 although a few have explored the prevalence of other sleep disorders, such as sleep-disordered breathing and limb movements during sleep.5
The International Classification of Sleep Disorders, 2nd edition,6 defines insomnia as difficulty going to sleep or staying asleep (the latter defined as waking up in the middle of the night, with wakeful episodes lasting more than 30 minutes), early-morning awakenings (waking 30 minutes or more before the intended time), or nonrestorative sleep, causing significant distress or impairment of day-time functioning.
INSOMNIA WORSENS QUALITY OF LIFE
Insomnia significantly worsens quality of life in cancer patients, and if it can be detected and effectively treated, quality of life is likely to improve. Studies in cancer patients have found that those with insomnia:
- Were less able to cope with stress and carry on their activities of daily living3
- Were much less able to function and reported more pain, less energy, and greater difficulty in dealing with emotional problems7
- Had poor quality of life, both physically and emotionally.3,8
PERHAPS MORE THAN HALF OF CANCER PATIENTS HAVE INSOMNIA
Depending on the methods used and populations studied, at least 30% and perhaps more than half of patients with cancer have insomnia (Table 1).3,4,8–14 It is one of the most commonly reported complaints in this group,15–17 and it occurs before, during, and after treatment of cancer.
Although the prevalence may differ in various cancers, it is still higher than in the general population. In a study of about 450 patients with cancer or depression and 300 healthy volunteers, 62% of the cancer patients reported moderate to severe sleep disturbance, compared with 52% of the depressed patients and 30% of the healthy volunteers.18
When Davidson et al3 surveyed nearly 1,000 cancer patients, one-third said they had insomnia. The problem was most prevalent in lung and breast cancer patients.
In a longitudinal study by Savard et al,13 the prevalence of insomnia declined over time but remained high even at the end of 18 months. It was more prevalent in patients with gynecologic and breast cancer than in those with prostate cancer.13,19
SLEEP PROBLEMS ARE UNDERREPORTED
Sleep problems in cancer patients often go unrecognized because patients do not report them. In a survey of 150 patients,20 44% reported having had sleep problems during the preceding month. However, only one-third of those with sleep problems told their health care providers. This highlights the need for physicians to address sleep complaints in cancer patients at every visit and, if needed, to refer them to a sleep specialist for further evaluation and management.
INSOMNIA IS OFTEN ASSOCIATED WITH OTHER PROBLEMS
Many things can interfere with sleep in cancer patients: the cancer itself (eg, pain due to tumor invasion), medical treatments (eg, narcotics, chemotherapy, neuroleptics, sympathomimetics, steroids, sedative hypnotics), psychosocial disturbances (eg, depression, anxiety, stress), and comorbid medical issues.
In this population, insomnia is often part of a cluster of symptoms that includes pain, fatigue, depression, and anxiety. These act synergistically, worsening quality of life.21–24
Cancer-related fatigue and insomnia
Cancer-related fatigue is a distressing, persistent, subjective sense of tiredness or exhaustion that is related to cancer or cancer treatment, that is not proportional to recent activity and that interferes with usual functioning.25 It has been reported by up to 90% of cancer patients in some studies.26–28
Cancer-related fatigue worsens quality of life and is one of the most distressing and persistent symptoms experienced before, during, and after cancer treatment.29,30 Furthermore, it can lead to sleep disturbances and daytime somnolence and further aggravate insomnia.31,32 The two conditions are often reported as part of a cluster of interrelated symptoms that include pain, depression, and loss of concentration and other cognitive functions, suggesting that they may share a common etiology.33–35
Åhsberg et al36 examined different aspects of perceived cancer-related fatigue in patients undergoing radiotherapy and found correlations between lack of energy, sleepiness, and cancer-related fatigue.
Current understanding of the possible link between cancer-related fatigue and insomnia suggests that interventions targeting the insomnia and daytime sleepiness could decrease the fatigue as well.31
Pain and insomnia in cancer patients
Pain is reported by 60% to 90% of patients with advanced cancer,37,38 its intensity usually varying with the extent of disease. Too often, it is inadequately controlled.39 Furthermore, it is thought to contribute to insomnia.40
In a study of more than 1,600 cancer patients, nearly 60% reported insomnia in addition to pain.41 The severity of pain directly correlated with the probability of insomnia.
Conversely, research suggests that sleep disturbances, primarily insomnia, can increase cancer patients’ sensitivity to pain.42 One hypothesis is that adequate sleep is needed to promote processes relevant to recovery from pain, both physiologic (ie, tissue repair) and psychological (ie, transient cessation of the perception of pain signals).43
Paradoxically, opioids can worsen insomnia
Cancer pain is often treated with opioids, which, paradoxically, can cause or worsen insomnia.
Although opioids induce sleep, they also depress respiration, and at night, they can cause or worsen sleep-disordered breathing (obstructive or central sleep apnea or ataxic breathing), leading to episodes of hypoxia, arousals, and fragmented sleep.44 Moreover, opioids can lead to daytime sedation. Further, psychostimulants such as methylphenidate, given to counteract opioid-induced sedation, can cause anxiety and insomnia. Thus, the interaction between cancer-related pain, insomnia, and pain management leads to a vicious cycle. Understanding this process, we can try to break the cycle and help patients with cancer sleep better.
However, how best to treat sleep-disordered breathing in patients taking opioids long-term is not well established.
In general, the primary intervention is to reduce the opioid dose. Practitioners should continually assess the need for these drugs and consider referral to a drug-behavior treatment center to help with discontinuation of opioid use when deemed medically appropriate.45 Other strategies include positive airway pressure ventilation including continuous positive airway pressure, bilevel pressure devices with backup rate, or adaptive servoventilators. In some cases oxygen supplementation may be required.
Sleep-disordered breathing, when recognized and diagnosed, should be managed in partnership with a sleep specialist.
Depression and insomnia in cancer patients
By some estimates, up to half of cancer patients suffer from depression at some point in their illness.28 And not without reason: these patients face uncertainty about their life, and this often results in depression or anxiety.46
Many cancer patients with depression also have insomnia.28 Indeed, patients with persistent insomnia are at greater risk of developing psychological disorders such as depression and anxiety.47
In a survey of cancer patients, insomnia symptoms were more often attributed to thoughts or concerns about health, family, friends, the cancer diagnosis, and finances than to the actual physical effects of cancer.48
CANCER TREATMENT AND INSOMNIA
Many cancer patients experience sleep disturbances even before starting treatment.49 Liu et al50 showed that, in 76 women about to undergo chemotherapy for breast cancer, those who already had sleep disturbances, fatigue, and depression had more problems, and more severe problems, during chemotherapy.
Radiation therapy and chemotherapy have been reported to cause or precipitate insomnia (Table 2).8,13
Hormonal therapy and biological therapy can also cause or worsen preexisting insomnia.51,52 For example, androgen deprivation therapy for prostate cancer and hormonal therapy for breast cancer are often associated with sleep problems.49,50 Possible mechanisms of insomnia include hot flashes, night sweats, and anxiety caused by such treatments. Biological agents such as interferons, interleukins, and tumor necrosis factor (TNF) alpha, which are often used to treat malignant melanoma, can affect the sleep-wake cycle, leading to insomnia.53
Corticosteroids sharply raise serum cortisol levels, which can lead to insomnia. Cancer patients receiving dexamethasone to prevent radiation-induced emesis experienced more insomnia than patients who did not receive dexamethasone.54
IMMUNOLOGIC BASIS OF INSOMNIA IN CANCER PATIENTS
Cancer cells produce inflammatory cytokines such as interleukin 1 (IL-1), interleukin 6 (IL-6), and TNF alpha, and inflammation plays a role in tumor progression and possibly tumorigenesis.55
Specific cytokines also help regulate the sleep-wake cycle. Levels of IL-6 and TNF alpha peak during sleep, and daytime IL-6 levels are inversely related to the amount of nocturnal sleep.56 Vgontzas et al57 showed that although mean levels of 24-hour IL-6 and TNF alpha secretion were not significantly different in patients with insomnia vs healthy controls, chronic insomnia was associated with a shift in IL-6 and TNF alpha secretion from nighttime to daytime.57
Cancer and its treatment can affect secretion of the cytokines that play a role in the sleep-wake cycle. Thus, the sleep disturbances associated with cancer may also be related to the abnormalities in cytokine levels caused by either cancer or its treatment.
Mills et al58 found that inflammatory markers such as vascular endothelial growth factor and soluble intercellular adhesion molecule-1 were significantly elevated during chemotherapy in breast cancer patients, and the elevated vascular endothelial growth factor levels were associated with poorer sleep during treatment.
Further research is warranted to establish causality, to help us understand the mechanisms of insomnia and other cancer symptoms, and to develop new treatments for these complaints.
POOR SLEEP AND CANCER RISK AND OUTCOMES
Sleep disturbances have negative health consequences in cancer. Their impact ranges from plausible carcinogenesis to affecting the course of the disease and cancer survival.
Poor sleep and risk of cancer
Epidemiologic studies have examined a possible link between circadian rhythm disruption and breast cancer risk, using both direct measures such as melatonin levels and indirect measures such as sleep duration and shift work. (Melatonin production is related to sleep duration, and night-shift work leads to disruption of sleep pattern and quality of sleep, thus lowering melatonin levels.59)
The findings were mixed. Breast cancer risk was significantly and inversely associated with urinary melatonin levels (6-sulfatoxymelatonin) in the Nurses’ Health Study II,60 but not in the Guernsey III study in the United Kingdom.61 Breast cancer risk was significantly lower with longer sleep duration in Finnish women62 and in Chinese women in Singapore,63 but not in American women.64,65 Results of three cohort studies66–68 and two case-control studies69,70 suggested a higher breast cancer risk in women who work evening or overnight shifts. Shorter sleep duration was associated with a higher risk of colorectal adenomas.71
These studies make a strong case for an association of cancer with circadian rhythm disruption and shorter sleep duration, possibly from an effect on melatonin levels. However, one should be cautious in interpreting epidemiologic studies: although they show sleep disturbances to be associated with cancer risk, they do not establish causality.
Insomnia and cancer outcomes
Evidence is growing that sleep disturbances may affect compliance with treatment, immune function, and outcomes—including survival—in cancer patients.23,24
In patients newly diagnosed with various types of cancer, Degner and Sloan72 showed that those who suffered from insomnia, nausea, poor appetite, and pain had a lower survival rate at 5 years, independent of the cancer stage. However, no separate analyses were performed to examine the specific influence of insomnia on cancer survival.
Thompson and Li73 analyzed data from 101 breast cancer patients with available Oncotype DX recurrence scores (a proprietary genetic test performed on tumor tissue that predicts the likelihood of recurrence). The scores were strongly correlated with average hours of sleep per night before breast cancer diagnosis, with fewer hours of sleep associated with a higher (worse) score.
Since these studies were retrospective and merely suggest associations, prospective studies, using more standardized questionnaires and objective measures, are needed to establish causality and to further our understanding of the mechanisms involved.
HELPING CANCER PATIENTS SLEEP BETTER
Insomnia is generally diagnosed with a thorough history that includes sleep, medical issues, substance use, and psychiatric issues. The sleep history should include specific insomniarelated complaints, presleep conditions and habits, sleep-wake habits, other sleep-related symptoms, and daytime consequences. To obtain the information, one can use questionnaires, sleep logs, psychological screening tests, and bed-partner interviews.74
Managing insomnia involves both pharmacologic and nonpharmacologic treatment. It is also important to treat the associated disorders such as depression and anxiety disorders that often accompany insomnia. Long-term management of cancer patients should not be limited to surveillance of cancer but should also involve aggressive treatment of clusters of symptoms such as insomnia, cancer-related fatigue, and pain to yield better long-term quality of life.75–77
Nonpharmacologic treatment: Cognitive-behavioral therapy
Nonpharmacologic interventions use psychological and behavioral therapies. The American Academy of Sleep Medicine guidelines recommend cognitive behavioral therapy for all patients with insomnia, either alone or in combination with hypnotic medications.
Cognitive-behavioral therapy for insomnia includes various components that help the patient learn coping skills and ways to prevent or mitigate the severity of future episodes (Table 3). Various randomized controlled trials found it to be effective for treating insomnia in the general population.77–79
Several studies found that cognitive-behavioral therapy for insomnia was effective in cancer patients, not only improving sleep quality but also decreasing psychological distress, resulting in better overall quality of life.80,81
Savard et al81 conducted a randomized controlled trial of cognitive-behavioral therapy for insomnia in 57 patients with breast cancer, examining subjective and objective sleep measures, psychological functioning, quality of life, and immunologic responses. They found significant improvements in sleep efficiency, mood, quality of life, depression, anxiety, and need for sleep medications. Improvements in subjective sleep measures persisted on 12-month follow-up.
Berger et al,82 in another randomized controlled trial, assessed behavioral therapy using stimulus control, modified sleep restriction, relaxation therapy, and sleep hygiene in breast cancer patients receiving adjuvant chemotherapy. Behavioral therapy improved sleep quality over time, as measured by the Pittsburgh Sleep Quality Index.
Espie et al83 evaluated the effect of cognitive-behavioral therapy on prostate, colorectal, gynecologic, and breast cancer patients, with similar results.83
Cognitive-behavioral therapy is at least as effective as drug therapy for insomnia in the general population. In the limited studies done in cancer patients, it has been shown to be effective irrespective of the type of cancer and is associated with better long-term outcomes. It diminishes the distress associated with early insomnia, can reduce anxiety, and can promote sleep.
A National Institutes of Health conference on insomnia concluded that cognitivebehavioral therapy is at least as effective as medications for brief treatment of chronic insomnia and that its beneficial effects, in contrast to those produced by medications, may last beyond the termination of treatment.84
It is important to think about numerous factors when considering options such as cognitive-behavioral therapy, as patients with cancer have different complications that may affect sleep quality, such as cancer-related fatigue, cancer-related depression, psychological reactions to the disease, side effects of treatment, and cancer-related pain. These need to be addressed as well.
If cognitive-behavioral therapy is not available, self-help interventions (eg, written material, videos, television and Internet resources) can be used. These have several advantages over professionally administered interventions, including greater accessibility, less burden for the patient, and lower cost. Research is under way evaluating this approach in cancer patients.85
Drug therapy
The focus of therapy should be to treat underlying disorders that may be causing or contributing to insomnia. However, a substantial number of patients may need to be assessed for pharmacotherapy for insomnia.
Sleep problems in the general population are commonly treated with drugs, and most of the recommendations in cancer patients are based on experience in the general population. However, sleep medications should be used cautiously in cancer patients, since to our knowledge there have been no studies of these agents in patients with cancer.
Side effects also need to be considered. For example, sleep medications can profoundly worsen cancer-related fatigue.
Hypnotics are often prescribed for cancer patients.86,87 A study in five major oncology centers showed that about half of the 1,500 patients were prescribed at least one psychotropic drug.86 In this study, hypnotics were the most frequently prescribed drugs, accounting for 48% of total prescriptions, and 44% of the psychotropic prescriptions were written for sleep.
Benzodiazepine receptor agonists such as zaleplon, zolpidem, and eszopiclone can be used for problems with falling asleep and staying asleep.88,89 They are better tolerated than older, long-acting benzodiazepines,90 which can cause alterations in sleep-cycle architecture or rebound insomnia. The earlier agents can also cause adverse effects such as tolerance, drowsiness, and cognitive impairment.
A National Institutes of Health conference stated that benzodiazepine receptor agonists are efficacious in the short-term management of insomnia and that their adverse effects are much less frequent and severe than those of the benzodiazepines or other sedating drugs.84 It also stated that all antidepressants, antihistamines (H1 receptor antagonists), and anti-psychotics have potentially significant adverse effects that raise concerns about their risk-to-benefit ratio and their suitability as treatment for chronic insomnia.
Benzodiazepines are commonly prescribed for insomnia. They increase sleep efficiency, decrease arousals, and increase stage 2 sleep.
Melatonin receptor agonists have been approved by the US Food and Drug Administration for treating insomnia. A recent meta-analysis of eight studies in healthy patients showed improvements in subjective and objective sleep outcomes with the use of ramelteon.91 The dosages primarily used were 4 to 32 mg. However, most of the studies used a dosage of 4 to 8 mg.
Antidepressants. Some of the antidepressants are also used for insomnia, but they can cause daytime fatigue.
Mirtazapine was shown to be effective for insomnia and coexistent mood disorder in cancer patients, but larger trials are needed.92
A recent clinical trial with secondary data analyses evaluated the effect of paroxetine on insomnia, depression, and fatigue in patients with cancer. Paroxetine significantly reduced insomnia in both depressed and nondepressed patients after 2 to 3 weeks of treatment.93
Table 4 summarizes classes of drugs used for insomnia and their additional therapeutic properties.
Many cancer patients don't sleep well, for a variety of reasons. It is an important problem: not only does poor sleep worsen quality of life, it may affect prognosis. Moreover, treatment is available.
Yet many physicians caring for cancer patients do not ask about sleep problems, underestimating their impact or focusing on more urgent problems. Also, patients may not want to bring up the topic because they consider poor sleep to be unavoidable and untreatable and because they fear that reporting it may shift the focus of their treatment from trying to cure the cancer to easing its symptoms.
This practical review will help health care professionals avoid the common barriers to diagnosis and treatment of poor sleep in cancer patients. Because there are few data on other sleep disorders such as sleep apnea and restless leg syndrome, we will focus on the most common one in cancer patients—insomnia—and its effects on other symptoms and quality of life.
MORE PATIENTS SURVIVE CANCER NOW
Today, more patients are surviving cancer, but cancer symptoms and the side effects of surgery, chemotherapy, and radiation therapy may persist for years.1,2 The most common complaints include cancer-related fatigue, leg restlessness, anxiety, insomnia, and excessive sleepiness.3
Sleep disturbances appear to contribute to the other problems and are relatively easier to quantify. Most studies of sleep disorders in cancer patients have looked specifically at insomnia,4 although a few have explored the prevalence of other sleep disorders, such as sleep-disordered breathing and limb movements during sleep.5
The International Classification of Sleep Disorders, 2nd edition,6 defines insomnia as difficulty going to sleep or staying asleep (the latter defined as waking up in the middle of the night, with wakeful episodes lasting more than 30 minutes), early-morning awakenings (waking 30 minutes or more before the intended time), or nonrestorative sleep, causing significant distress or impairment of day-time functioning.
INSOMNIA WORSENS QUALITY OF LIFE
Insomnia significantly worsens quality of life in cancer patients, and if it can be detected and effectively treated, quality of life is likely to improve. Studies in cancer patients have found that those with insomnia:
- Were less able to cope with stress and carry on their activities of daily living3
- Were much less able to function and reported more pain, less energy, and greater difficulty in dealing with emotional problems7
- Had poor quality of life, both physically and emotionally.3,8
PERHAPS MORE THAN HALF OF CANCER PATIENTS HAVE INSOMNIA
Depending on the methods used and populations studied, at least 30% and perhaps more than half of patients with cancer have insomnia (Table 1).3,4,8–14 It is one of the most commonly reported complaints in this group,15–17 and it occurs before, during, and after treatment of cancer.
Although the prevalence may differ in various cancers, it is still higher than in the general population. In a study of about 450 patients with cancer or depression and 300 healthy volunteers, 62% of the cancer patients reported moderate to severe sleep disturbance, compared with 52% of the depressed patients and 30% of the healthy volunteers.18
When Davidson et al3 surveyed nearly 1,000 cancer patients, one-third said they had insomnia. The problem was most prevalent in lung and breast cancer patients.
In a longitudinal study by Savard et al,13 the prevalence of insomnia declined over time but remained high even at the end of 18 months. It was more prevalent in patients with gynecologic and breast cancer than in those with prostate cancer.13,19
SLEEP PROBLEMS ARE UNDERREPORTED
Sleep problems in cancer patients often go unrecognized because patients do not report them. In a survey of 150 patients,20 44% reported having had sleep problems during the preceding month. However, only one-third of those with sleep problems told their health care providers. This highlights the need for physicians to address sleep complaints in cancer patients at every visit and, if needed, to refer them to a sleep specialist for further evaluation and management.
INSOMNIA IS OFTEN ASSOCIATED WITH OTHER PROBLEMS
Many things can interfere with sleep in cancer patients: the cancer itself (eg, pain due to tumor invasion), medical treatments (eg, narcotics, chemotherapy, neuroleptics, sympathomimetics, steroids, sedative hypnotics), psychosocial disturbances (eg, depression, anxiety, stress), and comorbid medical issues.
In this population, insomnia is often part of a cluster of symptoms that includes pain, fatigue, depression, and anxiety. These act synergistically, worsening quality of life.21–24
Cancer-related fatigue and insomnia
Cancer-related fatigue is a distressing, persistent, subjective sense of tiredness or exhaustion that is related to cancer or cancer treatment, that is not proportional to recent activity and that interferes with usual functioning.25 It has been reported by up to 90% of cancer patients in some studies.26–28
Cancer-related fatigue worsens quality of life and is one of the most distressing and persistent symptoms experienced before, during, and after cancer treatment.29,30 Furthermore, it can lead to sleep disturbances and daytime somnolence and further aggravate insomnia.31,32 The two conditions are often reported as part of a cluster of interrelated symptoms that include pain, depression, and loss of concentration and other cognitive functions, suggesting that they may share a common etiology.33–35
Åhsberg et al36 examined different aspects of perceived cancer-related fatigue in patients undergoing radiotherapy and found correlations between lack of energy, sleepiness, and cancer-related fatigue.
Current understanding of the possible link between cancer-related fatigue and insomnia suggests that interventions targeting the insomnia and daytime sleepiness could decrease the fatigue as well.31
Pain and insomnia in cancer patients
Pain is reported by 60% to 90% of patients with advanced cancer,37,38 its intensity usually varying with the extent of disease. Too often, it is inadequately controlled.39 Furthermore, it is thought to contribute to insomnia.40
In a study of more than 1,600 cancer patients, nearly 60% reported insomnia in addition to pain.41 The severity of pain directly correlated with the probability of insomnia.
Conversely, research suggests that sleep disturbances, primarily insomnia, can increase cancer patients’ sensitivity to pain.42 One hypothesis is that adequate sleep is needed to promote processes relevant to recovery from pain, both physiologic (ie, tissue repair) and psychological (ie, transient cessation of the perception of pain signals).43
Paradoxically, opioids can worsen insomnia
Cancer pain is often treated with opioids, which, paradoxically, can cause or worsen insomnia.
Although opioids induce sleep, they also depress respiration, and at night, they can cause or worsen sleep-disordered breathing (obstructive or central sleep apnea or ataxic breathing), leading to episodes of hypoxia, arousals, and fragmented sleep.44 Moreover, opioids can lead to daytime sedation. Further, psychostimulants such as methylphenidate, given to counteract opioid-induced sedation, can cause anxiety and insomnia. Thus, the interaction between cancer-related pain, insomnia, and pain management leads to a vicious cycle. Understanding this process, we can try to break the cycle and help patients with cancer sleep better.
However, how best to treat sleep-disordered breathing in patients taking opioids long-term is not well established.
In general, the primary intervention is to reduce the opioid dose. Practitioners should continually assess the need for these drugs and consider referral to a drug-behavior treatment center to help with discontinuation of opioid use when deemed medically appropriate.45 Other strategies include positive airway pressure ventilation including continuous positive airway pressure, bilevel pressure devices with backup rate, or adaptive servoventilators. In some cases oxygen supplementation may be required.
Sleep-disordered breathing, when recognized and diagnosed, should be managed in partnership with a sleep specialist.
Depression and insomnia in cancer patients
By some estimates, up to half of cancer patients suffer from depression at some point in their illness.28 And not without reason: these patients face uncertainty about their life, and this often results in depression or anxiety.46
Many cancer patients with depression also have insomnia.28 Indeed, patients with persistent insomnia are at greater risk of developing psychological disorders such as depression and anxiety.47
In a survey of cancer patients, insomnia symptoms were more often attributed to thoughts or concerns about health, family, friends, the cancer diagnosis, and finances than to the actual physical effects of cancer.48
CANCER TREATMENT AND INSOMNIA
Many cancer patients experience sleep disturbances even before starting treatment.49 Liu et al50 showed that, in 76 women about to undergo chemotherapy for breast cancer, those who already had sleep disturbances, fatigue, and depression had more problems, and more severe problems, during chemotherapy.
Radiation therapy and chemotherapy have been reported to cause or precipitate insomnia (Table 2).8,13
Hormonal therapy and biological therapy can also cause or worsen preexisting insomnia.51,52 For example, androgen deprivation therapy for prostate cancer and hormonal therapy for breast cancer are often associated with sleep problems.49,50 Possible mechanisms of insomnia include hot flashes, night sweats, and anxiety caused by such treatments. Biological agents such as interferons, interleukins, and tumor necrosis factor (TNF) alpha, which are often used to treat malignant melanoma, can affect the sleep-wake cycle, leading to insomnia.53
Corticosteroids sharply raise serum cortisol levels, which can lead to insomnia. Cancer patients receiving dexamethasone to prevent radiation-induced emesis experienced more insomnia than patients who did not receive dexamethasone.54
IMMUNOLOGIC BASIS OF INSOMNIA IN CANCER PATIENTS
Cancer cells produce inflammatory cytokines such as interleukin 1 (IL-1), interleukin 6 (IL-6), and TNF alpha, and inflammation plays a role in tumor progression and possibly tumorigenesis.55
Specific cytokines also help regulate the sleep-wake cycle. Levels of IL-6 and TNF alpha peak during sleep, and daytime IL-6 levels are inversely related to the amount of nocturnal sleep.56 Vgontzas et al57 showed that although mean levels of 24-hour IL-6 and TNF alpha secretion were not significantly different in patients with insomnia vs healthy controls, chronic insomnia was associated with a shift in IL-6 and TNF alpha secretion from nighttime to daytime.57
Cancer and its treatment can affect secretion of the cytokines that play a role in the sleep-wake cycle. Thus, the sleep disturbances associated with cancer may also be related to the abnormalities in cytokine levels caused by either cancer or its treatment.
Mills et al58 found that inflammatory markers such as vascular endothelial growth factor and soluble intercellular adhesion molecule-1 were significantly elevated during chemotherapy in breast cancer patients, and the elevated vascular endothelial growth factor levels were associated with poorer sleep during treatment.
Further research is warranted to establish causality, to help us understand the mechanisms of insomnia and other cancer symptoms, and to develop new treatments for these complaints.
POOR SLEEP AND CANCER RISK AND OUTCOMES
Sleep disturbances have negative health consequences in cancer. Their impact ranges from plausible carcinogenesis to affecting the course of the disease and cancer survival.
Poor sleep and risk of cancer
Epidemiologic studies have examined a possible link between circadian rhythm disruption and breast cancer risk, using both direct measures such as melatonin levels and indirect measures such as sleep duration and shift work. (Melatonin production is related to sleep duration, and night-shift work leads to disruption of sleep pattern and quality of sleep, thus lowering melatonin levels.59)
The findings were mixed. Breast cancer risk was significantly and inversely associated with urinary melatonin levels (6-sulfatoxymelatonin) in the Nurses’ Health Study II,60 but not in the Guernsey III study in the United Kingdom.61 Breast cancer risk was significantly lower with longer sleep duration in Finnish women62 and in Chinese women in Singapore,63 but not in American women.64,65 Results of three cohort studies66–68 and two case-control studies69,70 suggested a higher breast cancer risk in women who work evening or overnight shifts. Shorter sleep duration was associated with a higher risk of colorectal adenomas.71
These studies make a strong case for an association of cancer with circadian rhythm disruption and shorter sleep duration, possibly from an effect on melatonin levels. However, one should be cautious in interpreting epidemiologic studies: although they show sleep disturbances to be associated with cancer risk, they do not establish causality.
Insomnia and cancer outcomes
Evidence is growing that sleep disturbances may affect compliance with treatment, immune function, and outcomes—including survival—in cancer patients.23,24
In patients newly diagnosed with various types of cancer, Degner and Sloan72 showed that those who suffered from insomnia, nausea, poor appetite, and pain had a lower survival rate at 5 years, independent of the cancer stage. However, no separate analyses were performed to examine the specific influence of insomnia on cancer survival.
Thompson and Li73 analyzed data from 101 breast cancer patients with available Oncotype DX recurrence scores (a proprietary genetic test performed on tumor tissue that predicts the likelihood of recurrence). The scores were strongly correlated with average hours of sleep per night before breast cancer diagnosis, with fewer hours of sleep associated with a higher (worse) score.
Since these studies were retrospective and merely suggest associations, prospective studies, using more standardized questionnaires and objective measures, are needed to establish causality and to further our understanding of the mechanisms involved.
HELPING CANCER PATIENTS SLEEP BETTER
Insomnia is generally diagnosed with a thorough history that includes sleep, medical issues, substance use, and psychiatric issues. The sleep history should include specific insomniarelated complaints, presleep conditions and habits, sleep-wake habits, other sleep-related symptoms, and daytime consequences. To obtain the information, one can use questionnaires, sleep logs, psychological screening tests, and bed-partner interviews.74
Managing insomnia involves both pharmacologic and nonpharmacologic treatment. It is also important to treat the associated disorders such as depression and anxiety disorders that often accompany insomnia. Long-term management of cancer patients should not be limited to surveillance of cancer but should also involve aggressive treatment of clusters of symptoms such as insomnia, cancer-related fatigue, and pain to yield better long-term quality of life.75–77
Nonpharmacologic treatment: Cognitive-behavioral therapy
Nonpharmacologic interventions use psychological and behavioral therapies. The American Academy of Sleep Medicine guidelines recommend cognitive behavioral therapy for all patients with insomnia, either alone or in combination with hypnotic medications.
Cognitive-behavioral therapy for insomnia includes various components that help the patient learn coping skills and ways to prevent or mitigate the severity of future episodes (Table 3). Various randomized controlled trials found it to be effective for treating insomnia in the general population.77–79
Several studies found that cognitive-behavioral therapy for insomnia was effective in cancer patients, not only improving sleep quality but also decreasing psychological distress, resulting in better overall quality of life.80,81
Savard et al81 conducted a randomized controlled trial of cognitive-behavioral therapy for insomnia in 57 patients with breast cancer, examining subjective and objective sleep measures, psychological functioning, quality of life, and immunologic responses. They found significant improvements in sleep efficiency, mood, quality of life, depression, anxiety, and need for sleep medications. Improvements in subjective sleep measures persisted on 12-month follow-up.
Berger et al,82 in another randomized controlled trial, assessed behavioral therapy using stimulus control, modified sleep restriction, relaxation therapy, and sleep hygiene in breast cancer patients receiving adjuvant chemotherapy. Behavioral therapy improved sleep quality over time, as measured by the Pittsburgh Sleep Quality Index.
Espie et al83 evaluated the effect of cognitive-behavioral therapy on prostate, colorectal, gynecologic, and breast cancer patients, with similar results.83
Cognitive-behavioral therapy is at least as effective as drug therapy for insomnia in the general population. In the limited studies done in cancer patients, it has been shown to be effective irrespective of the type of cancer and is associated with better long-term outcomes. It diminishes the distress associated with early insomnia, can reduce anxiety, and can promote sleep.
A National Institutes of Health conference on insomnia concluded that cognitivebehavioral therapy is at least as effective as medications for brief treatment of chronic insomnia and that its beneficial effects, in contrast to those produced by medications, may last beyond the termination of treatment.84
It is important to think about numerous factors when considering options such as cognitive-behavioral therapy, as patients with cancer have different complications that may affect sleep quality, such as cancer-related fatigue, cancer-related depression, psychological reactions to the disease, side effects of treatment, and cancer-related pain. These need to be addressed as well.
If cognitive-behavioral therapy is not available, self-help interventions (eg, written material, videos, television and Internet resources) can be used. These have several advantages over professionally administered interventions, including greater accessibility, less burden for the patient, and lower cost. Research is under way evaluating this approach in cancer patients.85
Drug therapy
The focus of therapy should be to treat underlying disorders that may be causing or contributing to insomnia. However, a substantial number of patients may need to be assessed for pharmacotherapy for insomnia.
Sleep problems in the general population are commonly treated with drugs, and most of the recommendations in cancer patients are based on experience in the general population. However, sleep medications should be used cautiously in cancer patients, since to our knowledge there have been no studies of these agents in patients with cancer.
Side effects also need to be considered. For example, sleep medications can profoundly worsen cancer-related fatigue.
Hypnotics are often prescribed for cancer patients.86,87 A study in five major oncology centers showed that about half of the 1,500 patients were prescribed at least one psychotropic drug.86 In this study, hypnotics were the most frequently prescribed drugs, accounting for 48% of total prescriptions, and 44% of the psychotropic prescriptions were written for sleep.
Benzodiazepine receptor agonists such as zaleplon, zolpidem, and eszopiclone can be used for problems with falling asleep and staying asleep.88,89 They are better tolerated than older, long-acting benzodiazepines,90 which can cause alterations in sleep-cycle architecture or rebound insomnia. The earlier agents can also cause adverse effects such as tolerance, drowsiness, and cognitive impairment.
A National Institutes of Health conference stated that benzodiazepine receptor agonists are efficacious in the short-term management of insomnia and that their adverse effects are much less frequent and severe than those of the benzodiazepines or other sedating drugs.84 It also stated that all antidepressants, antihistamines (H1 receptor antagonists), and anti-psychotics have potentially significant adverse effects that raise concerns about their risk-to-benefit ratio and their suitability as treatment for chronic insomnia.
Benzodiazepines are commonly prescribed for insomnia. They increase sleep efficiency, decrease arousals, and increase stage 2 sleep.
Melatonin receptor agonists have been approved by the US Food and Drug Administration for treating insomnia. A recent meta-analysis of eight studies in healthy patients showed improvements in subjective and objective sleep outcomes with the use of ramelteon.91 The dosages primarily used were 4 to 32 mg. However, most of the studies used a dosage of 4 to 8 mg.
Antidepressants. Some of the antidepressants are also used for insomnia, but they can cause daytime fatigue.
Mirtazapine was shown to be effective for insomnia and coexistent mood disorder in cancer patients, but larger trials are needed.92
A recent clinical trial with secondary data analyses evaluated the effect of paroxetine on insomnia, depression, and fatigue in patients with cancer. Paroxetine significantly reduced insomnia in both depressed and nondepressed patients after 2 to 3 weeks of treatment.93
Table 4 summarizes classes of drugs used for insomnia and their additional therapeutic properties.
- Ness KK, Wall MM, Oakes JM, Robison LL, Gurney JG. Physical performance limitations and participation restrictions among cancer survivors: a population-based study. Ann Epidemiol 2006; 16:197–205.
- Deimling GT, Bowman KF, Sterns S, Wagner LJ, Kahana B. Cancer-related health worries and psychological distress among older adult, long-term cancer survivors. Psychooncology 2006; 15:306–320.
- Davidson JR, MacLean AW, Brundage MD, Schulze K. Sleep disturbance in cancer patients. Soc Sci Med 2002; 54:1309–1321.
- Savard J, Morin CM. Insomnia in the context of cancer: a review of a neglected problem. J Clin Oncol 2001; 19:895–908.
- Payne RJ, Hier MP, Kost KM, et al. High prevalence of obstructive sleep apnea among patients with head and neck cancer. J Otolaryngol 2005; 34:304–311.
- American Academy of Sleep Medicine. International Classification of Sleep Disorders—Second Edition (ICSD-2); 2005.
- Fortner BV, Stepanski EJ, Wang SC, Kasprowicz S, Durrence HH. Sleep and quality of life in breast cancer patients. J Pain Symptom Manage 2002; 24:471–480.
- Chen ML, Yu CT, Yang CH. Sleep disturbances and quality of life in lung cancer patients undergoing chemotherapy. Lung Cancer 2008; 62:391–400.
- Liu L, Ancoli-Israel S. Sleep disturbances in cancer. Psychiatr Ann 2008; 38:627–634.
- Ancoli-Israel S, Liu L, Marler MR, et al. Fatigue, sleep, and circadian rhythms prior to chemotherapy for breast cancer. Support Care Cancer 2006; 14:201–209.
- Miaskowski C, Lee K, Dunn L, et al. Sleep-wake circadian activity rhythm parameters and fatigue in oncology patients before the initiation of radiation therapy. Cancer Nurs 2011; 34:255–268.
- Liu L, Rissling M, Natarajan L, et al. The longitudinal relationship between fatigue and sleep in breast cancer patients undergoing chemotherapy. Sleep 2012; 35:237–245.
- Savard J, Ivers H, Villa J, Caplette-Gingras A, Morin CM. Natural course of insomnia comorbid with cancer: an 18-month longitudinal study. J Clin Oncol 2011; 29:3580–3586.
- Sela RA, Watanabe S, Nekolaichuk CL. Sleep disturbances in palliative cancer patients attending a pain and symptom control clinic. Palliat Support Care 2005; 3:23–31.
- Mao JJ, Armstrong K, Bowman MA, Xie SX, Kadakia R, Farrar JT. Symptom burden among cancer survivors: impact of age and comorbidity. J Am Board Fam Med 2007; 20:434–443.
- Schroevers MJ, Ranchor AV, Sanderman R. The role of age at the onset of cancer in relation to survivors’ long-term adjustment: a controlled comparison over an eight-year period. Psychooncology 2004; 13:740–752.
- Stein KD, Syrjala KL, Andrykowski MA. Physical and psychological long-term and late effects of cancer. Cancer 2008; 112(suppl 11):2577–2592.
- Anderson KO, Getto CJ, Mendoza TR, et al. Fatigue and sleep disturbance in patients with cancer, patients with clinical depression, and community-dwelling adults. J Pain Symptom Manage 2003; 25:307–318.
- Savard J, Villa J, Ivers H, Simard S, Morin CM. Prevalence, natural course, and risk factors of insomnia comorbid with cancer over a 2-month period. J Clin Oncol 2009; 27:5233–5239.
- Engstrom CA, Strohl RA, Rose L, Lewandowski L, Stefanek ME. Sleep alterations in cancer patients. Cancer Nurs 1999; 22:143–148.
- Hoffman A, Given BA, von Eye A, Given CW, Gift AG. A study on the relationship between fatigue, pain, insomnia, and gender in persons with lung cancer. Oncol Nurs Forum 2006; 33:404.
- Hoffman AJ, Given BA, von Eye A, Gift AG, Given CW. Relationships among pain, fatigue, insomnia, and gender in persons with lung cancer. Oncol Nurs Forum 2007; 34:785–792.
- Shapiro SL, Bootzin RR, Figueredo AJ, Lopez AM, Schwartz GE. The efficacy of mindfulness-based stress reduction in the treatment of disturbance in women with breast cancer: an exploratory study. J Psychosom Res 2003; 54:85–91.
- Shapiro SL, Lopez AM, Schwartz GE, et al. Quality of life and breast cancer: relationship to psychosocial variables. J Clin Psychol 2001; 57:501–519.
- Mock V, Atkinson A, Barsevick A, et al; National Comprehensive Cancer Network. NCCN practice guidelines for cancer-related fatigue. Oncology (Williston Park) 2000; 14:151–161.
- Cella D, Davis K, Breitbart W, Curt G;Fatigue Coalition. Cancer-related fatigue: prevalence of proposed diagnostic criteria in a United States sample of cancer survivors. J Clin Oncol 2001; 19:3385–3391.
- Sateia MJ, Lang BJ. Sleep and cancer: recent developments. Curr Oncol Rep 2008; 10:309–318.
- Ahluwalia M. Fatigue, pain, and depression among older adults with cancer: still underrecognized and undertreated. Geriatrics and Aging 2008; 11:495–501.
- Enderlin CA, Coleman EA, Cole C, Richards KC, Hutchins LF, Sherman AC. Sleep across chemotherapy treatment: a growing concern for women older than 50 with breast cancer. Oncol Nurs Forum 2010; 37:461–A3.
- Winningham ML, Nail LM, Burke MB, et al. Fatigue and the cancer experience: the state of the knowledge. Oncol Nurs Forum 1994; 21:23–36.
- Berger AM, Mitchell SA. Modifying cancer-related fatigue by optimizing sleep quality. J Natl Compr Canc Netw 2008; 6:3–13.
- Anderson KO, Getto CJ, Mendoza TR, et al. Fatigue and sleep disturbance in patients with cancer, patients with clinical depression, and community-dwelling adults. J Pain Symptom Manage 2003; 25:307–318.
- Armstrong TS, Cohen MZ, Eriksen LR, Hickey JV. Symptom clusters in oncology patients and implications for symptom research in people with primary brain tumors. J Nurs Scholarsh 2004; 36:197–206.
- Dodd MJ, Miaskowski C, Lee KA. Occurrence of symptom clusters. J Natl Cancer Inst Monogr 2004;76–78.
- Paice JA. Assessment of symptom clusters in people with cancer. J Natl Cancer Inst Monogr 2004;98–102.
- Åhsberg E, Fürst CJ. Dimensions of fatigue during radiotherapy—an application of the Swedish Occupational Fatigue Inventory (SOFI) on cancer patients. Acta Oncol 2001; 40:37–43.
- Foley KM. The treatment of cancer pain. N Engl J Med 1985; 313:84–95.
- Twycross RG, Fairfield S. Pain in far-advanced cancer. Pain 1982; 14:303–310.
- Cleeland CS, Gonin R, Hatfield AK, et al. Pain and its treatment in outpatients with metastatic cancer. N Engl J Med 1994; 330:592–596.
- Fleming L, Gillespie S, Espie CA. The development and impact of insomnia on cancer survivors: Psychooncology 2010; 19:991–996.
- Grond S, Zech D, Diefenbach C, Bischoff A. Prevalence and pattern of symptoms in patients with cancer pain: a prospective evaluation of 1635 cancer patients referred to a pain clinic. J Pain Symptom Manage 1994; 9:372–382.
- Smith MT, Haythornthwaite JA. How do sleep disturbance and chronic pain inter-relate? Insights from the longitudinal and cognitive-behavioral clinical trials literature. Sleep Med Rev 2004; 8:119–132.
- Lewin DS, Dahl RE. Importance of sleep in the management of pediatric pain. J Dev Behav Pediatr 1999; 20:244–252.
- Yue HJ, Guilleminault C. Opioid medication and sleep-disordered breathing. Med Clin North Am 2010; 94:435–446.
- Teichtahl H, Wang D. Sleep-disordered breathing with chronic opioid use. Expert Opin Drug Saf 2007; 6:641–649.
- Ancoli-Israel S, Moore PJ, Jones V. The relationship between fatigue and sleep in cancer patients: a review. Eur J Cancer Care (Engl) 2001; 10:245–255.
- Perlis ML, Giles DE, Buysse DJ, Tu X, Kupfer DJ. Self-reported sleep disturbance as a prodromal symptom in recurrent depression. J Affect Disord 1997; 42:209–212.
- Stone P, Hardy J, Broadley K, Tookman AJ, Kurowska A, A’Hern R. Fatigue in advanced cancer: a prospective controlled cross-sectional study. Br J Cancer 1999; 79:1479–1486.
- Cimprich B. Pretreatment symptom distress in women newly diagnosed with breast cancer. Cancer Nurs 1999; 22:185–194.
- Liu L, Fiorentino L, Natarajan L, et al. Pre-treatment symptom cluster in breast cancer patients is associated with worse sleep, fatigue and depression during chemotherapy. Psychooncology 2009; 18:187–194.
- Savard J, Hervouet S, Ivers H. Prostate cancer treatments and their side effects are associated with increased insomnia. Psychooncology 2013; 22:1381–1388.
- Fenlon DR, Corner JL, Haviland J. Menopausal hot flushes after breast cancer. Eur J Cancer Care (Engl) 2009; 18:140–148.
- Miller AH, Ancoli-Israel S, Bower JE, Capuron L, Irwin MR. Neuroendocrine-immune mechanisms of behavioral comorbidities in patients with cancer. J Clin Oncol 2008; 26:971–982.
- Kirkbride P, Bezjak A, Pater J, et al. Dexamethasone for the prophylaxis of radiation-induced emesis: a National Cancer Institute of Canada Clinical Trials Group phase III study. J Clin Oncol 2000; 18:1960–1966.
- Coussens LM, Werb Z. Inflammation and cancer. Nature 2002; 420:860–867.
- Vgontzas AN, Chrousos GP. Sleep, the hypothalamic-pituitary-adrenal axis, and cytokines: multiple interactions and disturbances in sleep disorders. Endocrinol Metab Clin North Am 2002; 31:15–36.
- Vgontzas AN, Zoumakis M, Papanicolaou DA, et al. Chronic insomnia is associated with a shift of interleukin-6 and tumor necrosis factor secretion from nighttime to daytime. Metabolism 2002; 51:887–892.
- Mills PJ, Parker B, Jones V, et al. The effects of standard anthracycline-based chemotherapy on soluble ICAM-1 and vascular endothelial growth factor levels in breast cancer. Clin Cancer Res 2004; 10:4998–5003.
- Reiter RJ, Tan DX, Korkmaz A, et al. Light at night, chronodisruption, melatonin suppression, and cancer risk: a review. Crit Rev Oncog 2007; 13:303–328.
- Schernhammer ES, Hankinson SE. Urinary melatonin levels and breast cancer risk. J Natl Cancer Inst 2005; 97:1084–1087.
- Travis RC, Allen DS, Fentiman IS, Key TJ. Melatonin and breast cancer: a prospective study. J Natl Cancer Inst 2004; 96:475–482.
- Verkasalo PK, Lillberg K, Stevens RG, et al. Sleep duration and breast cancer: a prospective cohort study. Cancer Res 2005; 65:9595–9600.
- Wu AH, Wang R, Koh WP, Stanczyk FZ, Lee HP, Yu MC. Sleep duration, melatonin and breast cancer among Chinese women in Singapore. Carcinogenesis 2008; 29:1244–1248.
- McElroy JA, Newcomb PA, Titus-Ernstoff L, Trentham-Dietz A, Hampton JM, Egan KM. Duration of sleep and breast cancer risk in a large population-based case-control study. J Sleep Res 2006; 15:241–249.
- Pinheiro SP, Schernhammer ES, Tworoger SS, Michels KB. A prospective study on habitual duration of sleep and incidence of breast cancer in a large cohort of women. Cancer Res 2006; 66:5521–5525.
- Lie JA, Roessink J, Kjaerheim K. Breast cancer and night work among Norwegian nurses. Cancer Causes Control 2006; 17:39–44.
- Schernhammer ES, Kroenke CH, Laden F, Hankinson SE. Night work and risk of breast cancer. Epidemiology 2006; 17:108–111.
- Schernhammer ES, Laden F, Speizer FE, et al. Rotating night shifts and risk of breast cancer in women participating in the Nurses’ Health Study. J Natl Cancer Inst 2001; 93:1563–1568.
- Davis S, Mirick DK, Stevens RG. Night shift work, light at night, and risk of breast cancer. J Natl Cancer Inst 2001; 93:1557–1562.
- Hansen J. Light at night, shiftwork, and breast cancer risk. J Natl Cancer Inst 2001; 93:1513–1515.
- Thompson CL, Larkin EK, Patel S, Berger NA, Redline S, Li L. Short duration of sleep increases risk of colorectal adenoma. Cancer 2011; 117:841–847.
- Degner LF, Sloan JA. Symptom distress in newly diagnosed ambulatory cancer patients and as a predictor of survival in lung cancer. J Pain Symptom Manage 1995; 10:423–431.
- Thompson CL, Li L. Association of sleep duration and breast cancer OncotypeDX recurrence score. Breast Cancer Res Treat 2012; 134:1291–1295.
- Schutte-Rodin S, Broch L, Buysse D, Dorsey C, Sateia M. Clinical guideline for the evaluation and management of chronic insomnia in adults. J Clin Sleep Med 2008; 4:487–504.
- Fan HG, Houédé-Tchen N, Yi QL, et al. Fatigue, menopausal symptoms, and cognitive function in women after adjuvant chemotherapy for breast cancer: 1- and 2-year follow-up of a prospective controlled study. J Clin Oncol 2005; 23:8025–8032.
- Ganz PA. Late effects of cancer and its treatment. Semin Oncol Nurs 2001; 17:241–248.
- Lee TS, Kilbreath SL, Refshauge KM, Pendlebury SC, Beith JM, Lee MJ. Quality of life of women treated with radiotherapy for breast cancer. Support Care Cancer 2008; 16:399–405.
- National Institutes of Health. National Institutes of Health state of the science conference statement on manifestations and management of chronic insomnia in adults, June 13–15, 2005. Sleep 2005; 28:1049–1057.
- Smith MT, Huang MI, Manber R. Cognitive behavior therapy for chronic insomnia occurring within the context of medical and psychiatric disorders. Clin Psychol Rev 2005; 25:559–592.
- Quesnel C, Savard J, Simard S, Ivers H, Morin CM. Efficacy of cognitive-behavioral therapy for insomnia in women treated for nonmetastatic breast cancer. J Consult Clin Psychol 2003; 71:189–200.
- Savard J, Simard S, Ivers H, Morin CM. Randomized study on the efficacy of cognitive-behavioral therapy for insomnia secondary to breast cancer, part I: sleep and psychological effects. J Clin Oncol 2005; 23:6083–6096.
- Berger AM, Kuhn BR, Farr LA, et al. Behavioral therapy intervention trial to improve sleep quality and cancer-related fatigue. Psychooncology 2009; 18:634–646.
- Espie CA, Fleming L, Cassidy J, et al. Randomized controlled clinical effectiveness trial of cognitive behavior therapy compared with treatment as usual for persistent insomnia in patients with cancer. J Clin Oncol 2008; 26:4651–4658.
- National Institutes of Health. National Institutes of Health state of the science conference statement on manifestations and management of chronic insomnia in adults, June 13–15, 2005. Sleep 2005; 28:1049–1057.
- Savard J, Villa J, Simard S, Ivers H, Morin CM. Feasibility of a self-help treatment for insomnia comorbid with cancer. Psychooncology 2011; 20:1013–1019.
- Derogatis LR, Feldstein M, Morrow G, et al. A survey of psychotropic drug prescriptions in an oncology population. Cancer 1979; 44:1919–1929.
- Stiefel FC, Kornblith AB, Holland JC. Changes in the prescription patterns of psychotropic drugs for cancer patients during a 10-year period. Cancer 1990; 65:1048–1053.
- Minton O, Richardson A, Sharpe M, Hotopf M, Stone P. A systematic review and meta-analysis of the pharmacological treatment of cancer-related fatigue. J Natl Cancer Inst 2008; 100:1155–1166.
- Minton O, Stone P, Richardson A, Sharpe M, Hotopf M. Drug therapy for the management of cancer-related fatigue. Cochrane Database Syst Rev 2008;CD006704.
- Krystal AD, Walsh JK, Laska E, et al. Sustained efficacy of eszopiclone over 6 months of nightly treatment: results of a randomized, double-blind, placebo-controlled study in adults with chronic insomnia. Sleep 2003; 26:793–799.
- Liu J, Wang LN. Ramelteon in the treatment of chronic insomnia: systematic review and meta-analysis. Int J Clin Pract 2012; 66:867–873.
- Cankurtaran ES, Ozalp E, Soygur H, Akbiyik DI, Turhan L, Alkis N. Mirtazapine improves sleep and lowers anxiety and depression in cancer patients: superiority over imipramine. Support Care Cancer 2008; 16:1291–1298.
- Palesh OG, Mustian KM, Peppone LJ, et al. Impact of paroxetine on sleep problems in 426 cancer patients receiving chemotherapy: a trial from the University of Rochester Cancer Center Community Clinical Oncology Program. Sleep Med 2012; 13:1184–1190.
- Ness KK, Wall MM, Oakes JM, Robison LL, Gurney JG. Physical performance limitations and participation restrictions among cancer survivors: a population-based study. Ann Epidemiol 2006; 16:197–205.
- Deimling GT, Bowman KF, Sterns S, Wagner LJ, Kahana B. Cancer-related health worries and psychological distress among older adult, long-term cancer survivors. Psychooncology 2006; 15:306–320.
- Davidson JR, MacLean AW, Brundage MD, Schulze K. Sleep disturbance in cancer patients. Soc Sci Med 2002; 54:1309–1321.
- Savard J, Morin CM. Insomnia in the context of cancer: a review of a neglected problem. J Clin Oncol 2001; 19:895–908.
- Payne RJ, Hier MP, Kost KM, et al. High prevalence of obstructive sleep apnea among patients with head and neck cancer. J Otolaryngol 2005; 34:304–311.
- American Academy of Sleep Medicine. International Classification of Sleep Disorders—Second Edition (ICSD-2); 2005.
- Fortner BV, Stepanski EJ, Wang SC, Kasprowicz S, Durrence HH. Sleep and quality of life in breast cancer patients. J Pain Symptom Manage 2002; 24:471–480.
- Chen ML, Yu CT, Yang CH. Sleep disturbances and quality of life in lung cancer patients undergoing chemotherapy. Lung Cancer 2008; 62:391–400.
- Liu L, Ancoli-Israel S. Sleep disturbances in cancer. Psychiatr Ann 2008; 38:627–634.
- Ancoli-Israel S, Liu L, Marler MR, et al. Fatigue, sleep, and circadian rhythms prior to chemotherapy for breast cancer. Support Care Cancer 2006; 14:201–209.
- Miaskowski C, Lee K, Dunn L, et al. Sleep-wake circadian activity rhythm parameters and fatigue in oncology patients before the initiation of radiation therapy. Cancer Nurs 2011; 34:255–268.
- Liu L, Rissling M, Natarajan L, et al. The longitudinal relationship between fatigue and sleep in breast cancer patients undergoing chemotherapy. Sleep 2012; 35:237–245.
- Savard J, Ivers H, Villa J, Caplette-Gingras A, Morin CM. Natural course of insomnia comorbid with cancer: an 18-month longitudinal study. J Clin Oncol 2011; 29:3580–3586.
- Sela RA, Watanabe S, Nekolaichuk CL. Sleep disturbances in palliative cancer patients attending a pain and symptom control clinic. Palliat Support Care 2005; 3:23–31.
- Mao JJ, Armstrong K, Bowman MA, Xie SX, Kadakia R, Farrar JT. Symptom burden among cancer survivors: impact of age and comorbidity. J Am Board Fam Med 2007; 20:434–443.
- Schroevers MJ, Ranchor AV, Sanderman R. The role of age at the onset of cancer in relation to survivors’ long-term adjustment: a controlled comparison over an eight-year period. Psychooncology 2004; 13:740–752.
- Stein KD, Syrjala KL, Andrykowski MA. Physical and psychological long-term and late effects of cancer. Cancer 2008; 112(suppl 11):2577–2592.
- Anderson KO, Getto CJ, Mendoza TR, et al. Fatigue and sleep disturbance in patients with cancer, patients with clinical depression, and community-dwelling adults. J Pain Symptom Manage 2003; 25:307–318.
- Savard J, Villa J, Ivers H, Simard S, Morin CM. Prevalence, natural course, and risk factors of insomnia comorbid with cancer over a 2-month period. J Clin Oncol 2009; 27:5233–5239.
- Engstrom CA, Strohl RA, Rose L, Lewandowski L, Stefanek ME. Sleep alterations in cancer patients. Cancer Nurs 1999; 22:143–148.
- Hoffman A, Given BA, von Eye A, Given CW, Gift AG. A study on the relationship between fatigue, pain, insomnia, and gender in persons with lung cancer. Oncol Nurs Forum 2006; 33:404.
- Hoffman AJ, Given BA, von Eye A, Gift AG, Given CW. Relationships among pain, fatigue, insomnia, and gender in persons with lung cancer. Oncol Nurs Forum 2007; 34:785–792.
- Shapiro SL, Bootzin RR, Figueredo AJ, Lopez AM, Schwartz GE. The efficacy of mindfulness-based stress reduction in the treatment of disturbance in women with breast cancer: an exploratory study. J Psychosom Res 2003; 54:85–91.
- Shapiro SL, Lopez AM, Schwartz GE, et al. Quality of life and breast cancer: relationship to psychosocial variables. J Clin Psychol 2001; 57:501–519.
- Mock V, Atkinson A, Barsevick A, et al; National Comprehensive Cancer Network. NCCN practice guidelines for cancer-related fatigue. Oncology (Williston Park) 2000; 14:151–161.
- Cella D, Davis K, Breitbart W, Curt G;Fatigue Coalition. Cancer-related fatigue: prevalence of proposed diagnostic criteria in a United States sample of cancer survivors. J Clin Oncol 2001; 19:3385–3391.
- Sateia MJ, Lang BJ. Sleep and cancer: recent developments. Curr Oncol Rep 2008; 10:309–318.
- Ahluwalia M. Fatigue, pain, and depression among older adults with cancer: still underrecognized and undertreated. Geriatrics and Aging 2008; 11:495–501.
- Enderlin CA, Coleman EA, Cole C, Richards KC, Hutchins LF, Sherman AC. Sleep across chemotherapy treatment: a growing concern for women older than 50 with breast cancer. Oncol Nurs Forum 2010; 37:461–A3.
- Winningham ML, Nail LM, Burke MB, et al. Fatigue and the cancer experience: the state of the knowledge. Oncol Nurs Forum 1994; 21:23–36.
- Berger AM, Mitchell SA. Modifying cancer-related fatigue by optimizing sleep quality. J Natl Compr Canc Netw 2008; 6:3–13.
- Anderson KO, Getto CJ, Mendoza TR, et al. Fatigue and sleep disturbance in patients with cancer, patients with clinical depression, and community-dwelling adults. J Pain Symptom Manage 2003; 25:307–318.
- Armstrong TS, Cohen MZ, Eriksen LR, Hickey JV. Symptom clusters in oncology patients and implications for symptom research in people with primary brain tumors. J Nurs Scholarsh 2004; 36:197–206.
- Dodd MJ, Miaskowski C, Lee KA. Occurrence of symptom clusters. J Natl Cancer Inst Monogr 2004;76–78.
- Paice JA. Assessment of symptom clusters in people with cancer. J Natl Cancer Inst Monogr 2004;98–102.
- Åhsberg E, Fürst CJ. Dimensions of fatigue during radiotherapy—an application of the Swedish Occupational Fatigue Inventory (SOFI) on cancer patients. Acta Oncol 2001; 40:37–43.
- Foley KM. The treatment of cancer pain. N Engl J Med 1985; 313:84–95.
- Twycross RG, Fairfield S. Pain in far-advanced cancer. Pain 1982; 14:303–310.
- Cleeland CS, Gonin R, Hatfield AK, et al. Pain and its treatment in outpatients with metastatic cancer. N Engl J Med 1994; 330:592–596.
- Fleming L, Gillespie S, Espie CA. The development and impact of insomnia on cancer survivors: Psychooncology 2010; 19:991–996.
- Grond S, Zech D, Diefenbach C, Bischoff A. Prevalence and pattern of symptoms in patients with cancer pain: a prospective evaluation of 1635 cancer patients referred to a pain clinic. J Pain Symptom Manage 1994; 9:372–382.
- Smith MT, Haythornthwaite JA. How do sleep disturbance and chronic pain inter-relate? Insights from the longitudinal and cognitive-behavioral clinical trials literature. Sleep Med Rev 2004; 8:119–132.
- Lewin DS, Dahl RE. Importance of sleep in the management of pediatric pain. J Dev Behav Pediatr 1999; 20:244–252.
- Yue HJ, Guilleminault C. Opioid medication and sleep-disordered breathing. Med Clin North Am 2010; 94:435–446.
- Teichtahl H, Wang D. Sleep-disordered breathing with chronic opioid use. Expert Opin Drug Saf 2007; 6:641–649.
- Ancoli-Israel S, Moore PJ, Jones V. The relationship between fatigue and sleep in cancer patients: a review. Eur J Cancer Care (Engl) 2001; 10:245–255.
- Perlis ML, Giles DE, Buysse DJ, Tu X, Kupfer DJ. Self-reported sleep disturbance as a prodromal symptom in recurrent depression. J Affect Disord 1997; 42:209–212.
- Stone P, Hardy J, Broadley K, Tookman AJ, Kurowska A, A’Hern R. Fatigue in advanced cancer: a prospective controlled cross-sectional study. Br J Cancer 1999; 79:1479–1486.
- Cimprich B. Pretreatment symptom distress in women newly diagnosed with breast cancer. Cancer Nurs 1999; 22:185–194.
- Liu L, Fiorentino L, Natarajan L, et al. Pre-treatment symptom cluster in breast cancer patients is associated with worse sleep, fatigue and depression during chemotherapy. Psychooncology 2009; 18:187–194.
- Savard J, Hervouet S, Ivers H. Prostate cancer treatments and their side effects are associated with increased insomnia. Psychooncology 2013; 22:1381–1388.
- Fenlon DR, Corner JL, Haviland J. Menopausal hot flushes after breast cancer. Eur J Cancer Care (Engl) 2009; 18:140–148.
- Miller AH, Ancoli-Israel S, Bower JE, Capuron L, Irwin MR. Neuroendocrine-immune mechanisms of behavioral comorbidities in patients with cancer. J Clin Oncol 2008; 26:971–982.
- Kirkbride P, Bezjak A, Pater J, et al. Dexamethasone for the prophylaxis of radiation-induced emesis: a National Cancer Institute of Canada Clinical Trials Group phase III study. J Clin Oncol 2000; 18:1960–1966.
- Coussens LM, Werb Z. Inflammation and cancer. Nature 2002; 420:860–867.
- Vgontzas AN, Chrousos GP. Sleep, the hypothalamic-pituitary-adrenal axis, and cytokines: multiple interactions and disturbances in sleep disorders. Endocrinol Metab Clin North Am 2002; 31:15–36.
- Vgontzas AN, Zoumakis M, Papanicolaou DA, et al. Chronic insomnia is associated with a shift of interleukin-6 and tumor necrosis factor secretion from nighttime to daytime. Metabolism 2002; 51:887–892.
- Mills PJ, Parker B, Jones V, et al. The effects of standard anthracycline-based chemotherapy on soluble ICAM-1 and vascular endothelial growth factor levels in breast cancer. Clin Cancer Res 2004; 10:4998–5003.
- Reiter RJ, Tan DX, Korkmaz A, et al. Light at night, chronodisruption, melatonin suppression, and cancer risk: a review. Crit Rev Oncog 2007; 13:303–328.
- Schernhammer ES, Hankinson SE. Urinary melatonin levels and breast cancer risk. J Natl Cancer Inst 2005; 97:1084–1087.
- Travis RC, Allen DS, Fentiman IS, Key TJ. Melatonin and breast cancer: a prospective study. J Natl Cancer Inst 2004; 96:475–482.
- Verkasalo PK, Lillberg K, Stevens RG, et al. Sleep duration and breast cancer: a prospective cohort study. Cancer Res 2005; 65:9595–9600.
- Wu AH, Wang R, Koh WP, Stanczyk FZ, Lee HP, Yu MC. Sleep duration, melatonin and breast cancer among Chinese women in Singapore. Carcinogenesis 2008; 29:1244–1248.
- McElroy JA, Newcomb PA, Titus-Ernstoff L, Trentham-Dietz A, Hampton JM, Egan KM. Duration of sleep and breast cancer risk in a large population-based case-control study. J Sleep Res 2006; 15:241–249.
- Pinheiro SP, Schernhammer ES, Tworoger SS, Michels KB. A prospective study on habitual duration of sleep and incidence of breast cancer in a large cohort of women. Cancer Res 2006; 66:5521–5525.
- Lie JA, Roessink J, Kjaerheim K. Breast cancer and night work among Norwegian nurses. Cancer Causes Control 2006; 17:39–44.
- Schernhammer ES, Kroenke CH, Laden F, Hankinson SE. Night work and risk of breast cancer. Epidemiology 2006; 17:108–111.
- Schernhammer ES, Laden F, Speizer FE, et al. Rotating night shifts and risk of breast cancer in women participating in the Nurses’ Health Study. J Natl Cancer Inst 2001; 93:1563–1568.
- Davis S, Mirick DK, Stevens RG. Night shift work, light at night, and risk of breast cancer. J Natl Cancer Inst 2001; 93:1557–1562.
- Hansen J. Light at night, shiftwork, and breast cancer risk. J Natl Cancer Inst 2001; 93:1513–1515.
- Thompson CL, Larkin EK, Patel S, Berger NA, Redline S, Li L. Short duration of sleep increases risk of colorectal adenoma. Cancer 2011; 117:841–847.
- Degner LF, Sloan JA. Symptom distress in newly diagnosed ambulatory cancer patients and as a predictor of survival in lung cancer. J Pain Symptom Manage 1995; 10:423–431.
- Thompson CL, Li L. Association of sleep duration and breast cancer OncotypeDX recurrence score. Breast Cancer Res Treat 2012; 134:1291–1295.
- Schutte-Rodin S, Broch L, Buysse D, Dorsey C, Sateia M. Clinical guideline for the evaluation and management of chronic insomnia in adults. J Clin Sleep Med 2008; 4:487–504.
- Fan HG, Houédé-Tchen N, Yi QL, et al. Fatigue, menopausal symptoms, and cognitive function in women after adjuvant chemotherapy for breast cancer: 1- and 2-year follow-up of a prospective controlled study. J Clin Oncol 2005; 23:8025–8032.
- Ganz PA. Late effects of cancer and its treatment. Semin Oncol Nurs 2001; 17:241–248.
- Lee TS, Kilbreath SL, Refshauge KM, Pendlebury SC, Beith JM, Lee MJ. Quality of life of women treated with radiotherapy for breast cancer. Support Care Cancer 2008; 16:399–405.
- National Institutes of Health. National Institutes of Health state of the science conference statement on manifestations and management of chronic insomnia in adults, June 13–15, 2005. Sleep 2005; 28:1049–1057.
- Smith MT, Huang MI, Manber R. Cognitive behavior therapy for chronic insomnia occurring within the context of medical and psychiatric disorders. Clin Psychol Rev 2005; 25:559–592.
- Quesnel C, Savard J, Simard S, Ivers H, Morin CM. Efficacy of cognitive-behavioral therapy for insomnia in women treated for nonmetastatic breast cancer. J Consult Clin Psychol 2003; 71:189–200.
- Savard J, Simard S, Ivers H, Morin CM. Randomized study on the efficacy of cognitive-behavioral therapy for insomnia secondary to breast cancer, part I: sleep and psychological effects. J Clin Oncol 2005; 23:6083–6096.
- Berger AM, Kuhn BR, Farr LA, et al. Behavioral therapy intervention trial to improve sleep quality and cancer-related fatigue. Psychooncology 2009; 18:634–646.
- Espie CA, Fleming L, Cassidy J, et al. Randomized controlled clinical effectiveness trial of cognitive behavior therapy compared with treatment as usual for persistent insomnia in patients with cancer. J Clin Oncol 2008; 26:4651–4658.
- National Institutes of Health. National Institutes of Health state of the science conference statement on manifestations and management of chronic insomnia in adults, June 13–15, 2005. Sleep 2005; 28:1049–1057.
- Savard J, Villa J, Simard S, Ivers H, Morin CM. Feasibility of a self-help treatment for insomnia comorbid with cancer. Psychooncology 2011; 20:1013–1019.
- Derogatis LR, Feldstein M, Morrow G, et al. A survey of psychotropic drug prescriptions in an oncology population. Cancer 1979; 44:1919–1929.
- Stiefel FC, Kornblith AB, Holland JC. Changes in the prescription patterns of psychotropic drugs for cancer patients during a 10-year period. Cancer 1990; 65:1048–1053.
- Minton O, Richardson A, Sharpe M, Hotopf M, Stone P. A systematic review and meta-analysis of the pharmacological treatment of cancer-related fatigue. J Natl Cancer Inst 2008; 100:1155–1166.
- Minton O, Stone P, Richardson A, Sharpe M, Hotopf M. Drug therapy for the management of cancer-related fatigue. Cochrane Database Syst Rev 2008;CD006704.
- Krystal AD, Walsh JK, Laska E, et al. Sustained efficacy of eszopiclone over 6 months of nightly treatment: results of a randomized, double-blind, placebo-controlled study in adults with chronic insomnia. Sleep 2003; 26:793–799.
- Liu J, Wang LN. Ramelteon in the treatment of chronic insomnia: systematic review and meta-analysis. Int J Clin Pract 2012; 66:867–873.
- Cankurtaran ES, Ozalp E, Soygur H, Akbiyik DI, Turhan L, Alkis N. Mirtazapine improves sleep and lowers anxiety and depression in cancer patients: superiority over imipramine. Support Care Cancer 2008; 16:1291–1298.
- Palesh OG, Mustian KM, Peppone LJ, et al. Impact of paroxetine on sleep problems in 426 cancer patients receiving chemotherapy: a trial from the University of Rochester Cancer Center Community Clinical Oncology Program. Sleep Med 2012; 13:1184–1190.
KEY POINTS
- Sleep disturbances, primarily insomnia, profoundly affect all aspects of quality of life.
- Insomnia can be caused or worsened by a number of other conditions, such as pain, fatigue, depression, and anxiety, and these in turn can be worsened by insomnia.
- Cognitive-behavioral therapy is the treatment of choice for chronic insomnia. Underlying problems should be addressed.
- Drugs are often prescribed to help cancer patients sleep but should be used with caution, as there is limited information from clinical trials in this population.
Myasthenia gravis: Newer therapies offer sustained improvement
Current therapies for myasthenia gravis can help most patients achieve sustained improvement. The overall prognosis has dramatically improved over the last 4 decades: the mortality rate used to be 75%; now it is 4.5%.1
Myasthenia gravis is the most common disorder of neuromuscular junction transmission and is also one of the best characterized autoimmune diseases. However, its symptoms—primarily weakness—vary from patient to patient, and in the same patient, by time of day and over longer time periods. The variation in symptoms can be very confusing to undiagnosed patients and puzzling to unsuspecting physicians. Such diagnostic uncertainty can give the patient additional frustration and emotional stress, which in turn exacerbate his or her condition.
In this review, we will give an overview of the pathogenesis, clinical manifestations, diagnosis, and treatment of myasthenia gravis.
TWO PEAKS IN INCIDENCE BY AGE
The annual incidence of myasthenia gravis is approximately 10 to 20 new cases per million, with a prevalence of about 150 to 200 per million.2
The age of onset has a bimodal distribution, with an early incidence peak in the second to third decade with a female predominance and a late peak in the 6th to the 8th decade with a male predominance.2
Myasthenia gravis is commonly associated with several other autoimmune disorders, including hypothyroidism, hyperthyroidism, systemic lupus erythematosus, rheumatoid arthritis, vitiligo, diabetes, and, more recently recognized, neuromyelitis optica.3
ANTIBODIES AGAINST AChR AND MuSK
In most cases of myasthenia gravis the patient has autoimmune antibodies against constituents of the neuromuscular junction, specifically acetylcholine receptor (AChR) and muscle-specific tyrosine kinase (MuSK) (Figure 1).
AChR antibody-positive myasthenia gravis
When antibodies bind to AChR on the postsynaptic membrane, they cross-link neighboring AChR units, which are absorbed into the muscle fiber and are broken up.4 In addition, the complement system is activated to mediate further damage on the postsynaptic membrane.
AChR antibodies may come from germinal centers of the thymus, where clustered myoid cells express AChR on the plasma membrane surface.5 About 60% of AChR antibody-positive myasthenia gravis patients have an enlarged thymus, and 10% have a thymoma—a tumor of the epithelial cells of this organ. Conversely, about 15% of patients with a thymoma have clinical myasthenia gravis, and an additional 20% possess antibodies against AChR in the serum without myasthenic symptoms.5
MuSK antibody-positive myasthenia gravis
Like AChR, MuSK is a transmembrane component of the postsynaptic neuromuscular junction. During formation of the neuromuscular junction, MuSK is activated through the binding of agrin (a nerve-derived proteoglycan) to lipoprotein-related protein 4 (LRP4), after which complicated intracellular signaling promotes the assembly and stabilization of AChR.6
Unlike AChR antibodies, antibodies against MuSK do not activate the complement system, and complement fixation is not essential for clinical myasthenic symptoms to appear.7 Also, myasthenia gravis with MuSK antibodies is rarely associated with thymoma.8
The precise mechanism by which MuSK antibody impairs transmission at the neuromuscular junction has been a mystery until recently. Animal models, including MuSK-mutant mice and mice injected with MuSK protein or with purified immunoglobulin G from patients with this disease, have revealed a significant reduction of AChR clusters and destruction of neuromuscular junction structures.7,9–12
In addition, MuSK antibodies produce pre-synaptic dysfunction, manifesting as a reduction of acetylcholine content. This information is based on studies in mice and on in vitro electrophysiologic analyses of neuromuscular junctions from a patient with this disease.7,9–13
Finally, MuSK antibodies may indirectly affect the recycling of acetylcholine. After post-synaptic activation, acetylcholine is normally hydrolized by acetylcholinesterase, which is located in the synaptic cleft but anchored to MuSK on the postsynaptic membrane. MuSK antibodies block the binding of MuSK to acetylcholinesterase, possibly leading to less accumulation of acetylcholinesterase.14 This process may explain why patients with MuSK antibody-positive myasthenia gravis tend to respond poorly to acetylcholinesterase inhibitors (more about this below).
Seronegative myasthenia gravis
In a series of 562 consecutive patients with generalized weakness due to myasthenia gravis, 92% were positive for AChR antibody, 3% were positive for MuSK antibody, and 5% were seronegative (possessing neither antibody).15 In contrast, about 50% of patients with purely ocular myasthenia gravis (ie, with isolated weakness of the levator palpebrae superioris, orbicularis oculi, or oculomotor muscles) are seropositive for AChR antibody. Only a few ocular MuSK antibody-positive cases have been described, leaving the rest seronegative. Rarely, both antibodies can be detected in the same patient.16
In patients who are negative for AChR antibodies at the time of disease onset, sero-conversion may occur later during the course. Repeating serologic testing 6 to 12 months later may detect AChR antibodies in approximately 15% of patients who were initially seronegative.15,17
The clinical presentation, electrophysiologic findings, thymic pathologic findings, and treatment responses are similar in AChR antibody-positive and seronegative myasthenia gravis.17 Muscle biopsy study in seronegative cases demonstrates a loss of AChR as well.18
Based on these observations, it has been proposed that seronegative patients may have low-affinity antibodies that can bind to tightly clustered AChRs on the postsynaptic membrane but escape detection by routine radioimmunoassays in a solution phase. With a sensitive cell-based immunofluorescence assay, low-affinity antibodies to clustered AChRs were detected in 66% of patients with generalized myasthenia gravis and in 50% of those with ocular myasthenia gravis who were seronegative on standard assays.19,20 These low-affinity AChR antibodies can also activate complement in vitro, increasing the likelihood that they are pathogenic. However, assays to detect low-affinity AChR antibodies are not yet commercially available.
Within the past year, three research groups independently reported detecting antibodies to LRP4 in 2% to 50% of seronegative myasthenia gravis patients. This wide variation in the prevalence of LRP4 antibodies could be related to patient ethnicity and methods of detection.21–23 LRP4 is a receptor for agrin and is required for agrin-induced MuSK activation and AChR clustering. LRP antibodies can activate complement; therefore, it is plausible that LRP4 antibody binding leads to AChR loss on the postsynaptic membrane. However, additional study is needed to determine if LRP4 antibodies are truly pathogenic in myasthenia gravis.
A DISORDER OF FATIGABLE WEAKNESS
Myasthenia gravis is a disorder of fatigable weakness producing fluctuating symptoms. Symptoms related to the involvement of specific muscle groups are listed in Table 1. Muscle weakness is often worse later in the day or after exercise.
Ocular myasthenia gravis accounts for about 15% of all cases. Of patients initially presenting with ocular symptoms only, twothirds will ultimately develop generalized symptoms, most within the first 2 years.24 No factor has been identified that predicts conversion from an ocular to a generalized form.
Several clinical phenotypes of MuSK antibody-positive myasthenia gravis have been described. An oculobulbar form presents with diplopia, ptosis, dysarthria, and profound atrophy of the muscles of the tongue and face. A restricted myopathic form presents with prominent neck, shoulder, and respiratory weakness without ocular involvement. A third form is a combination of ocular and proximal limb weakness, indistinguishable from AChR antibody-positive disease.25
MuSK antibody-positive patients do not respond as well to acetylcholinesterase inhibitors as AChR antibody-positive patients do. In one study, nearly 70% of MuSK antibody-positive patients demonstrated no response, poor tolerance, or cholinergic hypersensitivity to these agents.25 Fortunately, most MuSK antibody-positive patients have a favorable response to immunosuppressive therapy—sometimes a dramatic improvement after plasmapheresis.8
DIAGNOSIS OF MYASTHENIA GRAVIS
The common differential diagnoses for myasthenia gravis are listed in Table 2.
The essential feature of myasthenia gravis is fluctuating muscle weakness, often with fatigue. Many patients complain of weakness of specific muscle groups after their repeated usage. Pain is generally a less conspicuous symptom, and generalized fatigue without objective weakness is inconsistent with myasthenia gravis.
Signs of muscle weakness may include droopy eyelids, diplopia, inability to hold the head straight, difficulty swallowing or chewing, speech disturbances, difficulty breathing, and difficulty raising the arms or rising from the sitting position. A historical pattern of ptosis alternating from one eye to the other is fairly characteristic of myasthenia gravis.
The weakness of orbicularis oculi is easily identified on examination by prying open the eyes during forced eye closure. Limb weakness is usually more significant in the arms than in the legs. An often-neglected feature of myasthenia gravis is finger extensor weakness with a relative sparing of other distal hand muscles.2
The ice-pack test is performed by placing a small bag of ice over the ptotic eye for 2 to 5 minutes and assessing the degree of ptosis for any noticeable improvement. This test is not very helpful for assessing ocular motor weakness.
The edrophonium (Tensilon) test can be used for patients with ptosis or ophthalmoparesis. Edrophonium, a short-acting acetylcholinesterase inhibitor, is given intravenously while the patient is observed for objective improvement. The patient’s cardiovascular status should be monitored for arrhythmias and hypotension. Atropine should be immediately available in case severe bradycardia develops.
The ice-pack test and the edrophonium test can give false-negative and false-positive results, and the diagnosis of myasthenia gravis must be verified by other diagnostic tests.
Testing for antibodies
Testing for circulating AChR antibodies, MuSK antibodies, or both is the first step in the laboratory confirmation of myasthenia gravis.
There are three AChR antibody subtypes: binding, blocking, and modulating. Binding antibodies are present in 80% to 90% of patients with generalized myasthenia gravis and 50% of those with ocular myasthenia gravis. Testing for blocking and modulating AChR antibodies increases the sensitivity by less than 5% when added to testing for binding antibodies.
AChR antibody titers correlate poorly with disease severity between patients. However, in individual patients, antibody titers tend to go down in parallel with clinical improvement.
MuSK antibody is detected in nearly half of myasthenia gravis patients with generalized weakness who are negative for AChR antibody.
Electrophysiologic tests
Electrophysiologic tests can usually confirm the diagnosis of seronegative myasthenia gravis. They are also helpful in seropositive patients who have unusual clinical features or a poor response to treatment.
Repetitive nerve stimulation studies use a slow rate (2–5 Hz) of repetitive electrical stimulation. The study is positive if the motor response declines by more than 10%. However, a decremental response is not specific for myasthenia gravis, as it may be seen in other neuromuscular disorders such as motor neuron disease or Lambert-Eaton myasthenic syndrome.
This test is technically easier to do in distal muscles than in proximal muscles, but less sensitive. Therefore, proximal muscles such as the trapezius or facial muscles are usually also sampled to maximize the yield. To further maximize the sensitivity, muscles being tested should be warm, and acetylcholinesterase inhibitors should be withheld for 12 hours before.
Repetitive nerve stimulation studies in distal muscles are positive in approximately 75% of patients with generalized myasthenia gravis and in 30% with ocular myasthenia gravis.26
Single-fiber electromyography is more technically demanding than repetitive nerve stimulation and is less widely available. It is usually performed with a special needle electrode that can simultaneously identify action potentials arising from individual muscle fibers innervated by the same axon.
Variability in time of the second action potential relative to the first is called “jitter.” Abnormal jitter is seen in more than 95% of patients with generalized myasthenia gravis and in 85% to 90% of those with ocular myasthenia gravis.26,27 However, abnormal jitter can also be seen in other neuromuscular diseases such as motor neuron disease or in neuromuscular junctional disorders such as Lambert-Eaton myasthenic syndrome.
Imaging studies
Chest computed tomography or magnetic resonance imaging with contrast should be performed in all myasthenia gravis patients to look for a thymoma.
TREATMENT OF MYASTHENIA GRAVIS
Acetylcholinesterase inhibitors
As a reasonable first therapy in mild cases of myasthenia gravis, acetylcholinesterase inhibitors slow down the degradation of acetylcholine and prolong its effect in the neuromuscular junction, but they are not disease-modifying and their benefits are mild.
Pyridostigmine is the usual choice of acetylcholinesterase inhibitor. Its onset of action is rapid (15 to 30 minutes) and its action lasts for 3 to 4 hours. For most patients, the effective dosage range is 60 mg to 90 mg every 4 to 6 hours. A long-acting form is also available and can be given as a single nighttime dose.
Immunomodulating therapy
Patients who have moderate to severe symptoms require some form of immunomodulating therapy.
Plasmapheresis or intravenous immune globulin is reserved for patients with severe or rapidly worsening disease because their beneficial effects can be seen within the first week of treatment.
Longer-acting immunotherapies (corticosteroids, azathioprine, mycophenolate mofetil and others) have a slower onset of responses but provide sustained benefits. Which drug to use depends on factors such as comorbidity, side effects, and cost.
Drugs to avoid
A number of medications can exacerbate weakness in myasthenia gravis and should be avoided or used with caution. The list is long, but ones that deserve the most attention are penicillamine, interferons, procainamide, quinidine, and antibiotics, including quinolones and aminoglycosides. A more comprehensive list of medications that may exacerbate myasthenia gravis symptoms can be found in a review by Keesey.2
RAPID INDUCTION IMMUNOTHERAPIES : PLASMAPHERESIS, IMMUNE GLOBULIN
Both plasmapheresis and intravenous immune globulin act quickly over days, but in most patients their effects last only a few weeks. Both are used as rescue therapies for myasthenic crises, bridging therapy to slow-acting immunotherapeutic agents, or maintenance treatment for poorly controlled cases.
Several retrospective studies have confirmed the efficacy of plasmapheresis in more than 80% of patients with generalized symptoms.28,29
In a randomized trial in patients with generalized therapies, intravenous immune globulin improved muscle strength in the group of patients with severe symptoms.30 The effective dosage of intravenous immune globulin varies from 1 to 2 g/kg without observed difference between doses.31 Trials comparing the efficacy of intravenous immune globulin and plasmapheresis in acute and severe myasthenia gravis did not reveal a difference in efficacy.32,33 Intravenous immune globulin at a minimal dose of 0.4 g/kg every 3 months has been successfully used as a long-term maintenance monotherapy, and such a role could be expanded to more patients with further studies.34
The choice between plasmapheresis and intravenous immune globulin is often based on the ability of a patient to tolerate each treatment and on the availability of the plasmapheresis procedure. Intravenous immune globulin is easier to administer, is associated with fewer adverse events related to vascular access, and is therefore more appropriate than plasmapheresis in some centers.
CHRONIC MAINTENANCE IMMUNOMODULATING TREATMENT
Corticosteroids
Prednisone, the most commonly used agent, leads to remission or marked improvement in 70% to 80% of patients with ocular or generalized myasthenia gravis.35 It may also reduce the progression of ocular myasthenia gravis to the generalized form.36
The effective dose of prednisone depends on the severity and distribution of symptoms. Some patients may need up to 1.0 mg/kg/day (usually 50 to 80 mg per day). In patients with mild to moderate symptoms, a lower maximal dosage such as 20 to 40 mg per day can be sufficient.
Within 1 to 2 weeks after starting high-dose prednisone, up to 50% of patients may develop a transient deterioration, including possible precipitation of a myasthenic crisis.37 For this reason, high-dose prednisone is commonly started only in hospitalized patients who are also receiving plasmapheresis or intravenous immune globulin. Otherwise, an outpatient dose-escalation protocol can be used to achieve a target dose over several weeks.
Prednisone tapering can begin after the patient has been on the maximal dose for 1 to 2 months and significant improvement is evident. A monthly tapering of 5 to 10 mg is preferred, then more slowly after the daily dose reaches 30 mg. The usual maintenance dose averages about 5 mg daily.
Common side effects of prednisone include weight gain, cushingoid features, easy bruising, cataracts, glaucoma, hypertension, diabetes, dyslipidemia, and osteoporosis. Patients are advised to take supplemental calcium (1,500 mg per day) and vitamin D (400 to 800 IU per day). For those most at risk of osteoporosis, treatment with a bisphosphonate should be considered.
Other immunotherapeutic agents are often needed, either to replace the corticosteroid or to permit use of lower doses of it. Because of their delayed onset of action, starting such corticosteroid-sparing agents early in the course is often necessary. These agents are often initially combined with high-dose prednisone, with an eventual goal of weaning off prednisone entirely. This strategy offers the advantage of relatively rapid induction while avoiding the long-term adverse effects of corticosteroid treatment.
Azathioprine
Azathioprine doesn’t begin to show a beneficial effect in myasthenia gravis for 6 to 12 months, and it often reaches its maximal efficacy only after 1 to 2 years of treatment.38
In a study of 78 myasthenia gravis patients, 91% improved when treated with azathioprine alone or together with prednisone.39 In another study using azathioprine and prednisolone for generalized myasthenia gravis, nearly two-thirds of patients came off prednisolone while maintaining remission for 3 years.38
A typical maintenance dose is 2 to 3 mg/kg/day. Common side effects are nausea, vomiting, and malaise. Less frequent side effects include hematologic abnormalities, abnormal liver function, and pancreatitis. Monthly monitoring of complete blood cell counts and liver function tests is warranted for the first 6 months, then less often.
One in 300 people in the general population is homozygous for a mutant allele in the thiopurine methyltransferase (TPMT) gene. Patients with this genotype should not receive azathioprine because of the risk of life-threatening bone marrow suppression.40 A slightly increased risk of various forms of lymphoma has been documented.41
Mycophenolate mofetil
A well-tolerated medication with few side effects, mycophenolate mofetil is being used more in myasthenia gravis. The results of two recent randomized trials suggested that it is not effective in improving myasthenia gravis symptoms or sparing prednisone dosage when used for 90 days or 36 weeks.42,43 However, extensive clinical experience supports its longterm efficacy in myasthenia gravis.
In a retrospective study of 85 patients with generalized myasthenia gravis, mycophenolate at doses of 1 to 3 g daily improved symptoms in 73% and produced remission in 50%. Steroid dosage was reduced in 71% of patients.44
Another retrospective study, with 102 patients, verified a slow development of clinical benefit after months of mycophenolate therapy alone or in combination with prednisone. Approximately 50% of patients achieved a minimal manifestation status after 6 to 12 months of mycophenolate treatment. Eventually, at 24 months of treatment, 80% of patients had a desirable outcome of minimal clinical manifestation or better, 55% of patients were able to come off prednisone entirely, and 75% were taking less than 7.5 mg of prednisone per day.45
Common side effects of mycophenolate include nausea, diarrhea, and infections such as urinary tract infections and herpes reactivation. The complete blood cell count needs to be monitored frequently during the first 6 months of therapy. Leukopenia can occur but rarely necessitates stopping mycophenolate. Long-term safety data are lacking, but so far there has been no clearly increased risk of malignancy.
Mycophenolate exposure in pregnancy results in a high incidence of major fetal malformations. Therefore, its use in pregnant patients is discouraged, and women of child-bearing age should use effective contraception.46
Cyclosporine
A randomized trial in a small number of patients suggested that cyclosporine is fairly effective as monotherapy.47 Its onset of action in myasthenia gravis is faster than that of other corticosteroid-sparing agents, and clinical benefit can often be observed as early as 1 to 2 months. A dose of 5 mg/kg/day and a maintenance serum level of 100 to 150 ng/mL are generally recommended. However, renal, hepatic, and hematologic toxicities and interactions with other medications make cyclosporine a less attractive choice.
Methotrexate
A randomized trial evaluated the utility of methotrexate as a steroid-sparing agent compared with azathioprine.48 At 24 months, its steroid-sparing effect was similar to that of azathioprine, and the prednisone dosage had been reduced in more than 50% of patients.
Another phase II trial studying the efficacy of methotrexate in myasthenia gravis is under way.49
Rituximab
Rituximab is a monoclonal antibody against B-cell membrane marker CD20. A growing number of case series support its efficacy in patients with severe generalized myasthenia gravis refractory to multiple immunosuppressants.16,50 It seems particularly effective for MuSK antibody-positive disease, reducing MuSK antibody titers and having a treatment effect that lasts for years.
The standard dosage is 375 mg/m2 per week for 4 consecutive weeks. Peripheral B cells tend to be depleted within 2 weeks after the first infusion, while T-cell populations remain unchanged.50
A minimal infusion reaction such as flushing and chills can be seen with the first infusion. Patients may be more susceptible to certain infections such as reactivation of herpes zoster, but overall rituximab is well tolerated. Rare cases of progressive multifocal leukoencephalopathy have been reported in patients taking it, but none have occurred so far in myasthenia gravis treatment.
Cyclophosphamide
Cyclophosphamide is an alkylating agent that reduces proliferation of both B and T cells. It can be effective in myasthenia gravis, but potentially serious side effects limit its use. It should be reserved for the small percentage of cases that are refractory to other immunotherapies.
Thymectomy
Surgical treatment should be considered for patients with thymoma. If the tumor cannot be surgically resected, chemoradiotherapy can be considered for relief of myasthenic symptoms and for prevention of local invasion.
Thymomas recur in a minority of patients many years after the initial resection, sometimes without myasthenia gravis symptoms. A recurrence of symptoms does not necessarily indicate a recurrence of thymoma. The lack of correlation between myasthenia gravis symptoms and thymoma recurrence highlights the importance of radiologic follow-up in these patients.
For patients without thymoma, many experts believe that thymectomy is beneficial in patients under age 60 who have generalized myasthenia gravis. The likelihood of medication-free remission is about twice as high, and the likelihood of becoming asymptomatic is about one and a half times higher after thymectomy.51 However, it takes up to several years for the benefits of thymectomy to manifest, and thymectomy does not guarantee protection from developing AChR antibody-positive myasthenia gravis in the future.
The optimal timing of thymectomy is not well established; however, the procedure is usually recommended within the first 3 years of diagnosis.52 The response rates from thymectomy are similar for AChR antibody-positive and seronegative patients. In general, thymectomy for MuSK antibody-positive patients has not been effective, and its role in ocular myasthenia gravis is unclear.2,53
- Alshekhlee A, Miles JD, Katirji B, Preston DC, Kaminski HJ. Incidence and mortality rates of myasthenia gravis and myasthenic crisis in US hospitals. Neurology 2009; 72:1548–1554.
- Keesey JC. Clinical evaluation and management of myasthenia gravis. Muscle Nerve 2004; 29:484–505.
- Leite MI, Coutinho E, Lana-Peixoto M, et al. Myasthenia gravis and neuromyelitis optica spectrum disorder: a multicenter study of 16 patients. Neurology 2012; 78:1601–1607.
- Drachman DB, Angus CW, Adams RN, Michelson JD, Hoffman GJ. Myasthenic antibodies cross-link acetylcholine receptors to accelerate degradation. N Engl J Med 1978; 298:1116–1122.
- Fujii Y. The thymus, thymoma and myasthenia gravis. Surg Today 2013; 43:461–466.
- Evoli A, Lindstrom J. Myasthenia gravis with antibodies to MuSK: another step toward solving mystery? Neurology 2011; 77:1783–1784.
- Mori S, Kubo S, Akiyoshi T, et al. Antibodies against muscle-specific kinase impair both presynaptic and postsynaptic functions in a murine model of myasthenia gravis. Am J Pathol 2012; 180:798–810.
- Guptill JT, Sanders DB, Evoli A. Anti-MuSK antibody myasthenia gravis: clinical findings and response to treatment in two large cohorts. Muscle Nerve 2011; 44:36–40.
- Chevessier F, Girard E, Molgó J, et al. A mouse model for congenital myasthenic syndrome due to MuSK mutations reveals defects in structure and function of neuromuscular junctions. Hum Mol Genet 2008; 17:3577–3595.
- Richman DP, Nishi K, Morell SW, et al. Acute severe animal model of anti-muscle-specific kinase myasthenia: combined postsynaptic and presynaptic changes. Arch Neurol 2012; 69:453–460.
- Klooster R, Plomp JJ, Huijbers MG, et al. Muscle-specific kinase myasthenia gravis IgG4 autoantibodies cause severe neuromuscular junction dysfunction in mice. Brain 2012; 135:1081–1101.
- Viegas S, Jacobson L, Waters P, et al. Passive and active immunization models of MuSK-Ab positive myasthenia: electrophysiological evidence for pre and postsynaptic defects. Exp Neurol 2012; 234:506–512.
- Niks EH, Kuks JB, Wokke JH, et al. Pre- and postsynaptic neuromuscular junction abnormalities in musk myasthenia. Muscle Nerve 2010; 42:283–288.
- Kawakami Y, Ito M, Hirayama M, et al. Anti-MuSK autoantibodies block binding of collagen Q to MuSK. Neurology 2011; 77:1819–1826.
- Chan KH, Lachance DH, Harper CM, Lennon VA. Frequency of seronegativity in adult-acquired generalized myasthenia gravis. Muscle Nerve 2007; 36:651–658.
- Collongues N, Casez O, Lacour A, et al. Rituximab in refractory and non-refractory myasthenia: a retrospective multicenter study. Muscle Nerve 2012; 46:687–691.
- Sanders DB, Andrews PI, Howard JF, Massey JM. Seronegative myasthenia gravis. Neurology 1997; 48(suppl 5):40S–45S.
- Shiraishi H, Motomura M, Yoshimura T, et al. Acetylcholine receptors loss and postsynaptic damage in MuSK antibody-positive myasthenia gravis. Ann Neurol 2005; 57:289–293.
- Leite MI, Jacob S, Viegas S, et al. IgG1 antibodies to acetylcholine receptors in ‘seronegative’ myasthenia gravis. Brain 2008; 131:1940–1952.
- Jacob S, Viegas S, Leite MI, et al. Presence and pathogenic relevance of antibodies to clustered acetylcholine receptor in ocular and generalized myasthenia gravis. Arch Neurol 2012; 69:994–1001.
- Higuchi O, Hamuro J, Motomura M, Yamanashi Y. Autoantibodies to low-density lipoprotein receptor-related protein 4 in myasthenia gravis. Ann Neurol 2011; 69:418–422.
- Pevzner A, Schoser B, Peters K, et al. Anti-LRP4 autoantibodies in AChR- and MuSK-antibody-negative myasthenia gravis. J Neurol 2012; 259:427–435.
- Zhang B, Tzartos JS, Belimezi M, et al. Autoantibodies to lipoprotein-related protein 4 in patients with double-seronegative myasthenia gravis. Arch Neurol 2012; 69:445–451.
- Kupersmith MJ, Latkany R, Homel P. Development of generalized disease at 2 years in patients with ocular myasthenia gravis. Arch Neurol 2003; 60:243–248.
- Pasnoor M, Wolfe GI, Nations S, et al. Clinical findings in MuSK-antibody positive myasthenia gravis: a US experience. Muscle Nerve 2010; 41:370–374.
- Oh SJ, Kim DE, Kuruoglu R, Bradley RJ, Dwyer D. Diagnostic sensitivity of the laboratory tests in myasthenia gravis. Muscle Nerve 1992; 15:720–724.
- Sanders DB, Stålberg EV. AAEM minimonograph #25: single-fiber electromyography. Muscle Nerve 1996; 19:1069–1083.
- Lazo-Langner A, Espinosa-Poblano I, Tirado-Cárdenas N, et al. Therapeutic plasma exchange in Mexico: experience from a single institution. Am J Hematol 2002; 70:16–21.
- Carandina-Maffeis R, Nucci A, Marques JF, et al. Plasmapheresis in the treatment of myasthenia gravis: retrospective study of 26 patients. Arq Neuropsiquiatr 2004; 62:391–395.
- Zinman L, Ng E, Bril V. IV immunoglobulin in patients with myasthenia gravis: a randomized controlled trial. Neurology 2007; 68:837–841.
- Gajdos P, Tranchant C, Clair B, et al; Myasthenia Gravis Clinical Study Group. Treatment of myasthenia gravis exacerbation with intravenous immunoglobulin: a randomized double-blind clinical trial. Arch Neurol 2005; 62:1689–1693.
- Rønager J, Ravnborg M, Hermansen I, Vorstrup S. Immunoglobulin treatment versus plasma exchange in patients with chronic moderate to severe myasthenia gravis. Artif Organs 2001; 25:967–973.
- Barth D, Nabavi Nouri M, Ng E, Nwe P, Bril V. Comparison of IVIg and PLEX in patients with myasthenia gravis. Neurology 2011; 76:2017–2023.
- Wegner B, Ahmed I. Intravenous immunoglobulin monotherapy in long-term treatment of myasthenia gravis. Clin Neurol Neurosurg 2002; 105:3–8.
- Pascuzzi RM, Coslett HB, Johns TR. Long-term corticosteroid treatment of myasthenia gravis: report of 116 patients. Ann Neurol 1984; 15:291–298.
- Monsul NT, Patwa HS, Knorr AM, Lesser RL, Goldstein JM. The effect of prednisone on the progression from ocular to generalized myasthenia gravis. J Neurol Sci 2004; 217:131–133.
- Miller RG, Milner-Brown HS, Mirka A. Prednisone-induced worsening of neuromuscular function in myasthenia gravis. Neurology 1986; 36:729–732.
- Palace J, Newsom-Davis J, Lecky B. A randomized double-blind trial of prednisolone alone or with azathioprine in myasthenia gravis. Myasthenia Gravis Study Group. Neurology 1998; 50:1778–1783.
- Mertens HG, Hertel G, Reuther P, Ricker K. Effect of immunosuppressive drugs (azathioprine). Ann N Y Acad Sci 1981; 377:691–699.
- Relling MV, Gardner EE, Sandborn WJ, et al; Clinical Pharmacogenetics Implementation Consortium. Clinical Pharmacogenetics Implementation Consortium guidelines for thiopurine methyltransferase genotype and thiopurine dosing. Clin Pharmacol Ther 2011; 89:387–391.
- Finelli PF. Primary CNS lymphoma in myasthenic on long-term azathioprine. J Neurooncol 2005; 74:91–92.
- Sanders DB, Hart IK, Mantegazza R, et al. An international, phase III, randomized trial of mycophenolate mofetil in myasthenia gravis. Neurology 2008; 71:400–406.
- Muscle Study Group. A trial of mycophenolate mofetil with prednisone as initial immunotherapy in myasthenia gravis. Neurology 2008; 71:394–399.
- Meriggioli MN, Ciafaloni E, Al-Hayk KA, et al. Mycophenolate mofetil for myasthenia gravis: an analysis of efficacy, safety, and tolerability. Neurology 2003; 61:1438–1440.
- Hehir MK, Burns TM, Alpers J, Conaway MR, Sawa M, Sanders DB. Mycophenolate mofetil in AChR-antibody-positive myasthenia gravis: outcomes in 102 patients. Muscle Nerve 2010; 41:593–598.
- Merlob P, Stahl B, Klinger G. Tetrada of the possible mycophenolate mofetil embryopathy: a review. Reprod Toxicol 2009; 28:105–108.
- Tindall RS, Rollins JA, Phillips JT, Greenlee RG, Wells L, Belendiuk G. Preliminary results of a double-blind, randomized, placebo-controlled trial of cyclosporine in myasthenia gravis. N Engl J Med 1987; 316:719–724.
- Heckmann JM, Rawoot A, Bateman K, Renison R, Badri M. A single-blinded trial of methotrexate versus azathioprine as steroid-sparing agents in generalized myasthenia gravis. BMC Neurol 2011; 11:97.
- Pasnoor M, He J, Herbelin L, Dimachkie M, Barohn RJ; Muscle Study Group. Phase II trial of methotrexate in myasthenia gravis. Ann N Y Acad Sci 2012; 1275:23–28.
- Díaz-Manera J, Martínez-Hernández E, Querol L, et al. Long-lasting treatment effect of rituximab in MuSK myasthenia. Neurology 2012; 78:189–193.
- Gronseth GS, Barohn RJ. Practice parameter: thymectomy for autoimmune myasthenia gravis (an evidence-based review): Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology 2000; 55:7–15.
- Kumar V, Kaminski HJ. Treatment of myasthenia gravis. Curr Neurol Neurosci Rep 2011; 11:89–96.
- Pompeo E, Tacconi F, Massa R, Mineo D, Nahmias S, Mineo TC. Long-term outcome of thoracoscopic extended thymectomy for nonthymomatous myasthenia gravis. Eur J Cardiothorac Surg 2009; 36:164–169.
Current therapies for myasthenia gravis can help most patients achieve sustained improvement. The overall prognosis has dramatically improved over the last 4 decades: the mortality rate used to be 75%; now it is 4.5%.1
Myasthenia gravis is the most common disorder of neuromuscular junction transmission and is also one of the best characterized autoimmune diseases. However, its symptoms—primarily weakness—vary from patient to patient, and in the same patient, by time of day and over longer time periods. The variation in symptoms can be very confusing to undiagnosed patients and puzzling to unsuspecting physicians. Such diagnostic uncertainty can give the patient additional frustration and emotional stress, which in turn exacerbate his or her condition.
In this review, we will give an overview of the pathogenesis, clinical manifestations, diagnosis, and treatment of myasthenia gravis.
TWO PEAKS IN INCIDENCE BY AGE
The annual incidence of myasthenia gravis is approximately 10 to 20 new cases per million, with a prevalence of about 150 to 200 per million.2
The age of onset has a bimodal distribution, with an early incidence peak in the second to third decade with a female predominance and a late peak in the 6th to the 8th decade with a male predominance.2
Myasthenia gravis is commonly associated with several other autoimmune disorders, including hypothyroidism, hyperthyroidism, systemic lupus erythematosus, rheumatoid arthritis, vitiligo, diabetes, and, more recently recognized, neuromyelitis optica.3
ANTIBODIES AGAINST AChR AND MuSK
In most cases of myasthenia gravis the patient has autoimmune antibodies against constituents of the neuromuscular junction, specifically acetylcholine receptor (AChR) and muscle-specific tyrosine kinase (MuSK) (Figure 1).
AChR antibody-positive myasthenia gravis
When antibodies bind to AChR on the postsynaptic membrane, they cross-link neighboring AChR units, which are absorbed into the muscle fiber and are broken up.4 In addition, the complement system is activated to mediate further damage on the postsynaptic membrane.
AChR antibodies may come from germinal centers of the thymus, where clustered myoid cells express AChR on the plasma membrane surface.5 About 60% of AChR antibody-positive myasthenia gravis patients have an enlarged thymus, and 10% have a thymoma—a tumor of the epithelial cells of this organ. Conversely, about 15% of patients with a thymoma have clinical myasthenia gravis, and an additional 20% possess antibodies against AChR in the serum without myasthenic symptoms.5
MuSK antibody-positive myasthenia gravis
Like AChR, MuSK is a transmembrane component of the postsynaptic neuromuscular junction. During formation of the neuromuscular junction, MuSK is activated through the binding of agrin (a nerve-derived proteoglycan) to lipoprotein-related protein 4 (LRP4), after which complicated intracellular signaling promotes the assembly and stabilization of AChR.6
Unlike AChR antibodies, antibodies against MuSK do not activate the complement system, and complement fixation is not essential for clinical myasthenic symptoms to appear.7 Also, myasthenia gravis with MuSK antibodies is rarely associated with thymoma.8
The precise mechanism by which MuSK antibody impairs transmission at the neuromuscular junction has been a mystery until recently. Animal models, including MuSK-mutant mice and mice injected with MuSK protein or with purified immunoglobulin G from patients with this disease, have revealed a significant reduction of AChR clusters and destruction of neuromuscular junction structures.7,9–12
In addition, MuSK antibodies produce pre-synaptic dysfunction, manifesting as a reduction of acetylcholine content. This information is based on studies in mice and on in vitro electrophysiologic analyses of neuromuscular junctions from a patient with this disease.7,9–13
Finally, MuSK antibodies may indirectly affect the recycling of acetylcholine. After post-synaptic activation, acetylcholine is normally hydrolized by acetylcholinesterase, which is located in the synaptic cleft but anchored to MuSK on the postsynaptic membrane. MuSK antibodies block the binding of MuSK to acetylcholinesterase, possibly leading to less accumulation of acetylcholinesterase.14 This process may explain why patients with MuSK antibody-positive myasthenia gravis tend to respond poorly to acetylcholinesterase inhibitors (more about this below).
Seronegative myasthenia gravis
In a series of 562 consecutive patients with generalized weakness due to myasthenia gravis, 92% were positive for AChR antibody, 3% were positive for MuSK antibody, and 5% were seronegative (possessing neither antibody).15 In contrast, about 50% of patients with purely ocular myasthenia gravis (ie, with isolated weakness of the levator palpebrae superioris, orbicularis oculi, or oculomotor muscles) are seropositive for AChR antibody. Only a few ocular MuSK antibody-positive cases have been described, leaving the rest seronegative. Rarely, both antibodies can be detected in the same patient.16
In patients who are negative for AChR antibodies at the time of disease onset, sero-conversion may occur later during the course. Repeating serologic testing 6 to 12 months later may detect AChR antibodies in approximately 15% of patients who were initially seronegative.15,17
The clinical presentation, electrophysiologic findings, thymic pathologic findings, and treatment responses are similar in AChR antibody-positive and seronegative myasthenia gravis.17 Muscle biopsy study in seronegative cases demonstrates a loss of AChR as well.18
Based on these observations, it has been proposed that seronegative patients may have low-affinity antibodies that can bind to tightly clustered AChRs on the postsynaptic membrane but escape detection by routine radioimmunoassays in a solution phase. With a sensitive cell-based immunofluorescence assay, low-affinity antibodies to clustered AChRs were detected in 66% of patients with generalized myasthenia gravis and in 50% of those with ocular myasthenia gravis who were seronegative on standard assays.19,20 These low-affinity AChR antibodies can also activate complement in vitro, increasing the likelihood that they are pathogenic. However, assays to detect low-affinity AChR antibodies are not yet commercially available.
Within the past year, three research groups independently reported detecting antibodies to LRP4 in 2% to 50% of seronegative myasthenia gravis patients. This wide variation in the prevalence of LRP4 antibodies could be related to patient ethnicity and methods of detection.21–23 LRP4 is a receptor for agrin and is required for agrin-induced MuSK activation and AChR clustering. LRP antibodies can activate complement; therefore, it is plausible that LRP4 antibody binding leads to AChR loss on the postsynaptic membrane. However, additional study is needed to determine if LRP4 antibodies are truly pathogenic in myasthenia gravis.
A DISORDER OF FATIGABLE WEAKNESS
Myasthenia gravis is a disorder of fatigable weakness producing fluctuating symptoms. Symptoms related to the involvement of specific muscle groups are listed in Table 1. Muscle weakness is often worse later in the day or after exercise.
Ocular myasthenia gravis accounts for about 15% of all cases. Of patients initially presenting with ocular symptoms only, twothirds will ultimately develop generalized symptoms, most within the first 2 years.24 No factor has been identified that predicts conversion from an ocular to a generalized form.
Several clinical phenotypes of MuSK antibody-positive myasthenia gravis have been described. An oculobulbar form presents with diplopia, ptosis, dysarthria, and profound atrophy of the muscles of the tongue and face. A restricted myopathic form presents with prominent neck, shoulder, and respiratory weakness without ocular involvement. A third form is a combination of ocular and proximal limb weakness, indistinguishable from AChR antibody-positive disease.25
MuSK antibody-positive patients do not respond as well to acetylcholinesterase inhibitors as AChR antibody-positive patients do. In one study, nearly 70% of MuSK antibody-positive patients demonstrated no response, poor tolerance, or cholinergic hypersensitivity to these agents.25 Fortunately, most MuSK antibody-positive patients have a favorable response to immunosuppressive therapy—sometimes a dramatic improvement after plasmapheresis.8
DIAGNOSIS OF MYASTHENIA GRAVIS
The common differential diagnoses for myasthenia gravis are listed in Table 2.
The essential feature of myasthenia gravis is fluctuating muscle weakness, often with fatigue. Many patients complain of weakness of specific muscle groups after their repeated usage. Pain is generally a less conspicuous symptom, and generalized fatigue without objective weakness is inconsistent with myasthenia gravis.
Signs of muscle weakness may include droopy eyelids, diplopia, inability to hold the head straight, difficulty swallowing or chewing, speech disturbances, difficulty breathing, and difficulty raising the arms or rising from the sitting position. A historical pattern of ptosis alternating from one eye to the other is fairly characteristic of myasthenia gravis.
The weakness of orbicularis oculi is easily identified on examination by prying open the eyes during forced eye closure. Limb weakness is usually more significant in the arms than in the legs. An often-neglected feature of myasthenia gravis is finger extensor weakness with a relative sparing of other distal hand muscles.2
The ice-pack test is performed by placing a small bag of ice over the ptotic eye for 2 to 5 minutes and assessing the degree of ptosis for any noticeable improvement. This test is not very helpful for assessing ocular motor weakness.
The edrophonium (Tensilon) test can be used for patients with ptosis or ophthalmoparesis. Edrophonium, a short-acting acetylcholinesterase inhibitor, is given intravenously while the patient is observed for objective improvement. The patient’s cardiovascular status should be monitored for arrhythmias and hypotension. Atropine should be immediately available in case severe bradycardia develops.
The ice-pack test and the edrophonium test can give false-negative and false-positive results, and the diagnosis of myasthenia gravis must be verified by other diagnostic tests.
Testing for antibodies
Testing for circulating AChR antibodies, MuSK antibodies, or both is the first step in the laboratory confirmation of myasthenia gravis.
There are three AChR antibody subtypes: binding, blocking, and modulating. Binding antibodies are present in 80% to 90% of patients with generalized myasthenia gravis and 50% of those with ocular myasthenia gravis. Testing for blocking and modulating AChR antibodies increases the sensitivity by less than 5% when added to testing for binding antibodies.
AChR antibody titers correlate poorly with disease severity between patients. However, in individual patients, antibody titers tend to go down in parallel with clinical improvement.
MuSK antibody is detected in nearly half of myasthenia gravis patients with generalized weakness who are negative for AChR antibody.
Electrophysiologic tests
Electrophysiologic tests can usually confirm the diagnosis of seronegative myasthenia gravis. They are also helpful in seropositive patients who have unusual clinical features or a poor response to treatment.
Repetitive nerve stimulation studies use a slow rate (2–5 Hz) of repetitive electrical stimulation. The study is positive if the motor response declines by more than 10%. However, a decremental response is not specific for myasthenia gravis, as it may be seen in other neuromuscular disorders such as motor neuron disease or Lambert-Eaton myasthenic syndrome.
This test is technically easier to do in distal muscles than in proximal muscles, but less sensitive. Therefore, proximal muscles such as the trapezius or facial muscles are usually also sampled to maximize the yield. To further maximize the sensitivity, muscles being tested should be warm, and acetylcholinesterase inhibitors should be withheld for 12 hours before.
Repetitive nerve stimulation studies in distal muscles are positive in approximately 75% of patients with generalized myasthenia gravis and in 30% with ocular myasthenia gravis.26
Single-fiber electromyography is more technically demanding than repetitive nerve stimulation and is less widely available. It is usually performed with a special needle electrode that can simultaneously identify action potentials arising from individual muscle fibers innervated by the same axon.
Variability in time of the second action potential relative to the first is called “jitter.” Abnormal jitter is seen in more than 95% of patients with generalized myasthenia gravis and in 85% to 90% of those with ocular myasthenia gravis.26,27 However, abnormal jitter can also be seen in other neuromuscular diseases such as motor neuron disease or in neuromuscular junctional disorders such as Lambert-Eaton myasthenic syndrome.
Imaging studies
Chest computed tomography or magnetic resonance imaging with contrast should be performed in all myasthenia gravis patients to look for a thymoma.
TREATMENT OF MYASTHENIA GRAVIS
Acetylcholinesterase inhibitors
As a reasonable first therapy in mild cases of myasthenia gravis, acetylcholinesterase inhibitors slow down the degradation of acetylcholine and prolong its effect in the neuromuscular junction, but they are not disease-modifying and their benefits are mild.
Pyridostigmine is the usual choice of acetylcholinesterase inhibitor. Its onset of action is rapid (15 to 30 minutes) and its action lasts for 3 to 4 hours. For most patients, the effective dosage range is 60 mg to 90 mg every 4 to 6 hours. A long-acting form is also available and can be given as a single nighttime dose.
Immunomodulating therapy
Patients who have moderate to severe symptoms require some form of immunomodulating therapy.
Plasmapheresis or intravenous immune globulin is reserved for patients with severe or rapidly worsening disease because their beneficial effects can be seen within the first week of treatment.
Longer-acting immunotherapies (corticosteroids, azathioprine, mycophenolate mofetil and others) have a slower onset of responses but provide sustained benefits. Which drug to use depends on factors such as comorbidity, side effects, and cost.
Drugs to avoid
A number of medications can exacerbate weakness in myasthenia gravis and should be avoided or used with caution. The list is long, but ones that deserve the most attention are penicillamine, interferons, procainamide, quinidine, and antibiotics, including quinolones and aminoglycosides. A more comprehensive list of medications that may exacerbate myasthenia gravis symptoms can be found in a review by Keesey.2
RAPID INDUCTION IMMUNOTHERAPIES : PLASMAPHERESIS, IMMUNE GLOBULIN
Both plasmapheresis and intravenous immune globulin act quickly over days, but in most patients their effects last only a few weeks. Both are used as rescue therapies for myasthenic crises, bridging therapy to slow-acting immunotherapeutic agents, or maintenance treatment for poorly controlled cases.
Several retrospective studies have confirmed the efficacy of plasmapheresis in more than 80% of patients with generalized symptoms.28,29
In a randomized trial in patients with generalized therapies, intravenous immune globulin improved muscle strength in the group of patients with severe symptoms.30 The effective dosage of intravenous immune globulin varies from 1 to 2 g/kg without observed difference between doses.31 Trials comparing the efficacy of intravenous immune globulin and plasmapheresis in acute and severe myasthenia gravis did not reveal a difference in efficacy.32,33 Intravenous immune globulin at a minimal dose of 0.4 g/kg every 3 months has been successfully used as a long-term maintenance monotherapy, and such a role could be expanded to more patients with further studies.34
The choice between plasmapheresis and intravenous immune globulin is often based on the ability of a patient to tolerate each treatment and on the availability of the plasmapheresis procedure. Intravenous immune globulin is easier to administer, is associated with fewer adverse events related to vascular access, and is therefore more appropriate than plasmapheresis in some centers.
CHRONIC MAINTENANCE IMMUNOMODULATING TREATMENT
Corticosteroids
Prednisone, the most commonly used agent, leads to remission or marked improvement in 70% to 80% of patients with ocular or generalized myasthenia gravis.35 It may also reduce the progression of ocular myasthenia gravis to the generalized form.36
The effective dose of prednisone depends on the severity and distribution of symptoms. Some patients may need up to 1.0 mg/kg/day (usually 50 to 80 mg per day). In patients with mild to moderate symptoms, a lower maximal dosage such as 20 to 40 mg per day can be sufficient.
Within 1 to 2 weeks after starting high-dose prednisone, up to 50% of patients may develop a transient deterioration, including possible precipitation of a myasthenic crisis.37 For this reason, high-dose prednisone is commonly started only in hospitalized patients who are also receiving plasmapheresis or intravenous immune globulin. Otherwise, an outpatient dose-escalation protocol can be used to achieve a target dose over several weeks.
Prednisone tapering can begin after the patient has been on the maximal dose for 1 to 2 months and significant improvement is evident. A monthly tapering of 5 to 10 mg is preferred, then more slowly after the daily dose reaches 30 mg. The usual maintenance dose averages about 5 mg daily.
Common side effects of prednisone include weight gain, cushingoid features, easy bruising, cataracts, glaucoma, hypertension, diabetes, dyslipidemia, and osteoporosis. Patients are advised to take supplemental calcium (1,500 mg per day) and vitamin D (400 to 800 IU per day). For those most at risk of osteoporosis, treatment with a bisphosphonate should be considered.
Other immunotherapeutic agents are often needed, either to replace the corticosteroid or to permit use of lower doses of it. Because of their delayed onset of action, starting such corticosteroid-sparing agents early in the course is often necessary. These agents are often initially combined with high-dose prednisone, with an eventual goal of weaning off prednisone entirely. This strategy offers the advantage of relatively rapid induction while avoiding the long-term adverse effects of corticosteroid treatment.
Azathioprine
Azathioprine doesn’t begin to show a beneficial effect in myasthenia gravis for 6 to 12 months, and it often reaches its maximal efficacy only after 1 to 2 years of treatment.38
In a study of 78 myasthenia gravis patients, 91% improved when treated with azathioprine alone or together with prednisone.39 In another study using azathioprine and prednisolone for generalized myasthenia gravis, nearly two-thirds of patients came off prednisolone while maintaining remission for 3 years.38
A typical maintenance dose is 2 to 3 mg/kg/day. Common side effects are nausea, vomiting, and malaise. Less frequent side effects include hematologic abnormalities, abnormal liver function, and pancreatitis. Monthly monitoring of complete blood cell counts and liver function tests is warranted for the first 6 months, then less often.
One in 300 people in the general population is homozygous for a mutant allele in the thiopurine methyltransferase (TPMT) gene. Patients with this genotype should not receive azathioprine because of the risk of life-threatening bone marrow suppression.40 A slightly increased risk of various forms of lymphoma has been documented.41
Mycophenolate mofetil
A well-tolerated medication with few side effects, mycophenolate mofetil is being used more in myasthenia gravis. The results of two recent randomized trials suggested that it is not effective in improving myasthenia gravis symptoms or sparing prednisone dosage when used for 90 days or 36 weeks.42,43 However, extensive clinical experience supports its longterm efficacy in myasthenia gravis.
In a retrospective study of 85 patients with generalized myasthenia gravis, mycophenolate at doses of 1 to 3 g daily improved symptoms in 73% and produced remission in 50%. Steroid dosage was reduced in 71% of patients.44
Another retrospective study, with 102 patients, verified a slow development of clinical benefit after months of mycophenolate therapy alone or in combination with prednisone. Approximately 50% of patients achieved a minimal manifestation status after 6 to 12 months of mycophenolate treatment. Eventually, at 24 months of treatment, 80% of patients had a desirable outcome of minimal clinical manifestation or better, 55% of patients were able to come off prednisone entirely, and 75% were taking less than 7.5 mg of prednisone per day.45
Common side effects of mycophenolate include nausea, diarrhea, and infections such as urinary tract infections and herpes reactivation. The complete blood cell count needs to be monitored frequently during the first 6 months of therapy. Leukopenia can occur but rarely necessitates stopping mycophenolate. Long-term safety data are lacking, but so far there has been no clearly increased risk of malignancy.
Mycophenolate exposure in pregnancy results in a high incidence of major fetal malformations. Therefore, its use in pregnant patients is discouraged, and women of child-bearing age should use effective contraception.46
Cyclosporine
A randomized trial in a small number of patients suggested that cyclosporine is fairly effective as monotherapy.47 Its onset of action in myasthenia gravis is faster than that of other corticosteroid-sparing agents, and clinical benefit can often be observed as early as 1 to 2 months. A dose of 5 mg/kg/day and a maintenance serum level of 100 to 150 ng/mL are generally recommended. However, renal, hepatic, and hematologic toxicities and interactions with other medications make cyclosporine a less attractive choice.
Methotrexate
A randomized trial evaluated the utility of methotrexate as a steroid-sparing agent compared with azathioprine.48 At 24 months, its steroid-sparing effect was similar to that of azathioprine, and the prednisone dosage had been reduced in more than 50% of patients.
Another phase II trial studying the efficacy of methotrexate in myasthenia gravis is under way.49
Rituximab
Rituximab is a monoclonal antibody against B-cell membrane marker CD20. A growing number of case series support its efficacy in patients with severe generalized myasthenia gravis refractory to multiple immunosuppressants.16,50 It seems particularly effective for MuSK antibody-positive disease, reducing MuSK antibody titers and having a treatment effect that lasts for years.
The standard dosage is 375 mg/m2 per week for 4 consecutive weeks. Peripheral B cells tend to be depleted within 2 weeks after the first infusion, while T-cell populations remain unchanged.50
A minimal infusion reaction such as flushing and chills can be seen with the first infusion. Patients may be more susceptible to certain infections such as reactivation of herpes zoster, but overall rituximab is well tolerated. Rare cases of progressive multifocal leukoencephalopathy have been reported in patients taking it, but none have occurred so far in myasthenia gravis treatment.
Cyclophosphamide
Cyclophosphamide is an alkylating agent that reduces proliferation of both B and T cells. It can be effective in myasthenia gravis, but potentially serious side effects limit its use. It should be reserved for the small percentage of cases that are refractory to other immunotherapies.
Thymectomy
Surgical treatment should be considered for patients with thymoma. If the tumor cannot be surgically resected, chemoradiotherapy can be considered for relief of myasthenic symptoms and for prevention of local invasion.
Thymomas recur in a minority of patients many years after the initial resection, sometimes without myasthenia gravis symptoms. A recurrence of symptoms does not necessarily indicate a recurrence of thymoma. The lack of correlation between myasthenia gravis symptoms and thymoma recurrence highlights the importance of radiologic follow-up in these patients.
For patients without thymoma, many experts believe that thymectomy is beneficial in patients under age 60 who have generalized myasthenia gravis. The likelihood of medication-free remission is about twice as high, and the likelihood of becoming asymptomatic is about one and a half times higher after thymectomy.51 However, it takes up to several years for the benefits of thymectomy to manifest, and thymectomy does not guarantee protection from developing AChR antibody-positive myasthenia gravis in the future.
The optimal timing of thymectomy is not well established; however, the procedure is usually recommended within the first 3 years of diagnosis.52 The response rates from thymectomy are similar for AChR antibody-positive and seronegative patients. In general, thymectomy for MuSK antibody-positive patients has not been effective, and its role in ocular myasthenia gravis is unclear.2,53
Current therapies for myasthenia gravis can help most patients achieve sustained improvement. The overall prognosis has dramatically improved over the last 4 decades: the mortality rate used to be 75%; now it is 4.5%.1
Myasthenia gravis is the most common disorder of neuromuscular junction transmission and is also one of the best characterized autoimmune diseases. However, its symptoms—primarily weakness—vary from patient to patient, and in the same patient, by time of day and over longer time periods. The variation in symptoms can be very confusing to undiagnosed patients and puzzling to unsuspecting physicians. Such diagnostic uncertainty can give the patient additional frustration and emotional stress, which in turn exacerbate his or her condition.
In this review, we will give an overview of the pathogenesis, clinical manifestations, diagnosis, and treatment of myasthenia gravis.
TWO PEAKS IN INCIDENCE BY AGE
The annual incidence of myasthenia gravis is approximately 10 to 20 new cases per million, with a prevalence of about 150 to 200 per million.2
The age of onset has a bimodal distribution, with an early incidence peak in the second to third decade with a female predominance and a late peak in the 6th to the 8th decade with a male predominance.2
Myasthenia gravis is commonly associated with several other autoimmune disorders, including hypothyroidism, hyperthyroidism, systemic lupus erythematosus, rheumatoid arthritis, vitiligo, diabetes, and, more recently recognized, neuromyelitis optica.3
ANTIBODIES AGAINST AChR AND MuSK
In most cases of myasthenia gravis the patient has autoimmune antibodies against constituents of the neuromuscular junction, specifically acetylcholine receptor (AChR) and muscle-specific tyrosine kinase (MuSK) (Figure 1).
AChR antibody-positive myasthenia gravis
When antibodies bind to AChR on the postsynaptic membrane, they cross-link neighboring AChR units, which are absorbed into the muscle fiber and are broken up.4 In addition, the complement system is activated to mediate further damage on the postsynaptic membrane.
AChR antibodies may come from germinal centers of the thymus, where clustered myoid cells express AChR on the plasma membrane surface.5 About 60% of AChR antibody-positive myasthenia gravis patients have an enlarged thymus, and 10% have a thymoma—a tumor of the epithelial cells of this organ. Conversely, about 15% of patients with a thymoma have clinical myasthenia gravis, and an additional 20% possess antibodies against AChR in the serum without myasthenic symptoms.5
MuSK antibody-positive myasthenia gravis
Like AChR, MuSK is a transmembrane component of the postsynaptic neuromuscular junction. During formation of the neuromuscular junction, MuSK is activated through the binding of agrin (a nerve-derived proteoglycan) to lipoprotein-related protein 4 (LRP4), after which complicated intracellular signaling promotes the assembly and stabilization of AChR.6
Unlike AChR antibodies, antibodies against MuSK do not activate the complement system, and complement fixation is not essential for clinical myasthenic symptoms to appear.7 Also, myasthenia gravis with MuSK antibodies is rarely associated with thymoma.8
The precise mechanism by which MuSK antibody impairs transmission at the neuromuscular junction has been a mystery until recently. Animal models, including MuSK-mutant mice and mice injected with MuSK protein or with purified immunoglobulin G from patients with this disease, have revealed a significant reduction of AChR clusters and destruction of neuromuscular junction structures.7,9–12
In addition, MuSK antibodies produce pre-synaptic dysfunction, manifesting as a reduction of acetylcholine content. This information is based on studies in mice and on in vitro electrophysiologic analyses of neuromuscular junctions from a patient with this disease.7,9–13
Finally, MuSK antibodies may indirectly affect the recycling of acetylcholine. After post-synaptic activation, acetylcholine is normally hydrolized by acetylcholinesterase, which is located in the synaptic cleft but anchored to MuSK on the postsynaptic membrane. MuSK antibodies block the binding of MuSK to acetylcholinesterase, possibly leading to less accumulation of acetylcholinesterase.14 This process may explain why patients with MuSK antibody-positive myasthenia gravis tend to respond poorly to acetylcholinesterase inhibitors (more about this below).
Seronegative myasthenia gravis
In a series of 562 consecutive patients with generalized weakness due to myasthenia gravis, 92% were positive for AChR antibody, 3% were positive for MuSK antibody, and 5% were seronegative (possessing neither antibody).15 In contrast, about 50% of patients with purely ocular myasthenia gravis (ie, with isolated weakness of the levator palpebrae superioris, orbicularis oculi, or oculomotor muscles) are seropositive for AChR antibody. Only a few ocular MuSK antibody-positive cases have been described, leaving the rest seronegative. Rarely, both antibodies can be detected in the same patient.16
In patients who are negative for AChR antibodies at the time of disease onset, sero-conversion may occur later during the course. Repeating serologic testing 6 to 12 months later may detect AChR antibodies in approximately 15% of patients who were initially seronegative.15,17
The clinical presentation, electrophysiologic findings, thymic pathologic findings, and treatment responses are similar in AChR antibody-positive and seronegative myasthenia gravis.17 Muscle biopsy study in seronegative cases demonstrates a loss of AChR as well.18
Based on these observations, it has been proposed that seronegative patients may have low-affinity antibodies that can bind to tightly clustered AChRs on the postsynaptic membrane but escape detection by routine radioimmunoassays in a solution phase. With a sensitive cell-based immunofluorescence assay, low-affinity antibodies to clustered AChRs were detected in 66% of patients with generalized myasthenia gravis and in 50% of those with ocular myasthenia gravis who were seronegative on standard assays.19,20 These low-affinity AChR antibodies can also activate complement in vitro, increasing the likelihood that they are pathogenic. However, assays to detect low-affinity AChR antibodies are not yet commercially available.
Within the past year, three research groups independently reported detecting antibodies to LRP4 in 2% to 50% of seronegative myasthenia gravis patients. This wide variation in the prevalence of LRP4 antibodies could be related to patient ethnicity and methods of detection.21–23 LRP4 is a receptor for agrin and is required for agrin-induced MuSK activation and AChR clustering. LRP antibodies can activate complement; therefore, it is plausible that LRP4 antibody binding leads to AChR loss on the postsynaptic membrane. However, additional study is needed to determine if LRP4 antibodies are truly pathogenic in myasthenia gravis.
A DISORDER OF FATIGABLE WEAKNESS
Myasthenia gravis is a disorder of fatigable weakness producing fluctuating symptoms. Symptoms related to the involvement of specific muscle groups are listed in Table 1. Muscle weakness is often worse later in the day or after exercise.
Ocular myasthenia gravis accounts for about 15% of all cases. Of patients initially presenting with ocular symptoms only, twothirds will ultimately develop generalized symptoms, most within the first 2 years.24 No factor has been identified that predicts conversion from an ocular to a generalized form.
Several clinical phenotypes of MuSK antibody-positive myasthenia gravis have been described. An oculobulbar form presents with diplopia, ptosis, dysarthria, and profound atrophy of the muscles of the tongue and face. A restricted myopathic form presents with prominent neck, shoulder, and respiratory weakness without ocular involvement. A third form is a combination of ocular and proximal limb weakness, indistinguishable from AChR antibody-positive disease.25
MuSK antibody-positive patients do not respond as well to acetylcholinesterase inhibitors as AChR antibody-positive patients do. In one study, nearly 70% of MuSK antibody-positive patients demonstrated no response, poor tolerance, or cholinergic hypersensitivity to these agents.25 Fortunately, most MuSK antibody-positive patients have a favorable response to immunosuppressive therapy—sometimes a dramatic improvement after plasmapheresis.8
DIAGNOSIS OF MYASTHENIA GRAVIS
The common differential diagnoses for myasthenia gravis are listed in Table 2.
The essential feature of myasthenia gravis is fluctuating muscle weakness, often with fatigue. Many patients complain of weakness of specific muscle groups after their repeated usage. Pain is generally a less conspicuous symptom, and generalized fatigue without objective weakness is inconsistent with myasthenia gravis.
Signs of muscle weakness may include droopy eyelids, diplopia, inability to hold the head straight, difficulty swallowing or chewing, speech disturbances, difficulty breathing, and difficulty raising the arms or rising from the sitting position. A historical pattern of ptosis alternating from one eye to the other is fairly characteristic of myasthenia gravis.
The weakness of orbicularis oculi is easily identified on examination by prying open the eyes during forced eye closure. Limb weakness is usually more significant in the arms than in the legs. An often-neglected feature of myasthenia gravis is finger extensor weakness with a relative sparing of other distal hand muscles.2
The ice-pack test is performed by placing a small bag of ice over the ptotic eye for 2 to 5 minutes and assessing the degree of ptosis for any noticeable improvement. This test is not very helpful for assessing ocular motor weakness.
The edrophonium (Tensilon) test can be used for patients with ptosis or ophthalmoparesis. Edrophonium, a short-acting acetylcholinesterase inhibitor, is given intravenously while the patient is observed for objective improvement. The patient’s cardiovascular status should be monitored for arrhythmias and hypotension. Atropine should be immediately available in case severe bradycardia develops.
The ice-pack test and the edrophonium test can give false-negative and false-positive results, and the diagnosis of myasthenia gravis must be verified by other diagnostic tests.
Testing for antibodies
Testing for circulating AChR antibodies, MuSK antibodies, or both is the first step in the laboratory confirmation of myasthenia gravis.
There are three AChR antibody subtypes: binding, blocking, and modulating. Binding antibodies are present in 80% to 90% of patients with generalized myasthenia gravis and 50% of those with ocular myasthenia gravis. Testing for blocking and modulating AChR antibodies increases the sensitivity by less than 5% when added to testing for binding antibodies.
AChR antibody titers correlate poorly with disease severity between patients. However, in individual patients, antibody titers tend to go down in parallel with clinical improvement.
MuSK antibody is detected in nearly half of myasthenia gravis patients with generalized weakness who are negative for AChR antibody.
Electrophysiologic tests
Electrophysiologic tests can usually confirm the diagnosis of seronegative myasthenia gravis. They are also helpful in seropositive patients who have unusual clinical features or a poor response to treatment.
Repetitive nerve stimulation studies use a slow rate (2–5 Hz) of repetitive electrical stimulation. The study is positive if the motor response declines by more than 10%. However, a decremental response is not specific for myasthenia gravis, as it may be seen in other neuromuscular disorders such as motor neuron disease or Lambert-Eaton myasthenic syndrome.
This test is technically easier to do in distal muscles than in proximal muscles, but less sensitive. Therefore, proximal muscles such as the trapezius or facial muscles are usually also sampled to maximize the yield. To further maximize the sensitivity, muscles being tested should be warm, and acetylcholinesterase inhibitors should be withheld for 12 hours before.
Repetitive nerve stimulation studies in distal muscles are positive in approximately 75% of patients with generalized myasthenia gravis and in 30% with ocular myasthenia gravis.26
Single-fiber electromyography is more technically demanding than repetitive nerve stimulation and is less widely available. It is usually performed with a special needle electrode that can simultaneously identify action potentials arising from individual muscle fibers innervated by the same axon.
Variability in time of the second action potential relative to the first is called “jitter.” Abnormal jitter is seen in more than 95% of patients with generalized myasthenia gravis and in 85% to 90% of those with ocular myasthenia gravis.26,27 However, abnormal jitter can also be seen in other neuromuscular diseases such as motor neuron disease or in neuromuscular junctional disorders such as Lambert-Eaton myasthenic syndrome.
Imaging studies
Chest computed tomography or magnetic resonance imaging with contrast should be performed in all myasthenia gravis patients to look for a thymoma.
TREATMENT OF MYASTHENIA GRAVIS
Acetylcholinesterase inhibitors
As a reasonable first therapy in mild cases of myasthenia gravis, acetylcholinesterase inhibitors slow down the degradation of acetylcholine and prolong its effect in the neuromuscular junction, but they are not disease-modifying and their benefits are mild.
Pyridostigmine is the usual choice of acetylcholinesterase inhibitor. Its onset of action is rapid (15 to 30 minutes) and its action lasts for 3 to 4 hours. For most patients, the effective dosage range is 60 mg to 90 mg every 4 to 6 hours. A long-acting form is also available and can be given as a single nighttime dose.
Immunomodulating therapy
Patients who have moderate to severe symptoms require some form of immunomodulating therapy.
Plasmapheresis or intravenous immune globulin is reserved for patients with severe or rapidly worsening disease because their beneficial effects can be seen within the first week of treatment.
Longer-acting immunotherapies (corticosteroids, azathioprine, mycophenolate mofetil and others) have a slower onset of responses but provide sustained benefits. Which drug to use depends on factors such as comorbidity, side effects, and cost.
Drugs to avoid
A number of medications can exacerbate weakness in myasthenia gravis and should be avoided or used with caution. The list is long, but ones that deserve the most attention are penicillamine, interferons, procainamide, quinidine, and antibiotics, including quinolones and aminoglycosides. A more comprehensive list of medications that may exacerbate myasthenia gravis symptoms can be found in a review by Keesey.2
RAPID INDUCTION IMMUNOTHERAPIES : PLASMAPHERESIS, IMMUNE GLOBULIN
Both plasmapheresis and intravenous immune globulin act quickly over days, but in most patients their effects last only a few weeks. Both are used as rescue therapies for myasthenic crises, bridging therapy to slow-acting immunotherapeutic agents, or maintenance treatment for poorly controlled cases.
Several retrospective studies have confirmed the efficacy of plasmapheresis in more than 80% of patients with generalized symptoms.28,29
In a randomized trial in patients with generalized therapies, intravenous immune globulin improved muscle strength in the group of patients with severe symptoms.30 The effective dosage of intravenous immune globulin varies from 1 to 2 g/kg without observed difference between doses.31 Trials comparing the efficacy of intravenous immune globulin and plasmapheresis in acute and severe myasthenia gravis did not reveal a difference in efficacy.32,33 Intravenous immune globulin at a minimal dose of 0.4 g/kg every 3 months has been successfully used as a long-term maintenance monotherapy, and such a role could be expanded to more patients with further studies.34
The choice between plasmapheresis and intravenous immune globulin is often based on the ability of a patient to tolerate each treatment and on the availability of the plasmapheresis procedure. Intravenous immune globulin is easier to administer, is associated with fewer adverse events related to vascular access, and is therefore more appropriate than plasmapheresis in some centers.
CHRONIC MAINTENANCE IMMUNOMODULATING TREATMENT
Corticosteroids
Prednisone, the most commonly used agent, leads to remission or marked improvement in 70% to 80% of patients with ocular or generalized myasthenia gravis.35 It may also reduce the progression of ocular myasthenia gravis to the generalized form.36
The effective dose of prednisone depends on the severity and distribution of symptoms. Some patients may need up to 1.0 mg/kg/day (usually 50 to 80 mg per day). In patients with mild to moderate symptoms, a lower maximal dosage such as 20 to 40 mg per day can be sufficient.
Within 1 to 2 weeks after starting high-dose prednisone, up to 50% of patients may develop a transient deterioration, including possible precipitation of a myasthenic crisis.37 For this reason, high-dose prednisone is commonly started only in hospitalized patients who are also receiving plasmapheresis or intravenous immune globulin. Otherwise, an outpatient dose-escalation protocol can be used to achieve a target dose over several weeks.
Prednisone tapering can begin after the patient has been on the maximal dose for 1 to 2 months and significant improvement is evident. A monthly tapering of 5 to 10 mg is preferred, then more slowly after the daily dose reaches 30 mg. The usual maintenance dose averages about 5 mg daily.
Common side effects of prednisone include weight gain, cushingoid features, easy bruising, cataracts, glaucoma, hypertension, diabetes, dyslipidemia, and osteoporosis. Patients are advised to take supplemental calcium (1,500 mg per day) and vitamin D (400 to 800 IU per day). For those most at risk of osteoporosis, treatment with a bisphosphonate should be considered.
Other immunotherapeutic agents are often needed, either to replace the corticosteroid or to permit use of lower doses of it. Because of their delayed onset of action, starting such corticosteroid-sparing agents early in the course is often necessary. These agents are often initially combined with high-dose prednisone, with an eventual goal of weaning off prednisone entirely. This strategy offers the advantage of relatively rapid induction while avoiding the long-term adverse effects of corticosteroid treatment.
Azathioprine
Azathioprine doesn’t begin to show a beneficial effect in myasthenia gravis for 6 to 12 months, and it often reaches its maximal efficacy only after 1 to 2 years of treatment.38
In a study of 78 myasthenia gravis patients, 91% improved when treated with azathioprine alone or together with prednisone.39 In another study using azathioprine and prednisolone for generalized myasthenia gravis, nearly two-thirds of patients came off prednisolone while maintaining remission for 3 years.38
A typical maintenance dose is 2 to 3 mg/kg/day. Common side effects are nausea, vomiting, and malaise. Less frequent side effects include hematologic abnormalities, abnormal liver function, and pancreatitis. Monthly monitoring of complete blood cell counts and liver function tests is warranted for the first 6 months, then less often.
One in 300 people in the general population is homozygous for a mutant allele in the thiopurine methyltransferase (TPMT) gene. Patients with this genotype should not receive azathioprine because of the risk of life-threatening bone marrow suppression.40 A slightly increased risk of various forms of lymphoma has been documented.41
Mycophenolate mofetil
A well-tolerated medication with few side effects, mycophenolate mofetil is being used more in myasthenia gravis. The results of two recent randomized trials suggested that it is not effective in improving myasthenia gravis symptoms or sparing prednisone dosage when used for 90 days or 36 weeks.42,43 However, extensive clinical experience supports its longterm efficacy in myasthenia gravis.
In a retrospective study of 85 patients with generalized myasthenia gravis, mycophenolate at doses of 1 to 3 g daily improved symptoms in 73% and produced remission in 50%. Steroid dosage was reduced in 71% of patients.44
Another retrospective study, with 102 patients, verified a slow development of clinical benefit after months of mycophenolate therapy alone or in combination with prednisone. Approximately 50% of patients achieved a minimal manifestation status after 6 to 12 months of mycophenolate treatment. Eventually, at 24 months of treatment, 80% of patients had a desirable outcome of minimal clinical manifestation or better, 55% of patients were able to come off prednisone entirely, and 75% were taking less than 7.5 mg of prednisone per day.45
Common side effects of mycophenolate include nausea, diarrhea, and infections such as urinary tract infections and herpes reactivation. The complete blood cell count needs to be monitored frequently during the first 6 months of therapy. Leukopenia can occur but rarely necessitates stopping mycophenolate. Long-term safety data are lacking, but so far there has been no clearly increased risk of malignancy.
Mycophenolate exposure in pregnancy results in a high incidence of major fetal malformations. Therefore, its use in pregnant patients is discouraged, and women of child-bearing age should use effective contraception.46
Cyclosporine
A randomized trial in a small number of patients suggested that cyclosporine is fairly effective as monotherapy.47 Its onset of action in myasthenia gravis is faster than that of other corticosteroid-sparing agents, and clinical benefit can often be observed as early as 1 to 2 months. A dose of 5 mg/kg/day and a maintenance serum level of 100 to 150 ng/mL are generally recommended. However, renal, hepatic, and hematologic toxicities and interactions with other medications make cyclosporine a less attractive choice.
Methotrexate
A randomized trial evaluated the utility of methotrexate as a steroid-sparing agent compared with azathioprine.48 At 24 months, its steroid-sparing effect was similar to that of azathioprine, and the prednisone dosage had been reduced in more than 50% of patients.
Another phase II trial studying the efficacy of methotrexate in myasthenia gravis is under way.49
Rituximab
Rituximab is a monoclonal antibody against B-cell membrane marker CD20. A growing number of case series support its efficacy in patients with severe generalized myasthenia gravis refractory to multiple immunosuppressants.16,50 It seems particularly effective for MuSK antibody-positive disease, reducing MuSK antibody titers and having a treatment effect that lasts for years.
The standard dosage is 375 mg/m2 per week for 4 consecutive weeks. Peripheral B cells tend to be depleted within 2 weeks after the first infusion, while T-cell populations remain unchanged.50
A minimal infusion reaction such as flushing and chills can be seen with the first infusion. Patients may be more susceptible to certain infections such as reactivation of herpes zoster, but overall rituximab is well tolerated. Rare cases of progressive multifocal leukoencephalopathy have been reported in patients taking it, but none have occurred so far in myasthenia gravis treatment.
Cyclophosphamide
Cyclophosphamide is an alkylating agent that reduces proliferation of both B and T cells. It can be effective in myasthenia gravis, but potentially serious side effects limit its use. It should be reserved for the small percentage of cases that are refractory to other immunotherapies.
Thymectomy
Surgical treatment should be considered for patients with thymoma. If the tumor cannot be surgically resected, chemoradiotherapy can be considered for relief of myasthenic symptoms and for prevention of local invasion.
Thymomas recur in a minority of patients many years after the initial resection, sometimes without myasthenia gravis symptoms. A recurrence of symptoms does not necessarily indicate a recurrence of thymoma. The lack of correlation between myasthenia gravis symptoms and thymoma recurrence highlights the importance of radiologic follow-up in these patients.
For patients without thymoma, many experts believe that thymectomy is beneficial in patients under age 60 who have generalized myasthenia gravis. The likelihood of medication-free remission is about twice as high, and the likelihood of becoming asymptomatic is about one and a half times higher after thymectomy.51 However, it takes up to several years for the benefits of thymectomy to manifest, and thymectomy does not guarantee protection from developing AChR antibody-positive myasthenia gravis in the future.
The optimal timing of thymectomy is not well established; however, the procedure is usually recommended within the first 3 years of diagnosis.52 The response rates from thymectomy are similar for AChR antibody-positive and seronegative patients. In general, thymectomy for MuSK antibody-positive patients has not been effective, and its role in ocular myasthenia gravis is unclear.2,53
- Alshekhlee A, Miles JD, Katirji B, Preston DC, Kaminski HJ. Incidence and mortality rates of myasthenia gravis and myasthenic crisis in US hospitals. Neurology 2009; 72:1548–1554.
- Keesey JC. Clinical evaluation and management of myasthenia gravis. Muscle Nerve 2004; 29:484–505.
- Leite MI, Coutinho E, Lana-Peixoto M, et al. Myasthenia gravis and neuromyelitis optica spectrum disorder: a multicenter study of 16 patients. Neurology 2012; 78:1601–1607.
- Drachman DB, Angus CW, Adams RN, Michelson JD, Hoffman GJ. Myasthenic antibodies cross-link acetylcholine receptors to accelerate degradation. N Engl J Med 1978; 298:1116–1122.
- Fujii Y. The thymus, thymoma and myasthenia gravis. Surg Today 2013; 43:461–466.
- Evoli A, Lindstrom J. Myasthenia gravis with antibodies to MuSK: another step toward solving mystery? Neurology 2011; 77:1783–1784.
- Mori S, Kubo S, Akiyoshi T, et al. Antibodies against muscle-specific kinase impair both presynaptic and postsynaptic functions in a murine model of myasthenia gravis. Am J Pathol 2012; 180:798–810.
- Guptill JT, Sanders DB, Evoli A. Anti-MuSK antibody myasthenia gravis: clinical findings and response to treatment in two large cohorts. Muscle Nerve 2011; 44:36–40.
- Chevessier F, Girard E, Molgó J, et al. A mouse model for congenital myasthenic syndrome due to MuSK mutations reveals defects in structure and function of neuromuscular junctions. Hum Mol Genet 2008; 17:3577–3595.
- Richman DP, Nishi K, Morell SW, et al. Acute severe animal model of anti-muscle-specific kinase myasthenia: combined postsynaptic and presynaptic changes. Arch Neurol 2012; 69:453–460.
- Klooster R, Plomp JJ, Huijbers MG, et al. Muscle-specific kinase myasthenia gravis IgG4 autoantibodies cause severe neuromuscular junction dysfunction in mice. Brain 2012; 135:1081–1101.
- Viegas S, Jacobson L, Waters P, et al. Passive and active immunization models of MuSK-Ab positive myasthenia: electrophysiological evidence for pre and postsynaptic defects. Exp Neurol 2012; 234:506–512.
- Niks EH, Kuks JB, Wokke JH, et al. Pre- and postsynaptic neuromuscular junction abnormalities in musk myasthenia. Muscle Nerve 2010; 42:283–288.
- Kawakami Y, Ito M, Hirayama M, et al. Anti-MuSK autoantibodies block binding of collagen Q to MuSK. Neurology 2011; 77:1819–1826.
- Chan KH, Lachance DH, Harper CM, Lennon VA. Frequency of seronegativity in adult-acquired generalized myasthenia gravis. Muscle Nerve 2007; 36:651–658.
- Collongues N, Casez O, Lacour A, et al. Rituximab in refractory and non-refractory myasthenia: a retrospective multicenter study. Muscle Nerve 2012; 46:687–691.
- Sanders DB, Andrews PI, Howard JF, Massey JM. Seronegative myasthenia gravis. Neurology 1997; 48(suppl 5):40S–45S.
- Shiraishi H, Motomura M, Yoshimura T, et al. Acetylcholine receptors loss and postsynaptic damage in MuSK antibody-positive myasthenia gravis. Ann Neurol 2005; 57:289–293.
- Leite MI, Jacob S, Viegas S, et al. IgG1 antibodies to acetylcholine receptors in ‘seronegative’ myasthenia gravis. Brain 2008; 131:1940–1952.
- Jacob S, Viegas S, Leite MI, et al. Presence and pathogenic relevance of antibodies to clustered acetylcholine receptor in ocular and generalized myasthenia gravis. Arch Neurol 2012; 69:994–1001.
- Higuchi O, Hamuro J, Motomura M, Yamanashi Y. Autoantibodies to low-density lipoprotein receptor-related protein 4 in myasthenia gravis. Ann Neurol 2011; 69:418–422.
- Pevzner A, Schoser B, Peters K, et al. Anti-LRP4 autoantibodies in AChR- and MuSK-antibody-negative myasthenia gravis. J Neurol 2012; 259:427–435.
- Zhang B, Tzartos JS, Belimezi M, et al. Autoantibodies to lipoprotein-related protein 4 in patients with double-seronegative myasthenia gravis. Arch Neurol 2012; 69:445–451.
- Kupersmith MJ, Latkany R, Homel P. Development of generalized disease at 2 years in patients with ocular myasthenia gravis. Arch Neurol 2003; 60:243–248.
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- Alshekhlee A, Miles JD, Katirji B, Preston DC, Kaminski HJ. Incidence and mortality rates of myasthenia gravis and myasthenic crisis in US hospitals. Neurology 2009; 72:1548–1554.
- Keesey JC. Clinical evaluation and management of myasthenia gravis. Muscle Nerve 2004; 29:484–505.
- Leite MI, Coutinho E, Lana-Peixoto M, et al. Myasthenia gravis and neuromyelitis optica spectrum disorder: a multicenter study of 16 patients. Neurology 2012; 78:1601–1607.
- Drachman DB, Angus CW, Adams RN, Michelson JD, Hoffman GJ. Myasthenic antibodies cross-link acetylcholine receptors to accelerate degradation. N Engl J Med 1978; 298:1116–1122.
- Fujii Y. The thymus, thymoma and myasthenia gravis. Surg Today 2013; 43:461–466.
- Evoli A, Lindstrom J. Myasthenia gravis with antibodies to MuSK: another step toward solving mystery? Neurology 2011; 77:1783–1784.
- Mori S, Kubo S, Akiyoshi T, et al. Antibodies against muscle-specific kinase impair both presynaptic and postsynaptic functions in a murine model of myasthenia gravis. Am J Pathol 2012; 180:798–810.
- Guptill JT, Sanders DB, Evoli A. Anti-MuSK antibody myasthenia gravis: clinical findings and response to treatment in two large cohorts. Muscle Nerve 2011; 44:36–40.
- Chevessier F, Girard E, Molgó J, et al. A mouse model for congenital myasthenic syndrome due to MuSK mutations reveals defects in structure and function of neuromuscular junctions. Hum Mol Genet 2008; 17:3577–3595.
- Richman DP, Nishi K, Morell SW, et al. Acute severe animal model of anti-muscle-specific kinase myasthenia: combined postsynaptic and presynaptic changes. Arch Neurol 2012; 69:453–460.
- Klooster R, Plomp JJ, Huijbers MG, et al. Muscle-specific kinase myasthenia gravis IgG4 autoantibodies cause severe neuromuscular junction dysfunction in mice. Brain 2012; 135:1081–1101.
- Viegas S, Jacobson L, Waters P, et al. Passive and active immunization models of MuSK-Ab positive myasthenia: electrophysiological evidence for pre and postsynaptic defects. Exp Neurol 2012; 234:506–512.
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KEY POINTS
- In most cases of myasthenia gravis, the patient has antibodies against acetylcholine receptor (AChR) or musclespecific tyrosine kinase (MuSK).
- Myasthenia gravis is diagnosed by clinical signs, bedside tests (the ice-pack test or the edrophonium test), serologic tests for AChR antibodies or MuSK antibodies, and electrophysiologic tests.
- Acetylcholinesterase inhibitors are the first-step therapy, but patients who have moderate to severe symptoms require some form of immunomodulating therapy.
- A number of drugs can exacerbate weakness in myasthenia gravis and should be avoided or used with caution. These include penicillamine, interferons, procainamide, quinidine, and antibiotics such as quinolones and aminoglycosides.