Clinical question: How often do inpatient providers report direct communication with outpatient providers, and how is direct communication associated with 30-day readmissions?

Background: Studies have demonstrated that adverse events and errors occurring after hospital discharge can result from poor provider communication between the inpatient and outpatient setting.

Study design: Prospective cohort.

Setting: Johns Hopkins Hospital, Baltimore.

Synopsis: The presence or absence of direct communication between inpatient and outpatient healthcare providers was captured from a required field in an electronic discharge worksheet completed by the discharging physician. Of 6,635 hospitalizations studied, successful direct communication was reported in 36.7% of cases. Predictors of successful direct communication included patients cared for by hospitalists without house staff (OR 1.81, 95% CI 1.59-2.08), high expected 30-day readmission rate (OR 1.18, 95% CI 1.10-1.28), and insurance by Medicare (OR 1.35, 95% CI 1.16-1.56) and private insurance companies (OR 1.35, 95% CI 1.18-1.56). In adjusted analyses, direct communication between the inpatient and outpatient providers was not associated with 30-day readmissions (OR 1.08, 95% CI 0.92-1.26).

There were several limitations in this study. Only the primary team was surveyed; thus, it is not known if consulting providers might have contacted the outpatient providers. Only readmissions to the same medical center were studied, and therefore it is not known if patients were readmitted to other facilities. Additionally, information regarding discharge communication was self-reported, which might have introduced bias.

Bottom line: Self-reported direct communication between inpatient and outpatient providers occurred infrequently and was not associated with 30-day same-hospital readmission.

Citation: Oduyebo I, Lehmann C, Pollack C, et al. Association of self-reported hospital discharge handoffs with 30-day readmissions. JAMA Intern Med. 2013;173:624-629.

Clinical question: How often do inpatient providers report direct communication with outpatient providers, and how is direct communication associated with 30-day readmissions?

Background: Studies have demonstrated that adverse events and errors occurring after hospital discharge can result from poor provider communication between the inpatient and outpatient setting.

Study design: Prospective cohort.

Setting: Johns Hopkins Hospital, Baltimore.

Synopsis: The presence or absence of direct communication between inpatient and outpatient healthcare providers was captured from a required field in an electronic discharge worksheet completed by the discharging physician. Of 6,635 hospitalizations studied, successful direct communication was reported in 36.7% of cases. Predictors of successful direct communication included patients cared for by hospitalists without house staff (OR 1.81, 95% CI 1.59-2.08), high expected 30-day readmission rate (OR 1.18, 95% CI 1.10-1.28), and insurance by Medicare (OR 1.35, 95% CI 1.16-1.56) and private insurance companies (OR 1.35, 95% CI 1.18-1.56). In adjusted analyses, direct communication between the inpatient and outpatient providers was not associated with 30-day readmissions (OR 1.08, 95% CI 0.92-1.26).

There were several limitations in this study. Only the primary team was surveyed; thus, it is not known if consulting providers might have contacted the outpatient providers. Only readmissions to the same medical center were studied, and therefore it is not known if patients were readmitted to other facilities. Additionally, information regarding discharge communication was self-reported, which might have introduced bias.

Bottom line: Self-reported direct communication between inpatient and outpatient providers occurred infrequently and was not associated with 30-day same-hospital readmission.

Citation: Oduyebo I, Lehmann C, Pollack C, et al. Association of self-reported hospital discharge handoffs with 30-day readmissions. JAMA Intern Med. 2013;173:624-629.

Clinical question: How often do inpatient providers report direct communication with outpatient providers, and how is direct communication associated with 30-day readmissions?

Background: Studies have demonstrated that adverse events and errors occurring after hospital discharge can result from poor provider communication between the inpatient and outpatient setting.

Study design: Prospective cohort.

Setting: Johns Hopkins Hospital, Baltimore.

Synopsis: The presence or absence of direct communication between inpatient and outpatient healthcare providers was captured from a required field in an electronic discharge worksheet completed by the discharging physician. Of 6,635 hospitalizations studied, successful direct communication was reported in 36.7% of cases. Predictors of successful direct communication included patients cared for by hospitalists without house staff (OR 1.81, 95% CI 1.59-2.08), high expected 30-day readmission rate (OR 1.18, 95% CI 1.10-1.28), and insurance by Medicare (OR 1.35, 95% CI 1.16-1.56) and private insurance companies (OR 1.35, 95% CI 1.18-1.56). In adjusted analyses, direct communication between the inpatient and outpatient providers was not associated with 30-day readmissions (OR 1.08, 95% CI 0.92-1.26).

There were several limitations in this study. Only the primary team was surveyed; thus, it is not known if consulting providers might have contacted the outpatient providers. Only readmissions to the same medical center were studied, and therefore it is not known if patients were readmitted to other facilities. Additionally, information regarding discharge communication was self-reported, which might have introduced bias.

Bottom line: Self-reported direct communication between inpatient and outpatient providers occurred infrequently and was not associated with 30-day same-hospital readmission.

Citation: Oduyebo I, Lehmann C, Pollack C, et al. Association of self-reported hospital discharge handoffs with 30-day readmissions. JAMA Intern Med. 2013;173:624-629.

Clinical question: In patients who undergo inferior vena cava (IVC) filter placement for venous thromboembolism (VTE) prophylaxis or treatment, what are the associated patient characteristics, indications for placement, complications, retrieval date, and use of concomitant anticoagulant therapy?

Background: Retrievable IVC filters were designed to provide short-term protection from pulmonary embolism but are often left in place indefinitely. Retrievable IVC filters that are not removed can carry significant long-term risks. Further, the use of filters for VTE prophylaxis is controversial, and there are multiple sets of conflicting guidelines for filter insertion provided by various professional groups.

Study design: Retrospective review of IVC filter use over an eight-year period.

Setting: Boston Medical Center.

Synopsis: Medical records from all patients at Boston Medical Center who had a billing code for placement of an IVC filter between August 2003 and February 2011 were manually reviewed. Nine hundred fifty-two medical records were evaluated, of which 679 (71.3%) patients had retrievable IVC filters placed. The most common indications for filter placement were trauma (50.2%), malignancy (15.9%), and bleeding during anticoagulation (11.8%).

In total, 448 patients (47.1%) had filters placed for prophylactic purposes in the absence of documented VTE. Seventy-four patients developed VTE after filter placement; 48.2% of post-filter insertion VTEs occurred in patients who had no VTE prior to the filter; and 89.4% occurred in patients not receiving anticoagulants. An attempt was made to remove 71 of 679 (10.5%) retrievable filters, and 58 (8.5%) attempts were successful. There were 10 serious complications related to mechanical filter failure, including migration or fracture of the filter.

In this study, there was a high volume of filter use by the trauma service; thus, the patient population might be different from the hospital medicine patient population. The study also lacked systematic imaging and follow-up data. Further studies are needed to analyze the risks associated with IVC filter placement.

Bottom line: Use of IVC filters for VTE treatment and prophylaxis, in the context of low filter retrieval rates and lack of appropriate anticoagulant therapy, results in suboptimal outcomes.

Citation: Sarosiek S, Crowther M, Sloan M. Indications, complications, and management of inferior vena cava filters: the experience in 952 patients at an academic hospital with a level I trauma center. JAMA Intern Med. 2013;173:513-517.

Clinical question: In patients who undergo inferior vena cava (IVC) filter placement for venous thromboembolism (VTE) prophylaxis or treatment, what are the associated patient characteristics, indications for placement, complications, retrieval date, and use of concomitant anticoagulant therapy?

Background: Retrievable IVC filters were designed to provide short-term protection from pulmonary embolism but are often left in place indefinitely. Retrievable IVC filters that are not removed can carry significant long-term risks. Further, the use of filters for VTE prophylaxis is controversial, and there are multiple sets of conflicting guidelines for filter insertion provided by various professional groups.

Study design: Retrospective review of IVC filter use over an eight-year period.

Setting: Boston Medical Center.

Synopsis: Medical records from all patients at Boston Medical Center who had a billing code for placement of an IVC filter between August 2003 and February 2011 were manually reviewed. Nine hundred fifty-two medical records were evaluated, of which 679 (71.3%) patients had retrievable IVC filters placed. The most common indications for filter placement were trauma (50.2%), malignancy (15.9%), and bleeding during anticoagulation (11.8%).

In total, 448 patients (47.1%) had filters placed for prophylactic purposes in the absence of documented VTE. Seventy-four patients developed VTE after filter placement; 48.2% of post-filter insertion VTEs occurred in patients who had no VTE prior to the filter; and 89.4% occurred in patients not receiving anticoagulants. An attempt was made to remove 71 of 679 (10.5%) retrievable filters, and 58 (8.5%) attempts were successful. There were 10 serious complications related to mechanical filter failure, including migration or fracture of the filter.

In this study, there was a high volume of filter use by the trauma service; thus, the patient population might be different from the hospital medicine patient population. The study also lacked systematic imaging and follow-up data. Further studies are needed to analyze the risks associated with IVC filter placement.

Bottom line: Use of IVC filters for VTE treatment and prophylaxis, in the context of low filter retrieval rates and lack of appropriate anticoagulant therapy, results in suboptimal outcomes.

Citation: Sarosiek S, Crowther M, Sloan M. Indications, complications, and management of inferior vena cava filters: the experience in 952 patients at an academic hospital with a level I trauma center. JAMA Intern Med. 2013;173:513-517.

Clinical question: In patients who undergo inferior vena cava (IVC) filter placement for venous thromboembolism (VTE) prophylaxis or treatment, what are the associated patient characteristics, indications for placement, complications, retrieval date, and use of concomitant anticoagulant therapy?

Background: Retrievable IVC filters were designed to provide short-term protection from pulmonary embolism but are often left in place indefinitely. Retrievable IVC filters that are not removed can carry significant long-term risks. Further, the use of filters for VTE prophylaxis is controversial, and there are multiple sets of conflicting guidelines for filter insertion provided by various professional groups.

Study design: Retrospective review of IVC filter use over an eight-year period.

Setting: Boston Medical Center.

Synopsis: Medical records from all patients at Boston Medical Center who had a billing code for placement of an IVC filter between August 2003 and February 2011 were manually reviewed. Nine hundred fifty-two medical records were evaluated, of which 679 (71.3%) patients had retrievable IVC filters placed. The most common indications for filter placement were trauma (50.2%), malignancy (15.9%), and bleeding during anticoagulation (11.8%).

In total, 448 patients (47.1%) had filters placed for prophylactic purposes in the absence of documented VTE. Seventy-four patients developed VTE after filter placement; 48.2% of post-filter insertion VTEs occurred in patients who had no VTE prior to the filter; and 89.4% occurred in patients not receiving anticoagulants. An attempt was made to remove 71 of 679 (10.5%) retrievable filters, and 58 (8.5%) attempts were successful. There were 10 serious complications related to mechanical filter failure, including migration or fracture of the filter.

In this study, there was a high volume of filter use by the trauma service; thus, the patient population might be different from the hospital medicine patient population. The study also lacked systematic imaging and follow-up data. Further studies are needed to analyze the risks associated with IVC filter placement.

Bottom line: Use of IVC filters for VTE treatment and prophylaxis, in the context of low filter retrieval rates and lack of appropriate anticoagulant therapy, results in suboptimal outcomes.

Citation: Sarosiek S, Crowther M, Sloan M. Indications, complications, and management of inferior vena cava filters: the experience in 952 patients at an academic hospital with a level I trauma center. JAMA Intern Med. 2013;173:513-517.

Clinical question: Can a prediction model based on administrative and clinical data identify potentially avoidable 30-day readmissions in medical patients prior to discharge?

Background: An estimated 18% of Medicare beneficiaries are readmitted to the hospital within 30 days of discharge, costing nearly $17 billion per year. Interventions to reduce readmission rates are costly and should be focused on high-risk patients. To date, using models to predict 30-day readmission has been problematic and unreliable.

Study design: Retrospective cohort.

Setting: Academic medical center in Boston.

Synopsis: Using consecutive discharges from all medical services of Brigham and Women’s Hospital occurring over one year, this study derived and internally validated a prediction model for potentially avoidable 30-day readmissions. Of 10,731 discharges, there were 2,399 (22%) 30-day readmissions, and 879 (8.5%) were deemed potentially avoidable. Seven independent predictors for readmission were identified and used to create a predictor score referred to as the HOSPITAL score. Predictors included hemoglobin and sodium levels at discharge, number of hospitalizations in the past year, and four features of the index hospitalization, including type, discharge from an oncology service, presence of procedures, and length of stay. The score was internally validated and found to predict potentially avoidable 30-day readmission in medical patients with fair discriminatory power and good calibration.

This study is unique in that none of the classic comorbidities (e.g. congestive heart failure) were associated with a higher risk of 30-day readmission. Previously unrecognized predictors, including hemoglobin, sodium, and number of procedures performed, were incorporated. This suggests that comorbidities are not as important as illness severity or clinical instability. Hospitalists should await studies that externally validate the HOSPITAL score before incorporating it into practice.

Bottom line: A unique and simple seven-item prediction model identifies potentially avoidable 30-day readmissions but needs to be externally validated before being widely utilized.

Citation: Donze J, Drahomir A, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients. JAMA Intern Med. 2013;137(8):632-638.

Clinical question: Can a prediction model based on administrative and clinical data identify potentially avoidable 30-day readmissions in medical patients prior to discharge?

Background: An estimated 18% of Medicare beneficiaries are readmitted to the hospital within 30 days of discharge, costing nearly $17 billion per year. Interventions to reduce readmission rates are costly and should be focused on high-risk patients. To date, using models to predict 30-day readmission has been problematic and unreliable.

Study design: Retrospective cohort.

Setting: Academic medical center in Boston.

Synopsis: Using consecutive discharges from all medical services of Brigham and Women’s Hospital occurring over one year, this study derived and internally validated a prediction model for potentially avoidable 30-day readmissions. Of 10,731 discharges, there were 2,399 (22%) 30-day readmissions, and 879 (8.5%) were deemed potentially avoidable. Seven independent predictors for readmission were identified and used to create a predictor score referred to as the HOSPITAL score. Predictors included hemoglobin and sodium levels at discharge, number of hospitalizations in the past year, and four features of the index hospitalization, including type, discharge from an oncology service, presence of procedures, and length of stay. The score was internally validated and found to predict potentially avoidable 30-day readmission in medical patients with fair discriminatory power and good calibration.

This study is unique in that none of the classic comorbidities (e.g. congestive heart failure) were associated with a higher risk of 30-day readmission. Previously unrecognized predictors, including hemoglobin, sodium, and number of procedures performed, were incorporated. This suggests that comorbidities are not as important as illness severity or clinical instability. Hospitalists should await studies that externally validate the HOSPITAL score before incorporating it into practice.

Bottom line: A unique and simple seven-item prediction model identifies potentially avoidable 30-day readmissions but needs to be externally validated before being widely utilized.

Citation: Donze J, Drahomir A, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients. JAMA Intern Med. 2013;137(8):632-638.

Clinical question: Can a prediction model based on administrative and clinical data identify potentially avoidable 30-day readmissions in medical patients prior to discharge?

Background: An estimated 18% of Medicare beneficiaries are readmitted to the hospital within 30 days of discharge, costing nearly $17 billion per year. Interventions to reduce readmission rates are costly and should be focused on high-risk patients. To date, using models to predict 30-day readmission has been problematic and unreliable.

Study design: Retrospective cohort.

Setting: Academic medical center in Boston.

Synopsis: Using consecutive discharges from all medical services of Brigham and Women’s Hospital occurring over one year, this study derived and internally validated a prediction model for potentially avoidable 30-day readmissions. Of 10,731 discharges, there were 2,399 (22%) 30-day readmissions, and 879 (8.5%) were deemed potentially avoidable. Seven independent predictors for readmission were identified and used to create a predictor score referred to as the HOSPITAL score. Predictors included hemoglobin and sodium levels at discharge, number of hospitalizations in the past year, and four features of the index hospitalization, including type, discharge from an oncology service, presence of procedures, and length of stay. The score was internally validated and found to predict potentially avoidable 30-day readmission in medical patients with fair discriminatory power and good calibration.

This study is unique in that none of the classic comorbidities (e.g. congestive heart failure) were associated with a higher risk of 30-day readmission. Previously unrecognized predictors, including hemoglobin, sodium, and number of procedures performed, were incorporated. This suggests that comorbidities are not as important as illness severity or clinical instability. Hospitalists should await studies that externally validate the HOSPITAL score before incorporating it into practice.

Bottom line: A unique and simple seven-item prediction model identifies potentially avoidable 30-day readmissions but needs to be externally validated before being widely utilized.

Citation: Donze J, Drahomir A, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients. JAMA Intern Med. 2013;137(8):632-638.

Clinical question: How does timing of surgical antibiotic prophylaxis affect risk of postoperative surgical-site infections (SSIs)?

Background: Antibiotic prophylaxis for major surgical procedures has been proven in clinical trials to reduce rates of SSI. The Centers for Medicare & Medicaid Services’ (CMS) Surgical Care Improvement Project (SCIP) has implemented quality metrics to ensure antibiotics are administered within 60 minutes of incision; however, studies have failed to show that a 60-minute pre-incision window is advantageous.

Study design: Retrospective cohort.

Setting: Veterans Affairs hospitals.

Synopsis: Using SCIP and VA Surgical Quality Improvement Program data from 112 VA hospitals, 32,459 cases of hip or knee arthroplasty, colorectal surgery, arterial vascular surgery, and hysterectomy from 2005-2009 were reviewed. A postoperative SSI occurred in 1,497 cases (4.6%). Using several statistical methods, the relationship between timing of prophylactic antibiotic administration and postoperative SSI within 30 days was evaluated. In unadjusted models, higher SSI rates were observed with antibiotic administration more than 60 minutes prior to incision (OR 1.34, 95% CI 1.08-1.66) but not after incision (OR 1.26, 95% CI 0.92-1.72), compared with procedures with antibiotics administered within 60 minutes pre-incision. However, after adjustment for patient, procedure, and antibiotic variables, no significant relationship between timing and SSI was observed (P=0.50 for all specialties).

The study sample was comprised primarily of older men and did not include patients who underwent cardiac procedures, limiting the generalizability of the findings. Nonetheless, the study is the largest of its kind and confirms previous studies that suggest there is no significant relationship between timing of antibiotics and SSI. Prophylactic antibiotics should still be used when indicated; however, using timing of prophylactic antibiotics as a quality measure is unlikely to improve outcomes.

Bottom line: Adherence to the empiric 60-minute window metric for timing of prophylactic antibiotics is not significantly associated with risk of SSI.

Citation: Hawn MT, Richman JS, Vick CC, et al. Timing of surgical antibiotic prophylaxis and the risk of surgical site infection. JAMA Surg. 2013 March 20:1-8. doi: 10.1001/jamasurg.2013.134 [Epub ahead of print].

Clinical question: How does timing of surgical antibiotic prophylaxis affect risk of postoperative surgical-site infections (SSIs)?

Background: Antibiotic prophylaxis for major surgical procedures has been proven in clinical trials to reduce rates of SSI. The Centers for Medicare & Medicaid Services’ (CMS) Surgical Care Improvement Project (SCIP) has implemented quality metrics to ensure antibiotics are administered within 60 minutes of incision; however, studies have failed to show that a 60-minute pre-incision window is advantageous.

Study design: Retrospective cohort.

Setting: Veterans Affairs hospitals.

Synopsis: Using SCIP and VA Surgical Quality Improvement Program data from 112 VA hospitals, 32,459 cases of hip or knee arthroplasty, colorectal surgery, arterial vascular surgery, and hysterectomy from 2005-2009 were reviewed. A postoperative SSI occurred in 1,497 cases (4.6%). Using several statistical methods, the relationship between timing of prophylactic antibiotic administration and postoperative SSI within 30 days was evaluated. In unadjusted models, higher SSI rates were observed with antibiotic administration more than 60 minutes prior to incision (OR 1.34, 95% CI 1.08-1.66) but not after incision (OR 1.26, 95% CI 0.92-1.72), compared with procedures with antibiotics administered within 60 minutes pre-incision. However, after adjustment for patient, procedure, and antibiotic variables, no significant relationship between timing and SSI was observed (P=0.50 for all specialties).

The study sample was comprised primarily of older men and did not include patients who underwent cardiac procedures, limiting the generalizability of the findings. Nonetheless, the study is the largest of its kind and confirms previous studies that suggest there is no significant relationship between timing of antibiotics and SSI. Prophylactic antibiotics should still be used when indicated; however, using timing of prophylactic antibiotics as a quality measure is unlikely to improve outcomes.

Bottom line: Adherence to the empiric 60-minute window metric for timing of prophylactic antibiotics is not significantly associated with risk of SSI.

Citation: Hawn MT, Richman JS, Vick CC, et al. Timing of surgical antibiotic prophylaxis and the risk of surgical site infection. JAMA Surg. 2013 March 20:1-8. doi: 10.1001/jamasurg.2013.134 [Epub ahead of print].

Clinical question: How does timing of surgical antibiotic prophylaxis affect risk of postoperative surgical-site infections (SSIs)?

Background: Antibiotic prophylaxis for major surgical procedures has been proven in clinical trials to reduce rates of SSI. The Centers for Medicare & Medicaid Services’ (CMS) Surgical Care Improvement Project (SCIP) has implemented quality metrics to ensure antibiotics are administered within 60 minutes of incision; however, studies have failed to show that a 60-minute pre-incision window is advantageous.

Study design: Retrospective cohort.

Setting: Veterans Affairs hospitals.

Synopsis: Using SCIP and VA Surgical Quality Improvement Program data from 112 VA hospitals, 32,459 cases of hip or knee arthroplasty, colorectal surgery, arterial vascular surgery, and hysterectomy from 2005-2009 were reviewed. A postoperative SSI occurred in 1,497 cases (4.6%). Using several statistical methods, the relationship between timing of prophylactic antibiotic administration and postoperative SSI within 30 days was evaluated. In unadjusted models, higher SSI rates were observed with antibiotic administration more than 60 minutes prior to incision (OR 1.34, 95% CI 1.08-1.66) but not after incision (OR 1.26, 95% CI 0.92-1.72), compared with procedures with antibiotics administered within 60 minutes pre-incision. However, after adjustment for patient, procedure, and antibiotic variables, no significant relationship between timing and SSI was observed (P=0.50 for all specialties).

The study sample was comprised primarily of older men and did not include patients who underwent cardiac procedures, limiting the generalizability of the findings. Nonetheless, the study is the largest of its kind and confirms previous studies that suggest there is no significant relationship between timing of antibiotics and SSI. Prophylactic antibiotics should still be used when indicated; however, using timing of prophylactic antibiotics as a quality measure is unlikely to improve outcomes.

Bottom line: Adherence to the empiric 60-minute window metric for timing of prophylactic antibiotics is not significantly associated with risk of SSI.

Citation: Hawn MT, Richman JS, Vick CC, et al. Timing of surgical antibiotic prophylaxis and the risk of surgical site infection. JAMA Surg. 2013 March 20:1-8. doi: 10.1001/jamasurg.2013.134 [Epub ahead of print].

Clinical question: What is the long-term outcome of elderly survivors of in-hospital cardiac arrest?

Background: Previous studies have examined in-hospital survival from in-hospital cardiac arrest but have not looked at long-term outcomes and readmission of in-hospital cardiac arrest survivors.

Study design: Retrospective cohort.

Setting: Acute-care hospitals that submitted data to the Get with the Guidelines—Resuscitation registry between 2000 and 2008.

Synopsis: Using the Get with the Guidelines—Resuscitation registry from 401 acute-care hospitals, data from 6,972 Medicare patients aged 65 years or older who had a pulseless in-hospital cardiac arrest and survived to discharge were analyzed. Survival rates were 82% at 30 days, 72% at three months, 58.5% at one year, and 49.6% at two years. Survival at three years was 43.5%, similar to patients discharged with heart failure.

One-year survival decreased with increasing age. Survival also decreased with black race (52.5% vs. 60.4% for white patients, P=0.001) and male sex (58.6% vs. 60.9% for women, P=0.03). Patients with mild or no neurologic disability at discharge had a higher survival rate at one year than patients with moderate, severe, or coma state. Readmission rates at one year after discharge were 65.6% and 76.2% at two years. Black patients, women, and patients with neurologic disability at discharge were more likely to be readmitted.

Because this is an observational study looking at a quality database of Medicare patients, it excludes patients at VA hospitals and non-Medicare facilities. This data excludes assessments of quality of life after discharge and health status among those with long-term survival, and does not include cause of death.

Bottom line: One-year survival following in-hospital cardiac arrest for patients over age 65 approaches 60% and decreases with increasing age, male sex, and black race.

Citation: Chan PS, Nallamothu BK, Krumholz HM, et al. Long-term outcomes in elderly survivors of in-hospital cardiac arrest. N Engl J Med. 2013;368:1019-1026.

Clinical question: What is the long-term outcome of elderly survivors of in-hospital cardiac arrest?

Background: Previous studies have examined in-hospital survival from in-hospital cardiac arrest but have not looked at long-term outcomes and readmission of in-hospital cardiac arrest survivors.

Study design: Retrospective cohort.

Setting: Acute-care hospitals that submitted data to the Get with the Guidelines—Resuscitation registry between 2000 and 2008.

Synopsis: Using the Get with the Guidelines—Resuscitation registry from 401 acute-care hospitals, data from 6,972 Medicare patients aged 65 years or older who had a pulseless in-hospital cardiac arrest and survived to discharge were analyzed. Survival rates were 82% at 30 days, 72% at three months, 58.5% at one year, and 49.6% at two years. Survival at three years was 43.5%, similar to patients discharged with heart failure.

One-year survival decreased with increasing age. Survival also decreased with black race (52.5% vs. 60.4% for white patients, P=0.001) and male sex (58.6% vs. 60.9% for women, P=0.03). Patients with mild or no neurologic disability at discharge had a higher survival rate at one year than patients with moderate, severe, or coma state. Readmission rates at one year after discharge were 65.6% and 76.2% at two years. Black patients, women, and patients with neurologic disability at discharge were more likely to be readmitted.

Because this is an observational study looking at a quality database of Medicare patients, it excludes patients at VA hospitals and non-Medicare facilities. This data excludes assessments of quality of life after discharge and health status among those with long-term survival, and does not include cause of death.

Bottom line: One-year survival following in-hospital cardiac arrest for patients over age 65 approaches 60% and decreases with increasing age, male sex, and black race.

Citation: Chan PS, Nallamothu BK, Krumholz HM, et al. Long-term outcomes in elderly survivors of in-hospital cardiac arrest. N Engl J Med. 2013;368:1019-1026.

Clinical question: What is the long-term outcome of elderly survivors of in-hospital cardiac arrest?

Background: Previous studies have examined in-hospital survival from in-hospital cardiac arrest but have not looked at long-term outcomes and readmission of in-hospital cardiac arrest survivors.

Study design: Retrospective cohort.

Setting: Acute-care hospitals that submitted data to the Get with the Guidelines—Resuscitation registry between 2000 and 2008.

Synopsis: Using the Get with the Guidelines—Resuscitation registry from 401 acute-care hospitals, data from 6,972 Medicare patients aged 65 years or older who had a pulseless in-hospital cardiac arrest and survived to discharge were analyzed. Survival rates were 82% at 30 days, 72% at three months, 58.5% at one year, and 49.6% at two years. Survival at three years was 43.5%, similar to patients discharged with heart failure.

One-year survival decreased with increasing age. Survival also decreased with black race (52.5% vs. 60.4% for white patients, P=0.001) and male sex (58.6% vs. 60.9% for women, P=0.03). Patients with mild or no neurologic disability at discharge had a higher survival rate at one year than patients with moderate, severe, or coma state. Readmission rates at one year after discharge were 65.6% and 76.2% at two years. Black patients, women, and patients with neurologic disability at discharge were more likely to be readmitted.

Because this is an observational study looking at a quality database of Medicare patients, it excludes patients at VA hospitals and non-Medicare facilities. This data excludes assessments of quality of life after discharge and health status among those with long-term survival, and does not include cause of death.

Bottom line: One-year survival following in-hospital cardiac arrest for patients over age 65 approaches 60% and decreases with increasing age, male sex, and black race.

Citation: Chan PS, Nallamothu BK, Krumholz HM, et al. Long-term outcomes in elderly survivors of in-hospital cardiac arrest. N Engl J Med. 2013;368:1019-1026.

Click here to listen to excerpts of Dr. Duke's interview with The Hospitalist

Click here to listen to excerpts of Dr. Duke's interview with The Hospitalist

Click here to listen to excerpts of Dr. Duke's interview with The Hospitalist

Click here to listen to excerpts of Dr. Parkhurst's interview with The Hospitalist

Click here to listen to excerpts of Dr. Parkhurst's interview with The Hospitalist

Click here to listen to excerpts of Dr. Parkhurst's interview with The Hospitalist

Click here to listen to excerpts of Dr. Chretien's interview with The Hospitalist

Click here to listen to excerpts of Dr. Chretien's interview with The Hospitalist

Click here to listen to excerpts of Dr. Chretien's interview with The Hospitalist

Many health-care-reform initiatives are so new that few data are available to assess whether they are working as intended. The Centers for Medicare & Medicaid Services (CMS), however, has touted the early numbers from its Hospital Readmission Reduction Program to suggest that the policy is making a difference in curbing bounce-backs. The overall impact, however, might be decidedly more nuanced and provides a telling example of the challenges that such programs can present to hospitalists and other health-care providers.

At a Senate Finance Committee Hearing in February, Jonathan Blum, deputy administrator and director for the Center of Medicare at CMS, released data suggesting that 30-day readmission rates for all causes dropped to 17.8% of hospitalizations near the end of 2012 after remaining at roughly 19% in each of the five previous years. The difference translates into 70,000 fewer readmissions annually.

During the first round of penalties, CMS dinged 2,213 hospitals for an estimated $280 million, or an average of about $126,500 per hospital, for excessive readmissions linked to heart attack, heart failure, and pneumonia care. Blum made the case that the penalties—or the threat thereof—are helping to improve rates.

Those arguing that the policy could disproportionately impact institutions caring for more vulnerable, high-risk patients also found new support in a recent New England Journal of Medicine perspective suggesting that academic medical centers and safety-net hospitals were more likely to be penalized.¹ Among their suggestions, the perspective’s co-authors, from Harvard’s School of Public Health, suggested that the policy take patient socioeconomic status into account to provide a fairer basis of comparison.

A second recent study suggested that even the reduced readmission rates might not be telling the whole story. An analysis of patients released in 2010 from safety-net hospital Boston Medical Center showed that nearly 1 in 4 returned to the ED within a month of discharge.2 But more than half of those patients weren’t readmitted as inpatients, meaning that they wouldn’t show up under Medicare’s readmissions statistics.

Along with the mixed early reviews of EHR rollouts and the HCAHPS portion of the Hospital Value-Based Purchasing program, it’s another reminder that CMS metrics and incentives might not always add up as envisioned. In the near future, it seems, hospitals and health-care providers might have to contend with some imperfect numbers. TH

Bryn Nelson is a freelance medical writer in Seattle.

References

1. Joynt KE, Jha AK. Thirty-day readmissions–truth and consequences. N Engl J Med. 2012;366:1366-1369.

2. Rising KL, White LF, Fernandez WG, Boutwell, AE. Emergency department visits after hospital discharge: a missing part of the equation. Ann Emerg Med. 2013; in press.

Many health-care-reform initiatives are so new that few data are available to assess whether they are working as intended. The Centers for Medicare & Medicaid Services (CMS), however, has touted the early numbers from its Hospital Readmission Reduction Program to suggest that the policy is making a difference in curbing bounce-backs. The overall impact, however, might be decidedly more nuanced and provides a telling example of the challenges that such programs can present to hospitalists and other health-care providers.

At a Senate Finance Committee Hearing in February, Jonathan Blum, deputy administrator and director for the Center of Medicare at CMS, released data suggesting that 30-day readmission rates for all causes dropped to 17.8% of hospitalizations near the end of 2012 after remaining at roughly 19% in each of the five previous years. The difference translates into 70,000 fewer readmissions annually.

During the first round of penalties, CMS dinged 2,213 hospitals for an estimated $280 million, or an average of about $126,500 per hospital, for excessive readmissions linked to heart attack, heart failure, and pneumonia care. Blum made the case that the penalties—or the threat thereof—are helping to improve rates.

Those arguing that the policy could disproportionately impact institutions caring for more vulnerable, high-risk patients also found new support in a recent New England Journal of Medicine perspective suggesting that academic medical centers and safety-net hospitals were more likely to be penalized.¹ Among their suggestions, the perspective’s co-authors, from Harvard’s School of Public Health, suggested that the policy take patient socioeconomic status into account to provide a fairer basis of comparison.

A second recent study suggested that even the reduced readmission rates might not be telling the whole story. An analysis of patients released in 2010 from safety-net hospital Boston Medical Center showed that nearly 1 in 4 returned to the ED within a month of discharge.2 But more than half of those patients weren’t readmitted as inpatients, meaning that they wouldn’t show up under Medicare’s readmissions statistics.

Along with the mixed early reviews of EHR rollouts and the HCAHPS portion of the Hospital Value-Based Purchasing program, it’s another reminder that CMS metrics and incentives might not always add up as envisioned. In the near future, it seems, hospitals and health-care providers might have to contend with some imperfect numbers. TH

Bryn Nelson is a freelance medical writer in Seattle.

References

1. Joynt KE, Jha AK. Thirty-day readmissions–truth and consequences. N Engl J Med. 2012;366:1366-1369.

2. Rising KL, White LF, Fernandez WG, Boutwell, AE. Emergency department visits after hospital discharge: a missing part of the equation. Ann Emerg Med. 2013; in press.

Many health-care-reform initiatives are so new that few data are available to assess whether they are working as intended. The Centers for Medicare & Medicaid Services (CMS), however, has touted the early numbers from its Hospital Readmission Reduction Program to suggest that the policy is making a difference in curbing bounce-backs. The overall impact, however, might be decidedly more nuanced and provides a telling example of the challenges that such programs can present to hospitalists and other health-care providers.

At a Senate Finance Committee Hearing in February, Jonathan Blum, deputy administrator and director for the Center of Medicare at CMS, released data suggesting that 30-day readmission rates for all causes dropped to 17.8% of hospitalizations near the end of 2012 after remaining at roughly 19% in each of the five previous years. The difference translates into 70,000 fewer readmissions annually.

During the first round of penalties, CMS dinged 2,213 hospitals for an estimated $280 million, or an average of about $126,500 per hospital, for excessive readmissions linked to heart attack, heart failure, and pneumonia care. Blum made the case that the penalties—or the threat thereof—are helping to improve rates.

Those arguing that the policy could disproportionately impact institutions caring for more vulnerable, high-risk patients also found new support in a recent New England Journal of Medicine perspective suggesting that academic medical centers and safety-net hospitals were more likely to be penalized.¹ Among their suggestions, the perspective’s co-authors, from Harvard’s School of Public Health, suggested that the policy take patient socioeconomic status into account to provide a fairer basis of comparison.

A second recent study suggested that even the reduced readmission rates might not be telling the whole story. An analysis of patients released in 2010 from safety-net hospital Boston Medical Center showed that nearly 1 in 4 returned to the ED within a month of discharge.2 But more than half of those patients weren’t readmitted as inpatients, meaning that they wouldn’t show up under Medicare’s readmissions statistics.

Along with the mixed early reviews of EHR rollouts and the HCAHPS portion of the Hospital Value-Based Purchasing program, it’s another reminder that CMS metrics and incentives might not always add up as envisioned. In the near future, it seems, hospitals and health-care providers might have to contend with some imperfect numbers. TH

Bryn Nelson is a freelance medical writer in Seattle.

References

1. Joynt KE, Jha AK. Thirty-day readmissions–truth and consequences. N Engl J Med. 2012;366:1366-1369.

2. Rising KL, White LF, Fernandez WG, Boutwell, AE. Emergency department visits after hospital discharge: a missing part of the equation. Ann Emerg Med. 2013; in press.