Tide turns in favor of multivessel PCI in STEMI

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Tide turns in favor of multivessel PCI in STEMI

SNOWMASS, COLO. – Recent data seem to refute the 2013 American Heart Association/American College of Cardiology class III recommendation to avoid multivessel percutaneous coronary intervention at the time of primary PCI for ST-elevation MI, Dr. David R. Holmes Jr. observed at the Annual Cardiovascular Conference at Snowmass.

“The current AHA/ACC guidelines for STEMI should be and are being reevaluated regarding clarifications for the indications and timing of non–infarct artery revascularization,” according to Dr. Holmes, a cardiologist at the Mayo Clinic in Rochester, Minn., and an ACC past president.

Dr. David R. Holmes Jr.

Indeed, the ACC has already withdrawn from its ‘Choosing Wisely’ campaign its former recommendation discouraging multivessel revascularization at the time of primary PCI for STEMI. The college cited “new science showing that complete revascularization of all significant blocked arteries leads to better outcomes in some heart attack patients.”

Dr. Holmes was coauthor of a meta-analysis of 4 prospective and 14 retrospective studies involving more than 40,000 patients that concluded multivessel PCI for STEMI should be discouraged, and that significant nonculprit lesions should only be treated during staged procedures (J. Am. Coll. Cardiol. 2011;58:692-703). This meta-analysis was influential in the creation of the class III ‘don’t do it’ recommendation in the AHA/ACC guidelines. But Dr. Holmes said that in hindsight, the data included in the meta-analysis were something of a mishmash and “wound up being very hard to interpret.”

Greater clarity has been brought by two more recent randomized trials: PRAMI and CvLPRIT. Both were relatively small by cardiology standards, but they ended up showing similarly striking advantages in favor of using the STEMI hospitalization to perform preventive PCI of both the infarct-related artery and non–infarct arteries with major stenoses.

PRAMI included 465 acute STEMI patients who underwent infarct artery PCI and were then randomized to preventive PCI or infarct artery–only PCI. At a mean follow-up of 23 months, the preventive multivessel PCI group had a 65% reduction in the relative risk of the primary outcome, a composite of cardiac death, nonfatal MI, or refractory angina (N. Engl. J. Med. 2013;369:1115-23).

The yet-to-be-published CvLPRIT study was presented at the 2014 European Society of Cardiology meeting in Barcelona. The multicenter study included 296 STEMI patients with angiographically established significant multivessel disease who were randomized to primary PCI of the culprit vessel only or to complete revascularization. The primary outcome, the 12-month composite of all-cause mortality, recurrent MI, heart failure, or ischemia-driven revascularization, occurred in 10% of the complete revascularization group, compared with 21.2% of patients assigned to culprit artery–only PCI.

Also at the ESC conference, CvLPRIT investigator Dr. Anthony Gershlick of the University of Leicester (England) presented a meta-analysis combining the weighted results of PRAMI, CvLPRIT, and two earlier randomized trials: HELP AMI (Int. J. Cardiovasc. Intervent. 2004;6:128-33) and an Italian trial (Heart 2010;96:662-7). The results strongly favored multivessel PCI, with a 45% reduction in mortality and a 61% decrease in recurrent MI, compared with culprit vessel–only PCI at the time of admission for STEMI.

“Maybe there aren’t any innocent bystanders,” commented Dr. Holmes. “Maybe if you have somebody who has multivessel disease and you see something you think might be an innocent bystander but is a significant lesion, maybe it’s not so innocent. Maybe by treating them all at the time of the initial intervention the patient is going to do better.”

He reported having no financial conflicts of interest regarding his presentation.

[email protected]

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SNOWMASS, COLO. – Recent data seem to refute the 2013 American Heart Association/American College of Cardiology class III recommendation to avoid multivessel percutaneous coronary intervention at the time of primary PCI for ST-elevation MI, Dr. David R. Holmes Jr. observed at the Annual Cardiovascular Conference at Snowmass.

“The current AHA/ACC guidelines for STEMI should be and are being reevaluated regarding clarifications for the indications and timing of non–infarct artery revascularization,” according to Dr. Holmes, a cardiologist at the Mayo Clinic in Rochester, Minn., and an ACC past president.

Dr. David R. Holmes Jr.

Indeed, the ACC has already withdrawn from its ‘Choosing Wisely’ campaign its former recommendation discouraging multivessel revascularization at the time of primary PCI for STEMI. The college cited “new science showing that complete revascularization of all significant blocked arteries leads to better outcomes in some heart attack patients.”

Dr. Holmes was coauthor of a meta-analysis of 4 prospective and 14 retrospective studies involving more than 40,000 patients that concluded multivessel PCI for STEMI should be discouraged, and that significant nonculprit lesions should only be treated during staged procedures (J. Am. Coll. Cardiol. 2011;58:692-703). This meta-analysis was influential in the creation of the class III ‘don’t do it’ recommendation in the AHA/ACC guidelines. But Dr. Holmes said that in hindsight, the data included in the meta-analysis were something of a mishmash and “wound up being very hard to interpret.”

Greater clarity has been brought by two more recent randomized trials: PRAMI and CvLPRIT. Both were relatively small by cardiology standards, but they ended up showing similarly striking advantages in favor of using the STEMI hospitalization to perform preventive PCI of both the infarct-related artery and non–infarct arteries with major stenoses.

PRAMI included 465 acute STEMI patients who underwent infarct artery PCI and were then randomized to preventive PCI or infarct artery–only PCI. At a mean follow-up of 23 months, the preventive multivessel PCI group had a 65% reduction in the relative risk of the primary outcome, a composite of cardiac death, nonfatal MI, or refractory angina (N. Engl. J. Med. 2013;369:1115-23).

The yet-to-be-published CvLPRIT study was presented at the 2014 European Society of Cardiology meeting in Barcelona. The multicenter study included 296 STEMI patients with angiographically established significant multivessel disease who were randomized to primary PCI of the culprit vessel only or to complete revascularization. The primary outcome, the 12-month composite of all-cause mortality, recurrent MI, heart failure, or ischemia-driven revascularization, occurred in 10% of the complete revascularization group, compared with 21.2% of patients assigned to culprit artery–only PCI.

Also at the ESC conference, CvLPRIT investigator Dr. Anthony Gershlick of the University of Leicester (England) presented a meta-analysis combining the weighted results of PRAMI, CvLPRIT, and two earlier randomized trials: HELP AMI (Int. J. Cardiovasc. Intervent. 2004;6:128-33) and an Italian trial (Heart 2010;96:662-7). The results strongly favored multivessel PCI, with a 45% reduction in mortality and a 61% decrease in recurrent MI, compared with culprit vessel–only PCI at the time of admission for STEMI.

“Maybe there aren’t any innocent bystanders,” commented Dr. Holmes. “Maybe if you have somebody who has multivessel disease and you see something you think might be an innocent bystander but is a significant lesion, maybe it’s not so innocent. Maybe by treating them all at the time of the initial intervention the patient is going to do better.”

He reported having no financial conflicts of interest regarding his presentation.

[email protected]

SNOWMASS, COLO. – Recent data seem to refute the 2013 American Heart Association/American College of Cardiology class III recommendation to avoid multivessel percutaneous coronary intervention at the time of primary PCI for ST-elevation MI, Dr. David R. Holmes Jr. observed at the Annual Cardiovascular Conference at Snowmass.

“The current AHA/ACC guidelines for STEMI should be and are being reevaluated regarding clarifications for the indications and timing of non–infarct artery revascularization,” according to Dr. Holmes, a cardiologist at the Mayo Clinic in Rochester, Minn., and an ACC past president.

Dr. David R. Holmes Jr.

Indeed, the ACC has already withdrawn from its ‘Choosing Wisely’ campaign its former recommendation discouraging multivessel revascularization at the time of primary PCI for STEMI. The college cited “new science showing that complete revascularization of all significant blocked arteries leads to better outcomes in some heart attack patients.”

Dr. Holmes was coauthor of a meta-analysis of 4 prospective and 14 retrospective studies involving more than 40,000 patients that concluded multivessel PCI for STEMI should be discouraged, and that significant nonculprit lesions should only be treated during staged procedures (J. Am. Coll. Cardiol. 2011;58:692-703). This meta-analysis was influential in the creation of the class III ‘don’t do it’ recommendation in the AHA/ACC guidelines. But Dr. Holmes said that in hindsight, the data included in the meta-analysis were something of a mishmash and “wound up being very hard to interpret.”

Greater clarity has been brought by two more recent randomized trials: PRAMI and CvLPRIT. Both were relatively small by cardiology standards, but they ended up showing similarly striking advantages in favor of using the STEMI hospitalization to perform preventive PCI of both the infarct-related artery and non–infarct arteries with major stenoses.

PRAMI included 465 acute STEMI patients who underwent infarct artery PCI and were then randomized to preventive PCI or infarct artery–only PCI. At a mean follow-up of 23 months, the preventive multivessel PCI group had a 65% reduction in the relative risk of the primary outcome, a composite of cardiac death, nonfatal MI, or refractory angina (N. Engl. J. Med. 2013;369:1115-23).

The yet-to-be-published CvLPRIT study was presented at the 2014 European Society of Cardiology meeting in Barcelona. The multicenter study included 296 STEMI patients with angiographically established significant multivessel disease who were randomized to primary PCI of the culprit vessel only or to complete revascularization. The primary outcome, the 12-month composite of all-cause mortality, recurrent MI, heart failure, or ischemia-driven revascularization, occurred in 10% of the complete revascularization group, compared with 21.2% of patients assigned to culprit artery–only PCI.

Also at the ESC conference, CvLPRIT investigator Dr. Anthony Gershlick of the University of Leicester (England) presented a meta-analysis combining the weighted results of PRAMI, CvLPRIT, and two earlier randomized trials: HELP AMI (Int. J. Cardiovasc. Intervent. 2004;6:128-33) and an Italian trial (Heart 2010;96:662-7). The results strongly favored multivessel PCI, with a 45% reduction in mortality and a 61% decrease in recurrent MI, compared with culprit vessel–only PCI at the time of admission for STEMI.

“Maybe there aren’t any innocent bystanders,” commented Dr. Holmes. “Maybe if you have somebody who has multivessel disease and you see something you think might be an innocent bystander but is a significant lesion, maybe it’s not so innocent. Maybe by treating them all at the time of the initial intervention the patient is going to do better.”

He reported having no financial conflicts of interest regarding his presentation.

[email protected]

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EXPERT ANALYSIS FROM THE CARDIOVASCULAR CONFERENCE AT SNOWMASS

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Photographic AK counting isn’t ready for prime time

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Can photographs be as good as clinical examination for counting actinic keratoses?

Depending on the physician, AK counts varied widely on clinical assessment as well as in photos, reported Dr. Sudipta Sinnya of the Dermatology Research Centre at the University of Queensland, Brisbane, and associates. However, based on current two-dimensional technology, clinical counting yields superior results, said the researchers, who studied the counts of five trained observers carried out in two sessions with six patients.

“As technological advancements occur, three-dimensional photography will largely supersede two-dimensional photographs in clinical practice, and more robust image-capturing techniques should improve the accuracy of photogra­phic counting,” the researchers noted.

Read the full article from Acta Dermato-Venereologica (2015 [doi:10.2340/00015555-2040]) here.

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Can photographs be as good as clinical examination for counting actinic keratoses?

Depending on the physician, AK counts varied widely on clinical assessment as well as in photos, reported Dr. Sudipta Sinnya of the Dermatology Research Centre at the University of Queensland, Brisbane, and associates. However, based on current two-dimensional technology, clinical counting yields superior results, said the researchers, who studied the counts of five trained observers carried out in two sessions with six patients.

“As technological advancements occur, three-dimensional photography will largely supersede two-dimensional photographs in clinical practice, and more robust image-capturing techniques should improve the accuracy of photogra­phic counting,” the researchers noted.

Read the full article from Acta Dermato-Venereologica (2015 [doi:10.2340/00015555-2040]) here.

Can photographs be as good as clinical examination for counting actinic keratoses?

Depending on the physician, AK counts varied widely on clinical assessment as well as in photos, reported Dr. Sudipta Sinnya of the Dermatology Research Centre at the University of Queensland, Brisbane, and associates. However, based on current two-dimensional technology, clinical counting yields superior results, said the researchers, who studied the counts of five trained observers carried out in two sessions with six patients.

“As technological advancements occur, three-dimensional photography will largely supersede two-dimensional photographs in clinical practice, and more robust image-capturing techniques should improve the accuracy of photogra­phic counting,” the researchers noted.

Read the full article from Acta Dermato-Venereologica (2015 [doi:10.2340/00015555-2040]) here.

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Vagal stimulation may help upper limb stroke recovery

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Vagal stimulation may help upper limb stroke recovery

NASHVILLE, TENN. – Patients with upper limbs affected by ischemic stroke who paired traditional rehabilitation exercises with pulsed vagus nerve stimulation boosted functional scores significantly higher than did those who performed exercises alone in a small, randomized pilot trial.

Taken together with the low rate of adverse events associated with device implantation, the study suggests that coupling the interventions is feasible and likely to be beneficial, Dr. Jesse Dawson of the University of Glasgow, Scotland, said at the International Stroke Conference, sponsored by the American Heart Association.

The vagus nerve stimulator (VNS) is typically used to suppress epileptiform discharges and circumvent seizures. The usual stimulation pattern is continuous cycles of 30 seconds on and 5 minutes off. In his randomized, controlled trial, Dr. Dawson set the device to deliver 0.5-second pulses that coincided with each repetition of a rehabilitative movement. When simulated, the nerve releases two proneuroplastic neurotransmitters, acetylcholine and norepinephrine, which then disperse over the cerebral cortex.

“Our theory was that if we timed these releases at specific periods during rehabilitation therapy, we might be able to drive neuroplasticity toward those specific tasks,” Dr. Dawson said at the conference. The technique has proved effective in both aged rats and rat stroke models, he added.

The trial comprised 20 patients who had experienced an ischemic stroke about 2 years prior. Each was left with residual dysfunction in an upper extremity; seven had a paretic limb. The mean Action Research Arm Test (ARAT) score was 33, and the mean upper extremity Fugl-Meyer score was 43, indicating moderate impairment.

Ten patients underwent VNS implantation. Nine completed the trial. One withdrew after 2 weeks because of a transient vocal cord palsy. This later resolved spontaneously.

Other adverse events related to the VNS were also transient. They included taste disturbance, chest pain, mild dysphagia, and nausea after a therapy session.

The 6-week intervention consisted of 18 sessions, each lasting 2 hours. In each, the rehabilitative movement was repeated 300-400 times.

In a per-protocol analysis, there was no significant difference in the upper extremity Fugl-Meyer score at the study’s end. However, when the patient who had withdrawn was excluded from the analysis, the results did become statistically significant. Patients in the dual-therapy group gained almost 10 points, compared with a 3-point gain in the exercise-only group. The ARAT scores were not different at study’s end.

In light of the positive initial results, a sham-controlled, randomized trial is in the works. The trial will randomize 20-25 patients to either the VNS-paired exercise or exercise-only interventions. All participants will receive the VNS device, but only the paired intervention group will receive actual stimuli.

Patricia Smith, Ph.D., the Doris E. Porter Professor in Physical Therapy at the University of Texas Southwestern, Dallas, is the lead investigator.

MicroTransponder, which makes the VNS unit, is sponsoring both the studies. Neither Dr. Dawson nor Dr. Smith have any financial ties to the company.

[email protected]

On Twitter @alz_gal

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NASHVILLE, TENN. – Patients with upper limbs affected by ischemic stroke who paired traditional rehabilitation exercises with pulsed vagus nerve stimulation boosted functional scores significantly higher than did those who performed exercises alone in a small, randomized pilot trial.

Taken together with the low rate of adverse events associated with device implantation, the study suggests that coupling the interventions is feasible and likely to be beneficial, Dr. Jesse Dawson of the University of Glasgow, Scotland, said at the International Stroke Conference, sponsored by the American Heart Association.

The vagus nerve stimulator (VNS) is typically used to suppress epileptiform discharges and circumvent seizures. The usual stimulation pattern is continuous cycles of 30 seconds on and 5 minutes off. In his randomized, controlled trial, Dr. Dawson set the device to deliver 0.5-second pulses that coincided with each repetition of a rehabilitative movement. When simulated, the nerve releases two proneuroplastic neurotransmitters, acetylcholine and norepinephrine, which then disperse over the cerebral cortex.

“Our theory was that if we timed these releases at specific periods during rehabilitation therapy, we might be able to drive neuroplasticity toward those specific tasks,” Dr. Dawson said at the conference. The technique has proved effective in both aged rats and rat stroke models, he added.

The trial comprised 20 patients who had experienced an ischemic stroke about 2 years prior. Each was left with residual dysfunction in an upper extremity; seven had a paretic limb. The mean Action Research Arm Test (ARAT) score was 33, and the mean upper extremity Fugl-Meyer score was 43, indicating moderate impairment.

Ten patients underwent VNS implantation. Nine completed the trial. One withdrew after 2 weeks because of a transient vocal cord palsy. This later resolved spontaneously.

Other adverse events related to the VNS were also transient. They included taste disturbance, chest pain, mild dysphagia, and nausea after a therapy session.

The 6-week intervention consisted of 18 sessions, each lasting 2 hours. In each, the rehabilitative movement was repeated 300-400 times.

In a per-protocol analysis, there was no significant difference in the upper extremity Fugl-Meyer score at the study’s end. However, when the patient who had withdrawn was excluded from the analysis, the results did become statistically significant. Patients in the dual-therapy group gained almost 10 points, compared with a 3-point gain in the exercise-only group. The ARAT scores were not different at study’s end.

In light of the positive initial results, a sham-controlled, randomized trial is in the works. The trial will randomize 20-25 patients to either the VNS-paired exercise or exercise-only interventions. All participants will receive the VNS device, but only the paired intervention group will receive actual stimuli.

Patricia Smith, Ph.D., the Doris E. Porter Professor in Physical Therapy at the University of Texas Southwestern, Dallas, is the lead investigator.

MicroTransponder, which makes the VNS unit, is sponsoring both the studies. Neither Dr. Dawson nor Dr. Smith have any financial ties to the company.

[email protected]

On Twitter @alz_gal

NASHVILLE, TENN. – Patients with upper limbs affected by ischemic stroke who paired traditional rehabilitation exercises with pulsed vagus nerve stimulation boosted functional scores significantly higher than did those who performed exercises alone in a small, randomized pilot trial.

Taken together with the low rate of adverse events associated with device implantation, the study suggests that coupling the interventions is feasible and likely to be beneficial, Dr. Jesse Dawson of the University of Glasgow, Scotland, said at the International Stroke Conference, sponsored by the American Heart Association.

The vagus nerve stimulator (VNS) is typically used to suppress epileptiform discharges and circumvent seizures. The usual stimulation pattern is continuous cycles of 30 seconds on and 5 minutes off. In his randomized, controlled trial, Dr. Dawson set the device to deliver 0.5-second pulses that coincided with each repetition of a rehabilitative movement. When simulated, the nerve releases two proneuroplastic neurotransmitters, acetylcholine and norepinephrine, which then disperse over the cerebral cortex.

“Our theory was that if we timed these releases at specific periods during rehabilitation therapy, we might be able to drive neuroplasticity toward those specific tasks,” Dr. Dawson said at the conference. The technique has proved effective in both aged rats and rat stroke models, he added.

The trial comprised 20 patients who had experienced an ischemic stroke about 2 years prior. Each was left with residual dysfunction in an upper extremity; seven had a paretic limb. The mean Action Research Arm Test (ARAT) score was 33, and the mean upper extremity Fugl-Meyer score was 43, indicating moderate impairment.

Ten patients underwent VNS implantation. Nine completed the trial. One withdrew after 2 weeks because of a transient vocal cord palsy. This later resolved spontaneously.

Other adverse events related to the VNS were also transient. They included taste disturbance, chest pain, mild dysphagia, and nausea after a therapy session.

The 6-week intervention consisted of 18 sessions, each lasting 2 hours. In each, the rehabilitative movement was repeated 300-400 times.

In a per-protocol analysis, there was no significant difference in the upper extremity Fugl-Meyer score at the study’s end. However, when the patient who had withdrawn was excluded from the analysis, the results did become statistically significant. Patients in the dual-therapy group gained almost 10 points, compared with a 3-point gain in the exercise-only group. The ARAT scores were not different at study’s end.

In light of the positive initial results, a sham-controlled, randomized trial is in the works. The trial will randomize 20-25 patients to either the VNS-paired exercise or exercise-only interventions. All participants will receive the VNS device, but only the paired intervention group will receive actual stimuli.

Patricia Smith, Ph.D., the Doris E. Porter Professor in Physical Therapy at the University of Texas Southwestern, Dallas, is the lead investigator.

MicroTransponder, which makes the VNS unit, is sponsoring both the studies. Neither Dr. Dawson nor Dr. Smith have any financial ties to the company.

[email protected]

On Twitter @alz_gal

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AT THE INTERNATIONAL STROKE CONFERENCE

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Key clinical point: Vagus nerve stimulation paired with physical therapy may improve functional recovery in upper limbs after stroke.

Major finding: The paired intervention boosted the upper extremity Fugl-Meyer score by 10 points in the intervention group and 3 points in the control group.

Data source: A randomized trial of 20 patients about 2 years post stroke who had residual dysfunction in an upper extremity.

Disclosures: MicroTransponder, which makes the VNS unit, is sponsoring both the studies. Dr. Dawson has no financial ties to the company.

2011 Resident Work Hour Reforms Had No Effect on Mortality or Readmissions

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2011 Resident Work Hour Reforms Had No Effect on Mortality or Readmissions

Clinical question

Did the 2011 Accreditation Council for Graduate Medical Education resident work hour reforms affect patient outcomes?

Bottom line

Resident work hour reforms were proposed by the Accreditation Council for Graduate Medical Education (ACGME) to reduce resident fatigue (and thus potentially reduce the risk of medical errors), but implementation of the work hour changes also led to concerns over patient safety because of increased handoffs in care. This study shows that work hour reforms had no impact, either positive or negative, on the important patient outcomes of mortality and readmission rates. Other outcomes such as length of stay and number of intensive care unit transfers may need to be examined in future studies to detect more subtle differences. (LOE = 2b)

Reference

Patel MS, Volpp KG, Small DS, et al. Association of the 2011 ACGME resident duty hour reforms with mortality and readmissions among hospitalized Medicare patients. JAMA 2014;312(22):2364-2373.

Study design: Cohort (retrospective)

Funding source: Government

Allocation: Uncertain

Setting: Inpatient (any location)

Synopsis

In 2011, the ACGME instituted work hour reforms for residents that reduced the work hour limit from 30 consecutive hours to 16 hours for first-year residents and 24 hours for all other residents. Investigators in this study evaluated the effect of the 2011 ACGME reforms on 30-day all-location mortality and 30-day all-cause readmissions. Patients included in the study were Medicare patients who were admitted to acute care US hospitals from 2009 to 2012 with acute myocardial infarction, stroke, gastrointestinal bleeding, or congestive heart failure, or those admitted for general, orthopedic, or vascular surgery. Hospitals were classified by their level of teaching intensity using a resident-to-bed ratio defined as the number of residents divided by the number of staffed beds.

In an analysis that adjusted for demographics, co-morbidities, and the presence of surgical complications, the implementation of work hour reforms did not affect 30-day mortality or readmissions in more-intensive teaching hospitals relative to less-intensive teaching hospitals during the postreform year as compared with 2 years before the reform. Multiple factors beyond the implementation of work hour reforms, may have contributed to this lack of effect. First, adherence to the new reforms by residency programs in the first year is unclear. Second, concurrent initiatives to improve patient outcomes during this time may have affected all hospitals, teaching and nonteaching. Finally, the authors suggest that the greater emphasis on resident supervision with the new reforms may have counterbalanced any negative effects of increased resident handoffs.

Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.

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Did the 2011 Accreditation Council for Graduate Medical Education resident work hour reforms affect patient outcomes?

Bottom line

Resident work hour reforms were proposed by the Accreditation Council for Graduate Medical Education (ACGME) to reduce resident fatigue (and thus potentially reduce the risk of medical errors), but implementation of the work hour changes also led to concerns over patient safety because of increased handoffs in care. This study shows that work hour reforms had no impact, either positive or negative, on the important patient outcomes of mortality and readmission rates. Other outcomes such as length of stay and number of intensive care unit transfers may need to be examined in future studies to detect more subtle differences. (LOE = 2b)

Reference

Patel MS, Volpp KG, Small DS, et al. Association of the 2011 ACGME resident duty hour reforms with mortality and readmissions among hospitalized Medicare patients. JAMA 2014;312(22):2364-2373.

Study design: Cohort (retrospective)

Funding source: Government

Allocation: Uncertain

Setting: Inpatient (any location)

Synopsis

In 2011, the ACGME instituted work hour reforms for residents that reduced the work hour limit from 30 consecutive hours to 16 hours for first-year residents and 24 hours for all other residents. Investigators in this study evaluated the effect of the 2011 ACGME reforms on 30-day all-location mortality and 30-day all-cause readmissions. Patients included in the study were Medicare patients who were admitted to acute care US hospitals from 2009 to 2012 with acute myocardial infarction, stroke, gastrointestinal bleeding, or congestive heart failure, or those admitted for general, orthopedic, or vascular surgery. Hospitals were classified by their level of teaching intensity using a resident-to-bed ratio defined as the number of residents divided by the number of staffed beds.

In an analysis that adjusted for demographics, co-morbidities, and the presence of surgical complications, the implementation of work hour reforms did not affect 30-day mortality or readmissions in more-intensive teaching hospitals relative to less-intensive teaching hospitals during the postreform year as compared with 2 years before the reform. Multiple factors beyond the implementation of work hour reforms, may have contributed to this lack of effect. First, adherence to the new reforms by residency programs in the first year is unclear. Second, concurrent initiatives to improve patient outcomes during this time may have affected all hospitals, teaching and nonteaching. Finally, the authors suggest that the greater emphasis on resident supervision with the new reforms may have counterbalanced any negative effects of increased resident handoffs.

Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.

Clinical question

Did the 2011 Accreditation Council for Graduate Medical Education resident work hour reforms affect patient outcomes?

Bottom line

Resident work hour reforms were proposed by the Accreditation Council for Graduate Medical Education (ACGME) to reduce resident fatigue (and thus potentially reduce the risk of medical errors), but implementation of the work hour changes also led to concerns over patient safety because of increased handoffs in care. This study shows that work hour reforms had no impact, either positive or negative, on the important patient outcomes of mortality and readmission rates. Other outcomes such as length of stay and number of intensive care unit transfers may need to be examined in future studies to detect more subtle differences. (LOE = 2b)

Reference

Patel MS, Volpp KG, Small DS, et al. Association of the 2011 ACGME resident duty hour reforms with mortality and readmissions among hospitalized Medicare patients. JAMA 2014;312(22):2364-2373.

Study design: Cohort (retrospective)

Funding source: Government

Allocation: Uncertain

Setting: Inpatient (any location)

Synopsis

In 2011, the ACGME instituted work hour reforms for residents that reduced the work hour limit from 30 consecutive hours to 16 hours for first-year residents and 24 hours for all other residents. Investigators in this study evaluated the effect of the 2011 ACGME reforms on 30-day all-location mortality and 30-day all-cause readmissions. Patients included in the study were Medicare patients who were admitted to acute care US hospitals from 2009 to 2012 with acute myocardial infarction, stroke, gastrointestinal bleeding, or congestive heart failure, or those admitted for general, orthopedic, or vascular surgery. Hospitals were classified by their level of teaching intensity using a resident-to-bed ratio defined as the number of residents divided by the number of staffed beds.

In an analysis that adjusted for demographics, co-morbidities, and the presence of surgical complications, the implementation of work hour reforms did not affect 30-day mortality or readmissions in more-intensive teaching hospitals relative to less-intensive teaching hospitals during the postreform year as compared with 2 years before the reform. Multiple factors beyond the implementation of work hour reforms, may have contributed to this lack of effect. First, adherence to the new reforms by residency programs in the first year is unclear. Second, concurrent initiatives to improve patient outcomes during this time may have affected all hospitals, teaching and nonteaching. Finally, the authors suggest that the greater emphasis on resident supervision with the new reforms may have counterbalanced any negative effects of increased resident handoffs.

Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.

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Chlorhexidine Bathing Does Not Reduce Nosocomial Infections

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Chlorhexidine Bathing Does Not Reduce Nosocomial Infections

Clinical question: For critically ill patients, does daily bathing with chlorhexidine reduce health care–associated infections?

Bottom line

These results show that daily chlorhexidine bathing does not significantly affect the incidence of health care–associated infections. These data conflict with data from prior research, suggesting that more investigation is needed before incorporating chlorhexidine bathing into routine practice, especially given the increased cost with its use and the possibility of the development of chlorhexidine resistance. (LOE = 1b)

Reference: Noto MJ, Domenico HJ, Byrne DW, et al. Chlorhexidine bathing and health care-associated infections. JAMA 2015;313(4):369-378.

Study design: Cross-over trial (randomized)

Funding source: Government

Allocation: Concealed

Setting: Inpatient (ICU only)

Synopsis

Previous studies have shown benefit of daily chlorhexidine bathing in patients at high risk of nosocomial blood stream infections (Daily POEM 7-31-2013; Daily POEM 4-26-2013). In this study, investigators randomized 5 intensive care units at a tertiary care hospital to provide daily bathing of all patients with either 2% chlorhexidine-impregnated cloths or with nonantimicrobial cloths. Each unit followed the assigned protocol for 10 weeks, followed by a 2-week washout period, and then crossed over to the alternate protocol for another 10 weeks. All units crossed over 3 times during the study. Almost 10,000 patients were included in the study. The primary outcome was a composite of health-care associated infections, including central-line associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonia, and Clostridium difficile infections. There was no significant difference detected in the rate of the primary outcome between the chlorhexidine group and the control group with approximately 3 infections per 1000 patient-days in both groups. Adjusting for factors including demographics, co-morbidities, and the unit of admission also did not reveal a difference.

Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.

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Clinical question: For critically ill patients, does daily bathing with chlorhexidine reduce health care–associated infections?

Bottom line

These results show that daily chlorhexidine bathing does not significantly affect the incidence of health care–associated infections. These data conflict with data from prior research, suggesting that more investigation is needed before incorporating chlorhexidine bathing into routine practice, especially given the increased cost with its use and the possibility of the development of chlorhexidine resistance. (LOE = 1b)

Reference: Noto MJ, Domenico HJ, Byrne DW, et al. Chlorhexidine bathing and health care-associated infections. JAMA 2015;313(4):369-378.

Study design: Cross-over trial (randomized)

Funding source: Government

Allocation: Concealed

Setting: Inpatient (ICU only)

Synopsis

Previous studies have shown benefit of daily chlorhexidine bathing in patients at high risk of nosocomial blood stream infections (Daily POEM 7-31-2013; Daily POEM 4-26-2013). In this study, investigators randomized 5 intensive care units at a tertiary care hospital to provide daily bathing of all patients with either 2% chlorhexidine-impregnated cloths or with nonantimicrobial cloths. Each unit followed the assigned protocol for 10 weeks, followed by a 2-week washout period, and then crossed over to the alternate protocol for another 10 weeks. All units crossed over 3 times during the study. Almost 10,000 patients were included in the study. The primary outcome was a composite of health-care associated infections, including central-line associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonia, and Clostridium difficile infections. There was no significant difference detected in the rate of the primary outcome between the chlorhexidine group and the control group with approximately 3 infections per 1000 patient-days in both groups. Adjusting for factors including demographics, co-morbidities, and the unit of admission also did not reveal a difference.

Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.

Clinical question: For critically ill patients, does daily bathing with chlorhexidine reduce health care–associated infections?

Bottom line

These results show that daily chlorhexidine bathing does not significantly affect the incidence of health care–associated infections. These data conflict with data from prior research, suggesting that more investigation is needed before incorporating chlorhexidine bathing into routine practice, especially given the increased cost with its use and the possibility of the development of chlorhexidine resistance. (LOE = 1b)

Reference: Noto MJ, Domenico HJ, Byrne DW, et al. Chlorhexidine bathing and health care-associated infections. JAMA 2015;313(4):369-378.

Study design: Cross-over trial (randomized)

Funding source: Government

Allocation: Concealed

Setting: Inpatient (ICU only)

Synopsis

Previous studies have shown benefit of daily chlorhexidine bathing in patients at high risk of nosocomial blood stream infections (Daily POEM 7-31-2013; Daily POEM 4-26-2013). In this study, investigators randomized 5 intensive care units at a tertiary care hospital to provide daily bathing of all patients with either 2% chlorhexidine-impregnated cloths or with nonantimicrobial cloths. Each unit followed the assigned protocol for 10 weeks, followed by a 2-week washout period, and then crossed over to the alternate protocol for another 10 weeks. All units crossed over 3 times during the study. Almost 10,000 patients were included in the study. The primary outcome was a composite of health-care associated infections, including central-line associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonia, and Clostridium difficile infections. There was no significant difference detected in the rate of the primary outcome between the chlorhexidine group and the control group with approximately 3 infections per 1000 patient-days in both groups. Adjusting for factors including demographics, co-morbidities, and the unit of admission also did not reveal a difference.

Dr. Kulkarni is an assistant professor of hospital medicine at Northwestern University in Chicago.

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Aggressive infant leukemia has few mutations

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Aggressive infant leukemia has few mutations

Sleeping infant

Photo by Vera Kratochvil

Infants who have acute lymphoblastic leukemia (ALL) with MLL rearrangements have few other mutations, according to new research.

The findings suggest that targeting MLL rearrangements in these patients is likely the key to improving their survival.

“We frequently associate a cancer’s aggressiveness with its mutation rate, but this work indicates that the two don’t always go hand-in-hand,” said Richard K. Wilson, PhD, of the Washington University School of Medicine in St Louis, Missouri.

“Still, our findings provide a new direction for developing more effective treatments for these very young patients.”

Dr Wilson and his colleagues reported their findings in Nature Genetics.

The researchers performed whole-genome, exome, RNA, and targeted DNA sequencing to identify genetic alterations in 65 infants with ALL, including 47 with the MLL rearrangement.

The team was surprised to find that, despite being an aggressive leukemia, the MLL-rearranged subtype had among the lowest mutation rates reported for any cancer. The predominant leukemic clone carried a mean of 1.3 non-silent mutations.

“These results show that, to improve survival for patients with this aggressive leukemia, we need to develop drugs that target the abnormal proteins produced by the MLL fusion gene or that interact with the abnormal MLL fusion protein to shut down the cellular machinery that drives their tumors,” said James R. Downing, MD, of St Jude Research Hospital in Memphis, Tennessee. “That will not be easy, but this study found no obvious cooperating mutations to target.”

Almost half of infants with MLL-rearranged ALL (47%) had activating mutations in the kinase-PI3K-RAS signaling pathway. But the mutations were often present in only some of the leukemic cells.

Furthermore, the researchers analyzed leukemia cells in infants whose cancer returned after treatment and found that, at the time of relapse, the cells lacked these mutations.

“The fact that the mutations were often lost at relapse suggests that patients are unlikely to benefit from therapeutically targeting these mutations at diagnosis,” Dr Downing said.

The researchers also found that older children with MLL-rearranged leukemia had significantly more mutations than infants—a mean of 6.5 mutations per case (P=7.15 × 10−5).

Furthermore, 45% of the older children had mutations in genes that encode epigenetic regulatory proteins. And, aside from MLL, epigenetic regulators were rarely mutated in infants with MLL-rearranged ALL.

“While MLL belongs to a family of genes that encode epigenetic regulatory proteins, there was a striking difference between infants and older children regarding the frequency of mutations in other epigenetic regulatory genes,” said Anna Andersson, PhD, of Lund University in Sweden.

“This observation raises the possibility of a fundamental difference in the cell targeted for transformation in infants versus older patients,” said Tanja Gruber, MD, PhD, of St Jude.

“Our working hypothesis is that, in infants, the MLL rearrangement occurs in a developing blood cell, a prenatal progenitor cell, which requires fewer additional mutations to fully transform into leukemia. In contrast, in older patients, the MLL rearrangement isn’t enough on its own.”

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Sleeping infant

Photo by Vera Kratochvil

Infants who have acute lymphoblastic leukemia (ALL) with MLL rearrangements have few other mutations, according to new research.

The findings suggest that targeting MLL rearrangements in these patients is likely the key to improving their survival.

“We frequently associate a cancer’s aggressiveness with its mutation rate, but this work indicates that the two don’t always go hand-in-hand,” said Richard K. Wilson, PhD, of the Washington University School of Medicine in St Louis, Missouri.

“Still, our findings provide a new direction for developing more effective treatments for these very young patients.”

Dr Wilson and his colleagues reported their findings in Nature Genetics.

The researchers performed whole-genome, exome, RNA, and targeted DNA sequencing to identify genetic alterations in 65 infants with ALL, including 47 with the MLL rearrangement.

The team was surprised to find that, despite being an aggressive leukemia, the MLL-rearranged subtype had among the lowest mutation rates reported for any cancer. The predominant leukemic clone carried a mean of 1.3 non-silent mutations.

“These results show that, to improve survival for patients with this aggressive leukemia, we need to develop drugs that target the abnormal proteins produced by the MLL fusion gene or that interact with the abnormal MLL fusion protein to shut down the cellular machinery that drives their tumors,” said James R. Downing, MD, of St Jude Research Hospital in Memphis, Tennessee. “That will not be easy, but this study found no obvious cooperating mutations to target.”

Almost half of infants with MLL-rearranged ALL (47%) had activating mutations in the kinase-PI3K-RAS signaling pathway. But the mutations were often present in only some of the leukemic cells.

Furthermore, the researchers analyzed leukemia cells in infants whose cancer returned after treatment and found that, at the time of relapse, the cells lacked these mutations.

“The fact that the mutations were often lost at relapse suggests that patients are unlikely to benefit from therapeutically targeting these mutations at diagnosis,” Dr Downing said.

The researchers also found that older children with MLL-rearranged leukemia had significantly more mutations than infants—a mean of 6.5 mutations per case (P=7.15 × 10−5).

Furthermore, 45% of the older children had mutations in genes that encode epigenetic regulatory proteins. And, aside from MLL, epigenetic regulators were rarely mutated in infants with MLL-rearranged ALL.

“While MLL belongs to a family of genes that encode epigenetic regulatory proteins, there was a striking difference between infants and older children regarding the frequency of mutations in other epigenetic regulatory genes,” said Anna Andersson, PhD, of Lund University in Sweden.

“This observation raises the possibility of a fundamental difference in the cell targeted for transformation in infants versus older patients,” said Tanja Gruber, MD, PhD, of St Jude.

“Our working hypothesis is that, in infants, the MLL rearrangement occurs in a developing blood cell, a prenatal progenitor cell, which requires fewer additional mutations to fully transform into leukemia. In contrast, in older patients, the MLL rearrangement isn’t enough on its own.”

Sleeping infant

Photo by Vera Kratochvil

Infants who have acute lymphoblastic leukemia (ALL) with MLL rearrangements have few other mutations, according to new research.

The findings suggest that targeting MLL rearrangements in these patients is likely the key to improving their survival.

“We frequently associate a cancer’s aggressiveness with its mutation rate, but this work indicates that the two don’t always go hand-in-hand,” said Richard K. Wilson, PhD, of the Washington University School of Medicine in St Louis, Missouri.

“Still, our findings provide a new direction for developing more effective treatments for these very young patients.”

Dr Wilson and his colleagues reported their findings in Nature Genetics.

The researchers performed whole-genome, exome, RNA, and targeted DNA sequencing to identify genetic alterations in 65 infants with ALL, including 47 with the MLL rearrangement.

The team was surprised to find that, despite being an aggressive leukemia, the MLL-rearranged subtype had among the lowest mutation rates reported for any cancer. The predominant leukemic clone carried a mean of 1.3 non-silent mutations.

“These results show that, to improve survival for patients with this aggressive leukemia, we need to develop drugs that target the abnormal proteins produced by the MLL fusion gene or that interact with the abnormal MLL fusion protein to shut down the cellular machinery that drives their tumors,” said James R. Downing, MD, of St Jude Research Hospital in Memphis, Tennessee. “That will not be easy, but this study found no obvious cooperating mutations to target.”

Almost half of infants with MLL-rearranged ALL (47%) had activating mutations in the kinase-PI3K-RAS signaling pathway. But the mutations were often present in only some of the leukemic cells.

Furthermore, the researchers analyzed leukemia cells in infants whose cancer returned after treatment and found that, at the time of relapse, the cells lacked these mutations.

“The fact that the mutations were often lost at relapse suggests that patients are unlikely to benefit from therapeutically targeting these mutations at diagnosis,” Dr Downing said.

The researchers also found that older children with MLL-rearranged leukemia had significantly more mutations than infants—a mean of 6.5 mutations per case (P=7.15 × 10−5).

Furthermore, 45% of the older children had mutations in genes that encode epigenetic regulatory proteins. And, aside from MLL, epigenetic regulators were rarely mutated in infants with MLL-rearranged ALL.

“While MLL belongs to a family of genes that encode epigenetic regulatory proteins, there was a striking difference between infants and older children regarding the frequency of mutations in other epigenetic regulatory genes,” said Anna Andersson, PhD, of Lund University in Sweden.

“This observation raises the possibility of a fundamental difference in the cell targeted for transformation in infants versus older patients,” said Tanja Gruber, MD, PhD, of St Jude.

“Our working hypothesis is that, in infants, the MLL rearrangement occurs in a developing blood cell, a prenatal progenitor cell, which requires fewer additional mutations to fully transform into leukemia. In contrast, in older patients, the MLL rearrangement isn’t enough on its own.”

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FDA approves first biosimilar product

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FDA approves first biosimilar product

The US Food and Drug Administration (FDA) has approved the leukocyte growth factor Zarxio (filgrastim-sndz), the first biosimilar product to be approved in the US.

A biosimilar product is approved based on data showing that it is highly similar to an already-approved biological product.

Sandoz Inc’s Zarxio is biosimilar to Amgen Inc’s Neupogen (filgrastim), which was originally licensed in 1991. Zarxio is now approved for the same indications as Neupogen.

Zarxio can be prescribed for:

  • patients with cancer receiving myelosuppressive chemotherapy
  • patients with acute myeloid leukemia receiving induction or consolidation chemotherapy
  • patients with cancer undergoing bone marrow transplant
  • patients undergoing autologous peripheral blood progenitor cell collection and therapy
  • patients with severe chronic neutropenia.

Zarxio is marketed as Zarzio outside the US. The biosimilar is available in more than 60 countries worldwide.

“Biosimilars will provide access to important therapies for patients who need them,” said FDA Commissioner Margaret A. Hamburg, MD.

“Patients and the healthcare community can be confident that biosimilar products approved by the FDA meet the agency’s rigorous safety, efficacy, and quality standards.”

Zarxio data

The FDA’s approval of Zarxio is based on a review of evidence that included structural and functional characterization, in vivo data, human pharmacokinetic and pharmacodynamics data, clinical immunogenicity data, and other clinical safety and effectiveness data that demonstrates Zarxio is biosimilar to Neupogen.

The PIONEER study was the final piece of data the FDA used to approve Zarxio as biosimilar to Neupogen. The data was sufficient to allow extrapolation of the use of Zarxio to all indications of Neupogen.

In the PIONEER study, Zarxio and Neupogen both produced the expected reduction in the duration of severe neutropenia in cancer patients undergoing myelosuppressive chemotherapy—1.17 and 1.20 days, respectively.

The mean time to absolute neutrophil count recovery in cycle 1 was also similar—1.8 ± 0.97 days in the Zarxio arm and 1.7 ± 0.81 days in the Neupogen arm. No immunogenicity or antibodies against rhG-CSF were detected throughout the study.

The most common side effects of Zarxio are aching in the bones or muscles and redness, swelling, or itching at the injection site. Serious side effects may include spleen rupture; serious allergic reactions that may cause rash, shortness of breath, wheezing and/or swelling around the mouth and eyes; fast pulse and sweating; and acute respiratory distress syndrome.

About biosimilar approval

The Biologics Price Competition and Innovation Act of 2009 (BPCI Act) was passed as part of the Affordable Care Act that President Barack Obama signed into law in March 2010. The BPCI Act created an abbreviated licensure pathway for biological products shown to be “biosimilar” to or “interchangeable” with an FDA-licensed biological product, known as the reference product.

This abbreviated licensure pathway under section 351(k) of the Public Health Service Act permits reliance on certain existing scientific knowledge about the safety and effectiveness of the reference product, and it enables a biosimilar biological product to be licensed based on less than a full complement of product-specific preclinical and clinical data.

A biosimilar product can only be approved by the FDA if it has the same mechanism(s) of action, route(s) of administration, dosage form(s) and strength(s) as the reference product, and only for the indication(s) and condition(s) of use that have been approved for the reference product. The facilities where biosimilars are manufactured must also meet the FDA’s standards.

There must be no clinically meaningful differences between the biosimilar and the reference product in terms of safety and effectiveness. Only minor differences in clinically inactive components are allowable.

Zarxio has been approved as a biosimilar, not an interchangeable product. Under the BPCI Act, a biological product that has been approved as “interchangeable” may be substituted for the reference product without the intervention of the healthcare provider who prescribed the reference product.

 

 

For Zarxio’s approval, the FDA has designated a placeholder nonproprietary name for this product as “filgrastim-sndz.” The provision of a placeholder nonproprietary name should not be viewed as reflective of the agency’s decision on a comprehensive naming policy for biosimilars and other biological products.

While the FDA has not yet issued draft guidance on how current and future biological products marketed in the US should be named, the agency intends to do so in the near future.

For more details on Zarxio, see the full prescribing information.

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The US Food and Drug Administration (FDA) has approved the leukocyte growth factor Zarxio (filgrastim-sndz), the first biosimilar product to be approved in the US.

A biosimilar product is approved based on data showing that it is highly similar to an already-approved biological product.

Sandoz Inc’s Zarxio is biosimilar to Amgen Inc’s Neupogen (filgrastim), which was originally licensed in 1991. Zarxio is now approved for the same indications as Neupogen.

Zarxio can be prescribed for:

  • patients with cancer receiving myelosuppressive chemotherapy
  • patients with acute myeloid leukemia receiving induction or consolidation chemotherapy
  • patients with cancer undergoing bone marrow transplant
  • patients undergoing autologous peripheral blood progenitor cell collection and therapy
  • patients with severe chronic neutropenia.

Zarxio is marketed as Zarzio outside the US. The biosimilar is available in more than 60 countries worldwide.

“Biosimilars will provide access to important therapies for patients who need them,” said FDA Commissioner Margaret A. Hamburg, MD.

“Patients and the healthcare community can be confident that biosimilar products approved by the FDA meet the agency’s rigorous safety, efficacy, and quality standards.”

Zarxio data

The FDA’s approval of Zarxio is based on a review of evidence that included structural and functional characterization, in vivo data, human pharmacokinetic and pharmacodynamics data, clinical immunogenicity data, and other clinical safety and effectiveness data that demonstrates Zarxio is biosimilar to Neupogen.

The PIONEER study was the final piece of data the FDA used to approve Zarxio as biosimilar to Neupogen. The data was sufficient to allow extrapolation of the use of Zarxio to all indications of Neupogen.

In the PIONEER study, Zarxio and Neupogen both produced the expected reduction in the duration of severe neutropenia in cancer patients undergoing myelosuppressive chemotherapy—1.17 and 1.20 days, respectively.

The mean time to absolute neutrophil count recovery in cycle 1 was also similar—1.8 ± 0.97 days in the Zarxio arm and 1.7 ± 0.81 days in the Neupogen arm. No immunogenicity or antibodies against rhG-CSF were detected throughout the study.

The most common side effects of Zarxio are aching in the bones or muscles and redness, swelling, or itching at the injection site. Serious side effects may include spleen rupture; serious allergic reactions that may cause rash, shortness of breath, wheezing and/or swelling around the mouth and eyes; fast pulse and sweating; and acute respiratory distress syndrome.

About biosimilar approval

The Biologics Price Competition and Innovation Act of 2009 (BPCI Act) was passed as part of the Affordable Care Act that President Barack Obama signed into law in March 2010. The BPCI Act created an abbreviated licensure pathway for biological products shown to be “biosimilar” to or “interchangeable” with an FDA-licensed biological product, known as the reference product.

This abbreviated licensure pathway under section 351(k) of the Public Health Service Act permits reliance on certain existing scientific knowledge about the safety and effectiveness of the reference product, and it enables a biosimilar biological product to be licensed based on less than a full complement of product-specific preclinical and clinical data.

A biosimilar product can only be approved by the FDA if it has the same mechanism(s) of action, route(s) of administration, dosage form(s) and strength(s) as the reference product, and only for the indication(s) and condition(s) of use that have been approved for the reference product. The facilities where biosimilars are manufactured must also meet the FDA’s standards.

There must be no clinically meaningful differences between the biosimilar and the reference product in terms of safety and effectiveness. Only minor differences in clinically inactive components are allowable.

Zarxio has been approved as a biosimilar, not an interchangeable product. Under the BPCI Act, a biological product that has been approved as “interchangeable” may be substituted for the reference product without the intervention of the healthcare provider who prescribed the reference product.

 

 

For Zarxio’s approval, the FDA has designated a placeholder nonproprietary name for this product as “filgrastim-sndz.” The provision of a placeholder nonproprietary name should not be viewed as reflective of the agency’s decision on a comprehensive naming policy for biosimilars and other biological products.

While the FDA has not yet issued draft guidance on how current and future biological products marketed in the US should be named, the agency intends to do so in the near future.

For more details on Zarxio, see the full prescribing information.

The US Food and Drug Administration (FDA) has approved the leukocyte growth factor Zarxio (filgrastim-sndz), the first biosimilar product to be approved in the US.

A biosimilar product is approved based on data showing that it is highly similar to an already-approved biological product.

Sandoz Inc’s Zarxio is biosimilar to Amgen Inc’s Neupogen (filgrastim), which was originally licensed in 1991. Zarxio is now approved for the same indications as Neupogen.

Zarxio can be prescribed for:

  • patients with cancer receiving myelosuppressive chemotherapy
  • patients with acute myeloid leukemia receiving induction or consolidation chemotherapy
  • patients with cancer undergoing bone marrow transplant
  • patients undergoing autologous peripheral blood progenitor cell collection and therapy
  • patients with severe chronic neutropenia.

Zarxio is marketed as Zarzio outside the US. The biosimilar is available in more than 60 countries worldwide.

“Biosimilars will provide access to important therapies for patients who need them,” said FDA Commissioner Margaret A. Hamburg, MD.

“Patients and the healthcare community can be confident that biosimilar products approved by the FDA meet the agency’s rigorous safety, efficacy, and quality standards.”

Zarxio data

The FDA’s approval of Zarxio is based on a review of evidence that included structural and functional characterization, in vivo data, human pharmacokinetic and pharmacodynamics data, clinical immunogenicity data, and other clinical safety and effectiveness data that demonstrates Zarxio is biosimilar to Neupogen.

The PIONEER study was the final piece of data the FDA used to approve Zarxio as biosimilar to Neupogen. The data was sufficient to allow extrapolation of the use of Zarxio to all indications of Neupogen.

In the PIONEER study, Zarxio and Neupogen both produced the expected reduction in the duration of severe neutropenia in cancer patients undergoing myelosuppressive chemotherapy—1.17 and 1.20 days, respectively.

The mean time to absolute neutrophil count recovery in cycle 1 was also similar—1.8 ± 0.97 days in the Zarxio arm and 1.7 ± 0.81 days in the Neupogen arm. No immunogenicity or antibodies against rhG-CSF were detected throughout the study.

The most common side effects of Zarxio are aching in the bones or muscles and redness, swelling, or itching at the injection site. Serious side effects may include spleen rupture; serious allergic reactions that may cause rash, shortness of breath, wheezing and/or swelling around the mouth and eyes; fast pulse and sweating; and acute respiratory distress syndrome.

About biosimilar approval

The Biologics Price Competition and Innovation Act of 2009 (BPCI Act) was passed as part of the Affordable Care Act that President Barack Obama signed into law in March 2010. The BPCI Act created an abbreviated licensure pathway for biological products shown to be “biosimilar” to or “interchangeable” with an FDA-licensed biological product, known as the reference product.

This abbreviated licensure pathway under section 351(k) of the Public Health Service Act permits reliance on certain existing scientific knowledge about the safety and effectiveness of the reference product, and it enables a biosimilar biological product to be licensed based on less than a full complement of product-specific preclinical and clinical data.

A biosimilar product can only be approved by the FDA if it has the same mechanism(s) of action, route(s) of administration, dosage form(s) and strength(s) as the reference product, and only for the indication(s) and condition(s) of use that have been approved for the reference product. The facilities where biosimilars are manufactured must also meet the FDA’s standards.

There must be no clinically meaningful differences between the biosimilar and the reference product in terms of safety and effectiveness. Only minor differences in clinically inactive components are allowable.

Zarxio has been approved as a biosimilar, not an interchangeable product. Under the BPCI Act, a biological product that has been approved as “interchangeable” may be substituted for the reference product without the intervention of the healthcare provider who prescribed the reference product.

 

 

For Zarxio’s approval, the FDA has designated a placeholder nonproprietary name for this product as “filgrastim-sndz.” The provision of a placeholder nonproprietary name should not be viewed as reflective of the agency’s decision on a comprehensive naming policy for biosimilars and other biological products.

While the FDA has not yet issued draft guidance on how current and future biological products marketed in the US should be named, the agency intends to do so in the near future.

For more details on Zarxio, see the full prescribing information.

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Inpatient Ambulation

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Stepping toward discharge: Level of ambulation in hospitalized patients

A number of observational studies have documented the association between prolonged bed rest during hospitalization with adverse short‐ and long‐term functional impairments and disability in older patients.[1, 2, 3, 4] However, the body of evidence on the benefits of early mobilization on functional outcomes in both critically ill patients and more stable patients on medical‐surgical floors remains inconclusive.[5, 6, 7, 8, 9] Despite the increased emphasis on mobilizing patients early and often in the inpatient setting, there is surprisingly little information available regarding how typically active adult patients are during their hospital stay. The few published studies that are available are limited by small samples and types of patients who were monitored.[10, 11, 12, 13, 14] Therefore, the purpose of this real‐world study was to describe the level of ambulation in a large sample of hospitalized adult patients using a validated consumer‐grade wireless accelerometer.

METHODS

This was a prospective cohort study of ambulatory patients from 3 medical‐surgical units of a community hospital from March 2014 through July 2014. The study was approved by the Kaiser Permanente Southern California Institutional Review Board. All ambulatory medical and surgical adult patients were eligible for the study except for those with isolation precautions. Patients wore an accelerometer (Tractivity; Kineteks Corp., Vancouver, BC, Canada) on the ankle from soon after admission to the unit until discharge home. The sensors were only removed for bathing and medical procedures, at which time the devices were secured to the patient's bed and reworn upon their return to the room. The nursing staff was trained to use the vendor application to register the sensor to the patient, secure the sensor to the patient's ankle, transfer the sensor data to the vendor server, review the step counts on the web application, and manually key the step count into the electronic medical records (EMRs) as part of routine nursing workflow. The staff otherwise continued with usual patient mobilization practices.

We previously validated the Tractivity device in a field study of 20 hospitalized patients using a research‐grade accelerometer, Stepwatch, as the gold standard (unpublished data). We found that the inter‐Tractivity device reliability was near perfect (intraclass correlation=0.99), and that the Tractivity step counts correlated highly with the nurses' documentation on a paper log of distance walked measured in feet (r=0.76). A small number of steps (<100) were recorded over 24 hours when the device was worn by 2 bed bound patients. The 24‐hour Tractivity step count had acceptable limits of agreement with the Stepwatch (+284 [standard deviation: 314] steps; 95% limits of agreement 911‐343). In addition, for the current study, when we examined the step counts between patients who were classified by the nursing team as being able to walk <50 feet (n=320) compared to patients who were able to walk >50 feet (n=434), we found a significant difference in the median number of steps over a 24‐hour period (854 vs 1697, P<0.0001).

The step count data were exported from the vendor's server, examined for irregularities, and merged with administrative and clinical data for analysis. Data extracted from the EMR system included sociodemographic (age, gender, marital status, and race/ethnicity) and clinical characteristics (LACE score [readmission risk score based on length of stay (L); acuity of the admission (A); comorbidity of the patient (measured with the Charlson comorbidity index score) (C); and emergency department use (measured as the number of visits in the six months before admission) (E),[15] Charlson Comorbidity Index, length of stay, principal discharge diagnosis, and body mass index), and nursing documentation of functional status (bed bound, sit up in bed, stand next to bed, walk <50 feet, and walk >50 feet).

Descriptive statistics and nonparametric tests (Kruskal‐Wallis and Wilcoxon signed rank) were used to analyze the non‐normally distributed step count data. Quantile regression[16] was used to determine the association between the frequency of the care team's review and documentation of steps, with median total step count adjusting for age, gender, LACE score, and medicine/surgical service line. Whereas linear regression allows one to describe how the mean of a given outcome changes with respect to some set of covariates in circumstances where data are normally distributed, quantile regression allows one to assess how a set of covariates are related to a prespecified quantile (eg, 50% percentile median) of an outcome distribution. This modeling is especially appropriate here, because step count data are not normally distributed. Because step counts can vary with a number of factors, such as age and principal admitting and discharge diagnoses, we stratified our analyses by age (<65 or 65 years) and service lines (medical or surgical) due to the relatively small numbers of patients in each of the diagnostic groupings. Statistical analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC); P values <0.05 were considered statistically significant.

RESULTS

A total of 1667 patients wore the activity sensor during their hospital stay. We included 777 patients in our analysis who had lengths of stay long enough for 24 hours of continuous monitoring, and almost half of these patients had at least 48 hours of monitoring (n=378). The demographic and clinical characteristics of the sample are detailed in Table 1. The sample included mostly medical patients (77%), with a mean age of 6017 years, 57% females, and 55% nonwhites. Nearly all patients (97%) were classified as ambulatory at discharge based on the EMR data. Approximately 44% of the sensors were lost, mostly due to nursing staff forgetting to remove the devices at discharge; device failure was minimal (n=10).

Sample Characteristics of Patients With 24 Hours of Monitoring Discharged to Home (n=777)
Variables Value
  • NOTE: Data are presented as either meanstandard deviation or count (%).Preadmission level of function that was documented closest to admission time was used. The modal current level of function score in last 24 hours prior to discharge was used. LACE is the readmission risk score based on length of stay (L); acuity of the admission (A); comorbidity of the patient (measured with the Charlson comorbidity index score) (C); and emergency department use (measured as the number of visits in the six months before admission) (E). *Other categories include complications of pregnancy/childbirth, hematologic, other musculoskeletal and skin/subcutaneous disorders, injuries and poisoning, mental illness, other ill‐defined conditions.

Sociodemographics
Age
1840 years 111 (15%)
4165 years 325 (42%)
6575 years 187 (24%)
75 years 151 (19%)
Females 444 (57%)
Race/ethnicity
White 349 (45%)
Hispanics 277 (35%)
African American 101 (13%)
Asian/Pacific Islander 37 (5%)
Other 13 (2%)
Marital status
Partnered 435 (56%)
Unpartnered 332 (43%)
Other/unknown 10 (1%)
Clinical characteristics
Medical (principal discharge diagnoses)
Cardiovascular 116 (15%)
Respiratory 84 (11%)
Gastrointestinal 122 (16%)
Genitourinary 31 (4%)
Metabolic/electrolytes 26 (3%)
Septicemia 92 (12%)
Nervous system 21 (3%)
Cancer/malignancies 13 (1%)
Other* 103 (13%)
Surgical
Orthopedic surgery 60 (8%)
Other surgeries 109 (14%)
LACE score 9.33.5
Charlson index
01 665 (85%)
23 98 (13%)
4+ 14 (2%)
Length of stay, d 3.983.80
Body mass index 30.27.5
Functional status
Preadmission level of function
1, bed bound 3 (0.5%)
2, able to sit 6 (1%)
3, stand next to bed 3 (0.5%)
4, walk <50 feet 113 (14%)
5, walk >50 feet 651 (84%)
Missing 1 (0%)
Current level of function
1, bed bound 1 (0%)
2, able to sit 6 (1%)
3, stand next to bed 7 (1%)
4, walk <50 feet 320 (41%)
5, walk >50 feet 434 (56%)
Missing 9 (1%)

Patients accrued a median of 1158 (interquartile range: 6362238) steps over the 24 hours prior to discharge to home (Table 2). Approximately 13 (2%) patients registered zero steps in the last 24 hours; this may have been due to patients truly not accruing any steps, device failure, or the device was registered but never worn by the patient. Patients who were 65 years and older on both the medicine and surgical services accrued fewer steps compared to younger patients (962 vs 1294, P<0.0001). For patients who had at least 48 hours of continuous monitoring (n=378), there was a median increase of 377 steps from the first 24 hours from admission to the unit to the final 24 hours prior to discharge (811 steps to 1188 steps, P<0.0001) (Table 3 and Figure 1). The average length of stay for these patients was 5.74.9 days. Despite the longer length of stay, the level of ambulation at discharge was similar to patients with shorter stays. This is further illustrated in Figure 2 in the spaghetti plots of total steps over 4, 24‐hour monitoring increments. Ignoring the outliers, the plots suggest the following: (1) step counts tended to increase or stay about the same over the course of a hospitalization; and (2) for the medicine service line, step counts in the final 24 hours prior to discharge for patients with longer lengths of stay (72 or 96 hours) did not appear to be substantially different from patients with shorter lengths of stay. The data for the surgical patients are either too sparse or erratic to make any firm conclusions. Patients accrued steps throughout the day with the highest percentage of steps logged at approximately 6 am and 6 pm; these data are based on time stamps from the device, not the time of data transfer or documentation in the EMR (Figure 3).

Total Step Count in the Last 24 Hours Prior to Discharge to Home for Patients With 24 Hours of Monitoring
Service Total Steps Last 24 Hours
Mean SD Median
  • NOTE: Abbreviations: SD, standard deviation.

Medicine
<65 years old (n=321) 1,972 1,995 1,284
65 years old (n=287) 1,367 1,396 968
Surgical
<65 years old (n=118) 2,238 2,082 1,378
65 years old (n=51) 1,485 1,647 890
Total (n=777) 1,757 1,818 1,158
Total Step Count in the First 24 Hours of Admission to the Medical‐Surgical Unit and Last 24 Hours Prior to Discharge to Home for Patients With 48 Hours of Monitoring
Service Total Steps
First 24 Hours Last 24 Hours
Mean SD Median Mean SD Median
  • NOTE: Abbreviations: SD, standard deviation.

Medicine
<65 years old (n=168) 1,427 1,690 953 2,005 2,006 1,287
65 years old (n=127) 1,004 1,098 676 1,260 1,291 904
Surgical
<65 years old (n=53) 1,722 1,696 1060 2,553 2,142 1,882
65 years old (n=30) 1,184 1,470 704 1,829 1,996 1,053
Total (n=378) 1,307 1,515 811 1,817 1,864 1,188
Figure 1
Box plots of total step counts in the first 24 hours of admission to the medical‐surgical unit and last 24 hours prior to discharge to home for patients with ≥48 hours of monitoring by age and service line.
Figure 2
Spaghetti plots of total step counts for each 24‐hour monitoring period by age (<65 and ≥65 years) and service line (medical or surgical). Sample sizes are as follows: 24 hours (black dots, n = 399), 48 hours (red lines, n = 190), 72 hours (green lines, n = 80), 96 hours (blue lines, n = 108).
Figure 3
Distribution of step counts by percentage of accrued steps over 24 hours prior to discharge.

More frequent documentation of step counts in the EMR (proxy for step count data retrieval and review from the vendor web site) by the care team was associated with higher total step counts after adjustments for relevant covariates (P0.001); 3 or more documentations over a 24‐hour period appears to be a minimal frequency to achieving approximately 200 steps more than the median value (Table 4).

Association Between Frequency of Step Count Documentation in the EMR and Total Step Counts in the Last 24 Hours Prior to Discharge to Home for Those With at Least 24 Hours of Observation
Service Frequency of Documentation of Step Counts in EMR Over 24 Hours P Value Trenda Adjusted P Valueb
0 1 2 3 4
  • NOTE: Abbreviations: EMR, electronic medical record; SD, standard deviation.

  • P value for trend (quantile regression for median step counts).

  • Adjusted for age, gender, LACE score (readmission risk score based on length of stay (L); acuity of the admission (A); comorbidity of the patient (measured with the Charlson comorbidity index score) (C); and emergency department use (measured as the number of visits in the six months before admission) (E), and service line (medicine/surgical) where relevant.

Medicine
<65 years old (n=321) MeanSD 1,4051,414 2,4152,037 2,0101,929 1,9811,907 2,7412,876
Median 1,056 1,514 1284 1,196 1,702 0.004 0.003
N (%) 83 (26%) 109 (34%) 71 (22%) 25 (8%) 33 (10%)
65 years old (n=287) MeanSD 1,3481,711 1,1991428 1,290951 1,5291,180 1,8781,214
Median 850 773 999 1,278 1,498 0.07 0.10
N (%) 85 (30%) 82 (28%) 66 (23%) 20 (7%) 34 (12%)
Surgical
<65 years old (n=118) MeanSD 2,0772,001 1,8591,598 2,6182,536 2,3122,031 3,8022,979
Median 1,361 1,250 1,181 1,719 3,149 0.06 0.05
N (%) 42 (35%) 36 (31%) 18 (15%) 14 (12%) 8 (7%)
65 years old (n=51) MeanSD 2,0032,254 1,4781,603 1,1651,246 478 1,219469
Median 1,028 820 672 478 1,426 0.20 0.15
N (%) 13 (26%) 19 (37%) 15 (29%) 1 (2%) 3 (6%)
Total (n=777) MeanSD 1,5441,717 1,7361,799 1,7201,699 1,8831720 2,4152,304
Median 1,012 1,116 1,124 1,314 1,557 <0.001 <0.001
N (%) 223 (29%) 246 (31%) 170 (22%) 60 (8%) 78 (10%)

DISCUSSION

We found that ambulatory medical‐surgical patients accrued a median of 1158 total steps in the 24 hours prior to their discharge home, which translates to walking approximately 500 meters; older patients accrued fewer steps compared to younger patients. In patients with longer length of stay, the level of ambulation at discharge was similar to patients with shorter stays, suggesting there may be an ambulation threshold (1100 steps) that patients achieve regardless of the length of stay before they are discharged home. In addition, patients whose care team reviewed and documented step counts at least 3 times over a 24‐hour period accrued significantly more steps than patients whose care team made fewer documentations.

The median step counts accrued by surgical patients in our study are similar to that found in Cook and colleagues'[14] report of patients after elective cardiac surgery using another popular consumer‐grade accelerometer. The providers in that study also had access to the data via a dashboard, but it was not clear how this information was used. Brown et al.[12] conducted the first study to objectively monitor mobility using 2 accelerometers in 45 older male veterans who had no prior mobility impairment, and found that patients spent 83% of their hospitalization lying in bed. The veterans spent about 3% of the time (43 minutes per day) standing or walking over a mean length of stay of 5 days. In a similar study with 43 older Dutch patients who had an average length of stay of 7 days, Pedersen et al.[10] found that patients spent 71% of their time lying, 21% sitting, and 4% standing or walking. Unfortunately, neither the Brown et al. nor Pedersen et al. studies were able to distinguish between standing and ambulatory activities. In a more recent study of 47 patients on medical‐surgical units at 2 hospitals that relied on time and motion observation methods, the mean duration for ambulation was <2 minutes during an 8‐hour period.[13]

We took advantage of the variability in the nursing documentation of step counts in the EMR to determine if there was a dose‐response relationship between the frequency of nursing documentation in a 24‐hour period and number of steps patients accrued. We hypothesized that if nurses make an effort to retrieve data from the vendor website and manually key in the step counts in the EMR, they are more likely to incorporate this information in their nursing care, share the information with patients and other clinicians, and therefore create a positive feedback loop for greater ambulation. Although our findings suggest a positive association between more frequent documentation and increased step counts, we cannot exclude the possibility that nurses naturally modulate the frequency with which they review and document step counts based on their overall judgment of the patients' mobility status (ie, patients who are more functionally impaired are assumed to accrue fewer steps over a shift, and therefore, nurses are less inclined to retrieve and document the information frequently). Future studies could prospectively examine what the optimal frequency for review and feedback of step counts is during a typical 8‐ or 12‐hour nursing shift for both patients and the nursing care team to promote ambulation.

A major strength of our study is the collection of objective ambulation data on a large inpatient sample by clinical staff as part of routine nursing care. This strength is balanced with several limitations. Due to the temporal pattern associated with ambulation, we were only able to analyze data for patients who had at least 24 hours of continuous monitoring. This could affect the generalizability of our findings, though we believe there is limited pragmatic value in closely tracking ambulation in patients who have such short stays. There was substantial variability in the step counts, reflecting the mix of medical versus surgical patients and their age, with very small samples available for meaningful subgroup analyses other than what we have presented. We were not able to measure other dimensions of mobility such as transfers or sitting in a chair, because the sensor is designed to only measure steps. In addition, we lost a large number of devices, mostly due to staff forgetting to remove the devices from patients' ankles at discharge. Finally, because we did not blind the nurses and patients to the step count data, the preliminary normative step counts that we present in this article may be higher than expected in patients cared for on medical‐surgical units.

In summary, we found that it is possible to measure ambulation objectively and reliably in hospitalized patients, and have provided preliminary normative step counts for a representative but heterogeneous medical‐surgical population. We also found that most patients who were discharged were ambulating at least 1100 steps over the 24 hours prior to leaving the hospital, regardless of their length of stay. This might suggest that step counts could be a useful parameter in determining readiness for hospital discharge. Our data also suggest that more frequent, objective monitoring of step counts by the nursing care team was associated with patients ambulating more. Both of these findings deserve further exploration. Future studies will need to be conducted on larger samples of medical and surgical hospitalized patients to adequately establish more refined step count norms for specific clinical populations, but especially for older patients, because this age group is at a particularly higher risk of poor functional outcomes with hospitalization. Having accurate and reliable information on ambulation is fundamental to any effort to improve ambulation in hospitalized patients. Moreover, knowing the normative range for step counts in the last 24 hours prior to discharge across specific clinical and age subgroups, could assist with discharge planning and provision of appropriate rehabilitative services in the home or community for safe transitions out of the hospital.[17]

Acknowledgements

The authors express their gratitude to the patients and nurses at the Kaiser Permanente Southern California, Ontario Medical Center.

Disclosures: Funded by the Kaiser Permanente Southern California Care Improvement Research Team. Dr. Sallis contributed substantially to the study design, interpretation, and preparation of this article. Ms. Sturm and Chijioke contributed to the interpretation and preparation of this article. Dr. Kanter contributed to study design, interpretation, and preparation of this article. Mr. Huang contributed to the analysis, interpretation, and preparation of this article. Dr. Shen contributed to study design, analysis, interpretation, and preparation of this article. Dr. Nguyen had full access to the data and led the design, analysis, interpretation, and preparation of this article. Dr. Nguyen had full access to the data and will vouch for the integrity of the work as a whole, from inception to published article. The authors have no funding, financial relationships, or conflicts of interest to disclose.

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References
  1. Brown CJ, Friedkin RJ, Inouye SK. Prevalence and outcomes of low mobility in hospitalized older patients. J Am Geriatr Soc. 2004;52(8):12631270.
  2. Zisberg A, Shadmi E, Sinoff G, Gur‐Yaish N, Srulovici E, Admi H. Low mobility during hospitalization and functional decline in older adults. J Am Geriatr Soc. 2011;59(2):266273.
  3. Hirsch CH, Sommers L, Olsen A, Mullen L, Winograd CH. The natural history of functional morbidity in hospitalized older patients. J Am Geriatr Soc. 1990;38(12):12961303.
  4. Fisher SR, Kuo YF, Graham JE, Ottenbacher KJ, Ostir GV. Early ambulation and length of stay in older adults hospitalized for acute illness. Arch Intern Med. 2010;170(21):19421943.
  5. Adler J, Malone D. Early mobilization in the intensive care unit: a systematic review. Cardiopulm Phys Ther J. 2012;23(1):513.
  6. Kalisch BJ, Lee S, Dabney BW. Outcomes of inpatient mobilization: a literature review. J Clin Nurs. 2014;23(11–12):14861501.
  7. Greening NJ, Williams JE, Hussain SF, et al. An early rehabilitation intervention to enhance recovery during hospital admission for an exacerbation of chronic respiratory disease: randomised controlled trial. BMJ. 2014;349:g4315.
  8. Morton NA, Keating JL, Berlowitz DJ, Jackson B, Lim WK. Additional exercise does not change hospital or patient outcomes in older medical patients: a controlled clinical trial. Aust J Physiother. 2007;53(2):105111.
  9. Morton NA, Keating JL, Jeffs K. Exercise for acutely hospitalised older medical patients. Cochrane Database Syst Rev. 2007;(1):CD005955.
  10. Pedersen MM, Bodilsen AC, Petersen J, et al. Twenty‐four‐hour mobility during acute hospitalization in older medical patients. J Gerontol A Biol Sci Med Sci. 2013;68(3):331337.
  11. Ostir GV, Berges IM, Kuo YF, Goodwin JS, Fisher SR, Guralnik JM. Mobility activity and its value as a prognostic indicator of survival in hospitalized older adults. J Am Geriatr Soc. 2013;61(4):551557.
  12. Brown CJ, Redden DT, Flood KL, Allman RM. The underrecognized epidemic of low mobility during hospitalization of older adults. J Am Geriatr Soc. 2009;57(9):16601665.
  13. Doherty‐King B, Yoon JY, Pecanac K, Brown R, Mahoney J. Frequency and duration of nursing care related to older patient mobility. J Nurs Scholarsh. 2014;46(1):2027.
  14. Cook DJ, Thompson JE, Prinsen SK, Dearani JA, Deschamps C. Functional recovery in the elderly after major surgery: assessment of mobility recovery using wireless technology. Ann Thorac Surg. 2013;96(3):10571061.
  15. Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551557.
  16. Koenker R, Hallock K. Quantile regression: an introduction. J Econ Perspect. 2001;15(4):4356.
  17. Krumholz HM. Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100102.
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Journal of Hospital Medicine - 10(6)
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A number of observational studies have documented the association between prolonged bed rest during hospitalization with adverse short‐ and long‐term functional impairments and disability in older patients.[1, 2, 3, 4] However, the body of evidence on the benefits of early mobilization on functional outcomes in both critically ill patients and more stable patients on medical‐surgical floors remains inconclusive.[5, 6, 7, 8, 9] Despite the increased emphasis on mobilizing patients early and often in the inpatient setting, there is surprisingly little information available regarding how typically active adult patients are during their hospital stay. The few published studies that are available are limited by small samples and types of patients who were monitored.[10, 11, 12, 13, 14] Therefore, the purpose of this real‐world study was to describe the level of ambulation in a large sample of hospitalized adult patients using a validated consumer‐grade wireless accelerometer.

METHODS

This was a prospective cohort study of ambulatory patients from 3 medical‐surgical units of a community hospital from March 2014 through July 2014. The study was approved by the Kaiser Permanente Southern California Institutional Review Board. All ambulatory medical and surgical adult patients were eligible for the study except for those with isolation precautions. Patients wore an accelerometer (Tractivity; Kineteks Corp., Vancouver, BC, Canada) on the ankle from soon after admission to the unit until discharge home. The sensors were only removed for bathing and medical procedures, at which time the devices were secured to the patient's bed and reworn upon their return to the room. The nursing staff was trained to use the vendor application to register the sensor to the patient, secure the sensor to the patient's ankle, transfer the sensor data to the vendor server, review the step counts on the web application, and manually key the step count into the electronic medical records (EMRs) as part of routine nursing workflow. The staff otherwise continued with usual patient mobilization practices.

We previously validated the Tractivity device in a field study of 20 hospitalized patients using a research‐grade accelerometer, Stepwatch, as the gold standard (unpublished data). We found that the inter‐Tractivity device reliability was near perfect (intraclass correlation=0.99), and that the Tractivity step counts correlated highly with the nurses' documentation on a paper log of distance walked measured in feet (r=0.76). A small number of steps (<100) were recorded over 24 hours when the device was worn by 2 bed bound patients. The 24‐hour Tractivity step count had acceptable limits of agreement with the Stepwatch (+284 [standard deviation: 314] steps; 95% limits of agreement 911‐343). In addition, for the current study, when we examined the step counts between patients who were classified by the nursing team as being able to walk <50 feet (n=320) compared to patients who were able to walk >50 feet (n=434), we found a significant difference in the median number of steps over a 24‐hour period (854 vs 1697, P<0.0001).

The step count data were exported from the vendor's server, examined for irregularities, and merged with administrative and clinical data for analysis. Data extracted from the EMR system included sociodemographic (age, gender, marital status, and race/ethnicity) and clinical characteristics (LACE score [readmission risk score based on length of stay (L); acuity of the admission (A); comorbidity of the patient (measured with the Charlson comorbidity index score) (C); and emergency department use (measured as the number of visits in the six months before admission) (E),[15] Charlson Comorbidity Index, length of stay, principal discharge diagnosis, and body mass index), and nursing documentation of functional status (bed bound, sit up in bed, stand next to bed, walk <50 feet, and walk >50 feet).

Descriptive statistics and nonparametric tests (Kruskal‐Wallis and Wilcoxon signed rank) were used to analyze the non‐normally distributed step count data. Quantile regression[16] was used to determine the association between the frequency of the care team's review and documentation of steps, with median total step count adjusting for age, gender, LACE score, and medicine/surgical service line. Whereas linear regression allows one to describe how the mean of a given outcome changes with respect to some set of covariates in circumstances where data are normally distributed, quantile regression allows one to assess how a set of covariates are related to a prespecified quantile (eg, 50% percentile median) of an outcome distribution. This modeling is especially appropriate here, because step count data are not normally distributed. Because step counts can vary with a number of factors, such as age and principal admitting and discharge diagnoses, we stratified our analyses by age (<65 or 65 years) and service lines (medical or surgical) due to the relatively small numbers of patients in each of the diagnostic groupings. Statistical analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC); P values <0.05 were considered statistically significant.

RESULTS

A total of 1667 patients wore the activity sensor during their hospital stay. We included 777 patients in our analysis who had lengths of stay long enough for 24 hours of continuous monitoring, and almost half of these patients had at least 48 hours of monitoring (n=378). The demographic and clinical characteristics of the sample are detailed in Table 1. The sample included mostly medical patients (77%), with a mean age of 6017 years, 57% females, and 55% nonwhites. Nearly all patients (97%) were classified as ambulatory at discharge based on the EMR data. Approximately 44% of the sensors were lost, mostly due to nursing staff forgetting to remove the devices at discharge; device failure was minimal (n=10).

Sample Characteristics of Patients With 24 Hours of Monitoring Discharged to Home (n=777)
Variables Value
  • NOTE: Data are presented as either meanstandard deviation or count (%).Preadmission level of function that was documented closest to admission time was used. The modal current level of function score in last 24 hours prior to discharge was used. LACE is the readmission risk score based on length of stay (L); acuity of the admission (A); comorbidity of the patient (measured with the Charlson comorbidity index score) (C); and emergency department use (measured as the number of visits in the six months before admission) (E). *Other categories include complications of pregnancy/childbirth, hematologic, other musculoskeletal and skin/subcutaneous disorders, injuries and poisoning, mental illness, other ill‐defined conditions.

Sociodemographics
Age
1840 years 111 (15%)
4165 years 325 (42%)
6575 years 187 (24%)
75 years 151 (19%)
Females 444 (57%)
Race/ethnicity
White 349 (45%)
Hispanics 277 (35%)
African American 101 (13%)
Asian/Pacific Islander 37 (5%)
Other 13 (2%)
Marital status
Partnered 435 (56%)
Unpartnered 332 (43%)
Other/unknown 10 (1%)
Clinical characteristics
Medical (principal discharge diagnoses)
Cardiovascular 116 (15%)
Respiratory 84 (11%)
Gastrointestinal 122 (16%)
Genitourinary 31 (4%)
Metabolic/electrolytes 26 (3%)
Septicemia 92 (12%)
Nervous system 21 (3%)
Cancer/malignancies 13 (1%)
Other* 103 (13%)
Surgical
Orthopedic surgery 60 (8%)
Other surgeries 109 (14%)
LACE score 9.33.5
Charlson index
01 665 (85%)
23 98 (13%)
4+ 14 (2%)
Length of stay, d 3.983.80
Body mass index 30.27.5
Functional status
Preadmission level of function
1, bed bound 3 (0.5%)
2, able to sit 6 (1%)
3, stand next to bed 3 (0.5%)
4, walk <50 feet 113 (14%)
5, walk >50 feet 651 (84%)
Missing 1 (0%)
Current level of function
1, bed bound 1 (0%)
2, able to sit 6 (1%)
3, stand next to bed 7 (1%)
4, walk <50 feet 320 (41%)
5, walk >50 feet 434 (56%)
Missing 9 (1%)

Patients accrued a median of 1158 (interquartile range: 6362238) steps over the 24 hours prior to discharge to home (Table 2). Approximately 13 (2%) patients registered zero steps in the last 24 hours; this may have been due to patients truly not accruing any steps, device failure, or the device was registered but never worn by the patient. Patients who were 65 years and older on both the medicine and surgical services accrued fewer steps compared to younger patients (962 vs 1294, P<0.0001). For patients who had at least 48 hours of continuous monitoring (n=378), there was a median increase of 377 steps from the first 24 hours from admission to the unit to the final 24 hours prior to discharge (811 steps to 1188 steps, P<0.0001) (Table 3 and Figure 1). The average length of stay for these patients was 5.74.9 days. Despite the longer length of stay, the level of ambulation at discharge was similar to patients with shorter stays. This is further illustrated in Figure 2 in the spaghetti plots of total steps over 4, 24‐hour monitoring increments. Ignoring the outliers, the plots suggest the following: (1) step counts tended to increase or stay about the same over the course of a hospitalization; and (2) for the medicine service line, step counts in the final 24 hours prior to discharge for patients with longer lengths of stay (72 or 96 hours) did not appear to be substantially different from patients with shorter lengths of stay. The data for the surgical patients are either too sparse or erratic to make any firm conclusions. Patients accrued steps throughout the day with the highest percentage of steps logged at approximately 6 am and 6 pm; these data are based on time stamps from the device, not the time of data transfer or documentation in the EMR (Figure 3).

Total Step Count in the Last 24 Hours Prior to Discharge to Home for Patients With 24 Hours of Monitoring
Service Total Steps Last 24 Hours
Mean SD Median
  • NOTE: Abbreviations: SD, standard deviation.

Medicine
<65 years old (n=321) 1,972 1,995 1,284
65 years old (n=287) 1,367 1,396 968
Surgical
<65 years old (n=118) 2,238 2,082 1,378
65 years old (n=51) 1,485 1,647 890
Total (n=777) 1,757 1,818 1,158
Total Step Count in the First 24 Hours of Admission to the Medical‐Surgical Unit and Last 24 Hours Prior to Discharge to Home for Patients With 48 Hours of Monitoring
Service Total Steps
First 24 Hours Last 24 Hours
Mean SD Median Mean SD Median
  • NOTE: Abbreviations: SD, standard deviation.

Medicine
<65 years old (n=168) 1,427 1,690 953 2,005 2,006 1,287
65 years old (n=127) 1,004 1,098 676 1,260 1,291 904
Surgical
<65 years old (n=53) 1,722 1,696 1060 2,553 2,142 1,882
65 years old (n=30) 1,184 1,470 704 1,829 1,996 1,053
Total (n=378) 1,307 1,515 811 1,817 1,864 1,188
Figure 1
Box plots of total step counts in the first 24 hours of admission to the medical‐surgical unit and last 24 hours prior to discharge to home for patients with ≥48 hours of monitoring by age and service line.
Figure 2
Spaghetti plots of total step counts for each 24‐hour monitoring period by age (<65 and ≥65 years) and service line (medical or surgical). Sample sizes are as follows: 24 hours (black dots, n = 399), 48 hours (red lines, n = 190), 72 hours (green lines, n = 80), 96 hours (blue lines, n = 108).
Figure 3
Distribution of step counts by percentage of accrued steps over 24 hours prior to discharge.

More frequent documentation of step counts in the EMR (proxy for step count data retrieval and review from the vendor web site) by the care team was associated with higher total step counts after adjustments for relevant covariates (P0.001); 3 or more documentations over a 24‐hour period appears to be a minimal frequency to achieving approximately 200 steps more than the median value (Table 4).

Association Between Frequency of Step Count Documentation in the EMR and Total Step Counts in the Last 24 Hours Prior to Discharge to Home for Those With at Least 24 Hours of Observation
Service Frequency of Documentation of Step Counts in EMR Over 24 Hours P Value Trenda Adjusted P Valueb
0 1 2 3 4
  • NOTE: Abbreviations: EMR, electronic medical record; SD, standard deviation.

  • P value for trend (quantile regression for median step counts).

  • Adjusted for age, gender, LACE score (readmission risk score based on length of stay (L); acuity of the admission (A); comorbidity of the patient (measured with the Charlson comorbidity index score) (C); and emergency department use (measured as the number of visits in the six months before admission) (E), and service line (medicine/surgical) where relevant.

Medicine
<65 years old (n=321) MeanSD 1,4051,414 2,4152,037 2,0101,929 1,9811,907 2,7412,876
Median 1,056 1,514 1284 1,196 1,702 0.004 0.003
N (%) 83 (26%) 109 (34%) 71 (22%) 25 (8%) 33 (10%)
65 years old (n=287) MeanSD 1,3481,711 1,1991428 1,290951 1,5291,180 1,8781,214
Median 850 773 999 1,278 1,498 0.07 0.10
N (%) 85 (30%) 82 (28%) 66 (23%) 20 (7%) 34 (12%)
Surgical
<65 years old (n=118) MeanSD 2,0772,001 1,8591,598 2,6182,536 2,3122,031 3,8022,979
Median 1,361 1,250 1,181 1,719 3,149 0.06 0.05
N (%) 42 (35%) 36 (31%) 18 (15%) 14 (12%) 8 (7%)
65 years old (n=51) MeanSD 2,0032,254 1,4781,603 1,1651,246 478 1,219469
Median 1,028 820 672 478 1,426 0.20 0.15
N (%) 13 (26%) 19 (37%) 15 (29%) 1 (2%) 3 (6%)
Total (n=777) MeanSD 1,5441,717 1,7361,799 1,7201,699 1,8831720 2,4152,304
Median 1,012 1,116 1,124 1,314 1,557 <0.001 <0.001
N (%) 223 (29%) 246 (31%) 170 (22%) 60 (8%) 78 (10%)

DISCUSSION

We found that ambulatory medical‐surgical patients accrued a median of 1158 total steps in the 24 hours prior to their discharge home, which translates to walking approximately 500 meters; older patients accrued fewer steps compared to younger patients. In patients with longer length of stay, the level of ambulation at discharge was similar to patients with shorter stays, suggesting there may be an ambulation threshold (1100 steps) that patients achieve regardless of the length of stay before they are discharged home. In addition, patients whose care team reviewed and documented step counts at least 3 times over a 24‐hour period accrued significantly more steps than patients whose care team made fewer documentations.

The median step counts accrued by surgical patients in our study are similar to that found in Cook and colleagues'[14] report of patients after elective cardiac surgery using another popular consumer‐grade accelerometer. The providers in that study also had access to the data via a dashboard, but it was not clear how this information was used. Brown et al.[12] conducted the first study to objectively monitor mobility using 2 accelerometers in 45 older male veterans who had no prior mobility impairment, and found that patients spent 83% of their hospitalization lying in bed. The veterans spent about 3% of the time (43 minutes per day) standing or walking over a mean length of stay of 5 days. In a similar study with 43 older Dutch patients who had an average length of stay of 7 days, Pedersen et al.[10] found that patients spent 71% of their time lying, 21% sitting, and 4% standing or walking. Unfortunately, neither the Brown et al. nor Pedersen et al. studies were able to distinguish between standing and ambulatory activities. In a more recent study of 47 patients on medical‐surgical units at 2 hospitals that relied on time and motion observation methods, the mean duration for ambulation was <2 minutes during an 8‐hour period.[13]

We took advantage of the variability in the nursing documentation of step counts in the EMR to determine if there was a dose‐response relationship between the frequency of nursing documentation in a 24‐hour period and number of steps patients accrued. We hypothesized that if nurses make an effort to retrieve data from the vendor website and manually key in the step counts in the EMR, they are more likely to incorporate this information in their nursing care, share the information with patients and other clinicians, and therefore create a positive feedback loop for greater ambulation. Although our findings suggest a positive association between more frequent documentation and increased step counts, we cannot exclude the possibility that nurses naturally modulate the frequency with which they review and document step counts based on their overall judgment of the patients' mobility status (ie, patients who are more functionally impaired are assumed to accrue fewer steps over a shift, and therefore, nurses are less inclined to retrieve and document the information frequently). Future studies could prospectively examine what the optimal frequency for review and feedback of step counts is during a typical 8‐ or 12‐hour nursing shift for both patients and the nursing care team to promote ambulation.

A major strength of our study is the collection of objective ambulation data on a large inpatient sample by clinical staff as part of routine nursing care. This strength is balanced with several limitations. Due to the temporal pattern associated with ambulation, we were only able to analyze data for patients who had at least 24 hours of continuous monitoring. This could affect the generalizability of our findings, though we believe there is limited pragmatic value in closely tracking ambulation in patients who have such short stays. There was substantial variability in the step counts, reflecting the mix of medical versus surgical patients and their age, with very small samples available for meaningful subgroup analyses other than what we have presented. We were not able to measure other dimensions of mobility such as transfers or sitting in a chair, because the sensor is designed to only measure steps. In addition, we lost a large number of devices, mostly due to staff forgetting to remove the devices from patients' ankles at discharge. Finally, because we did not blind the nurses and patients to the step count data, the preliminary normative step counts that we present in this article may be higher than expected in patients cared for on medical‐surgical units.

In summary, we found that it is possible to measure ambulation objectively and reliably in hospitalized patients, and have provided preliminary normative step counts for a representative but heterogeneous medical‐surgical population. We also found that most patients who were discharged were ambulating at least 1100 steps over the 24 hours prior to leaving the hospital, regardless of their length of stay. This might suggest that step counts could be a useful parameter in determining readiness for hospital discharge. Our data also suggest that more frequent, objective monitoring of step counts by the nursing care team was associated with patients ambulating more. Both of these findings deserve further exploration. Future studies will need to be conducted on larger samples of medical and surgical hospitalized patients to adequately establish more refined step count norms for specific clinical populations, but especially for older patients, because this age group is at a particularly higher risk of poor functional outcomes with hospitalization. Having accurate and reliable information on ambulation is fundamental to any effort to improve ambulation in hospitalized patients. Moreover, knowing the normative range for step counts in the last 24 hours prior to discharge across specific clinical and age subgroups, could assist with discharge planning and provision of appropriate rehabilitative services in the home or community for safe transitions out of the hospital.[17]

Acknowledgements

The authors express their gratitude to the patients and nurses at the Kaiser Permanente Southern California, Ontario Medical Center.

Disclosures: Funded by the Kaiser Permanente Southern California Care Improvement Research Team. Dr. Sallis contributed substantially to the study design, interpretation, and preparation of this article. Ms. Sturm and Chijioke contributed to the interpretation and preparation of this article. Dr. Kanter contributed to study design, interpretation, and preparation of this article. Mr. Huang contributed to the analysis, interpretation, and preparation of this article. Dr. Shen contributed to study design, analysis, interpretation, and preparation of this article. Dr. Nguyen had full access to the data and led the design, analysis, interpretation, and preparation of this article. Dr. Nguyen had full access to the data and will vouch for the integrity of the work as a whole, from inception to published article. The authors have no funding, financial relationships, or conflicts of interest to disclose.

A number of observational studies have documented the association between prolonged bed rest during hospitalization with adverse short‐ and long‐term functional impairments and disability in older patients.[1, 2, 3, 4] However, the body of evidence on the benefits of early mobilization on functional outcomes in both critically ill patients and more stable patients on medical‐surgical floors remains inconclusive.[5, 6, 7, 8, 9] Despite the increased emphasis on mobilizing patients early and often in the inpatient setting, there is surprisingly little information available regarding how typically active adult patients are during their hospital stay. The few published studies that are available are limited by small samples and types of patients who were monitored.[10, 11, 12, 13, 14] Therefore, the purpose of this real‐world study was to describe the level of ambulation in a large sample of hospitalized adult patients using a validated consumer‐grade wireless accelerometer.

METHODS

This was a prospective cohort study of ambulatory patients from 3 medical‐surgical units of a community hospital from March 2014 through July 2014. The study was approved by the Kaiser Permanente Southern California Institutional Review Board. All ambulatory medical and surgical adult patients were eligible for the study except for those with isolation precautions. Patients wore an accelerometer (Tractivity; Kineteks Corp., Vancouver, BC, Canada) on the ankle from soon after admission to the unit until discharge home. The sensors were only removed for bathing and medical procedures, at which time the devices were secured to the patient's bed and reworn upon their return to the room. The nursing staff was trained to use the vendor application to register the sensor to the patient, secure the sensor to the patient's ankle, transfer the sensor data to the vendor server, review the step counts on the web application, and manually key the step count into the electronic medical records (EMRs) as part of routine nursing workflow. The staff otherwise continued with usual patient mobilization practices.

We previously validated the Tractivity device in a field study of 20 hospitalized patients using a research‐grade accelerometer, Stepwatch, as the gold standard (unpublished data). We found that the inter‐Tractivity device reliability was near perfect (intraclass correlation=0.99), and that the Tractivity step counts correlated highly with the nurses' documentation on a paper log of distance walked measured in feet (r=0.76). A small number of steps (<100) were recorded over 24 hours when the device was worn by 2 bed bound patients. The 24‐hour Tractivity step count had acceptable limits of agreement with the Stepwatch (+284 [standard deviation: 314] steps; 95% limits of agreement 911‐343). In addition, for the current study, when we examined the step counts between patients who were classified by the nursing team as being able to walk <50 feet (n=320) compared to patients who were able to walk >50 feet (n=434), we found a significant difference in the median number of steps over a 24‐hour period (854 vs 1697, P<0.0001).

The step count data were exported from the vendor's server, examined for irregularities, and merged with administrative and clinical data for analysis. Data extracted from the EMR system included sociodemographic (age, gender, marital status, and race/ethnicity) and clinical characteristics (LACE score [readmission risk score based on length of stay (L); acuity of the admission (A); comorbidity of the patient (measured with the Charlson comorbidity index score) (C); and emergency department use (measured as the number of visits in the six months before admission) (E),[15] Charlson Comorbidity Index, length of stay, principal discharge diagnosis, and body mass index), and nursing documentation of functional status (bed bound, sit up in bed, stand next to bed, walk <50 feet, and walk >50 feet).

Descriptive statistics and nonparametric tests (Kruskal‐Wallis and Wilcoxon signed rank) were used to analyze the non‐normally distributed step count data. Quantile regression[16] was used to determine the association between the frequency of the care team's review and documentation of steps, with median total step count adjusting for age, gender, LACE score, and medicine/surgical service line. Whereas linear regression allows one to describe how the mean of a given outcome changes with respect to some set of covariates in circumstances where data are normally distributed, quantile regression allows one to assess how a set of covariates are related to a prespecified quantile (eg, 50% percentile median) of an outcome distribution. This modeling is especially appropriate here, because step count data are not normally distributed. Because step counts can vary with a number of factors, such as age and principal admitting and discharge diagnoses, we stratified our analyses by age (<65 or 65 years) and service lines (medical or surgical) due to the relatively small numbers of patients in each of the diagnostic groupings. Statistical analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC); P values <0.05 were considered statistically significant.

RESULTS

A total of 1667 patients wore the activity sensor during their hospital stay. We included 777 patients in our analysis who had lengths of stay long enough for 24 hours of continuous monitoring, and almost half of these patients had at least 48 hours of monitoring (n=378). The demographic and clinical characteristics of the sample are detailed in Table 1. The sample included mostly medical patients (77%), with a mean age of 6017 years, 57% females, and 55% nonwhites. Nearly all patients (97%) were classified as ambulatory at discharge based on the EMR data. Approximately 44% of the sensors were lost, mostly due to nursing staff forgetting to remove the devices at discharge; device failure was minimal (n=10).

Sample Characteristics of Patients With 24 Hours of Monitoring Discharged to Home (n=777)
Variables Value
  • NOTE: Data are presented as either meanstandard deviation or count (%).Preadmission level of function that was documented closest to admission time was used. The modal current level of function score in last 24 hours prior to discharge was used. LACE is the readmission risk score based on length of stay (L); acuity of the admission (A); comorbidity of the patient (measured with the Charlson comorbidity index score) (C); and emergency department use (measured as the number of visits in the six months before admission) (E). *Other categories include complications of pregnancy/childbirth, hematologic, other musculoskeletal and skin/subcutaneous disorders, injuries and poisoning, mental illness, other ill‐defined conditions.

Sociodemographics
Age
1840 years 111 (15%)
4165 years 325 (42%)
6575 years 187 (24%)
75 years 151 (19%)
Females 444 (57%)
Race/ethnicity
White 349 (45%)
Hispanics 277 (35%)
African American 101 (13%)
Asian/Pacific Islander 37 (5%)
Other 13 (2%)
Marital status
Partnered 435 (56%)
Unpartnered 332 (43%)
Other/unknown 10 (1%)
Clinical characteristics
Medical (principal discharge diagnoses)
Cardiovascular 116 (15%)
Respiratory 84 (11%)
Gastrointestinal 122 (16%)
Genitourinary 31 (4%)
Metabolic/electrolytes 26 (3%)
Septicemia 92 (12%)
Nervous system 21 (3%)
Cancer/malignancies 13 (1%)
Other* 103 (13%)
Surgical
Orthopedic surgery 60 (8%)
Other surgeries 109 (14%)
LACE score 9.33.5
Charlson index
01 665 (85%)
23 98 (13%)
4+ 14 (2%)
Length of stay, d 3.983.80
Body mass index 30.27.5
Functional status
Preadmission level of function
1, bed bound 3 (0.5%)
2, able to sit 6 (1%)
3, stand next to bed 3 (0.5%)
4, walk <50 feet 113 (14%)
5, walk >50 feet 651 (84%)
Missing 1 (0%)
Current level of function
1, bed bound 1 (0%)
2, able to sit 6 (1%)
3, stand next to bed 7 (1%)
4, walk <50 feet 320 (41%)
5, walk >50 feet 434 (56%)
Missing 9 (1%)

Patients accrued a median of 1158 (interquartile range: 6362238) steps over the 24 hours prior to discharge to home (Table 2). Approximately 13 (2%) patients registered zero steps in the last 24 hours; this may have been due to patients truly not accruing any steps, device failure, or the device was registered but never worn by the patient. Patients who were 65 years and older on both the medicine and surgical services accrued fewer steps compared to younger patients (962 vs 1294, P<0.0001). For patients who had at least 48 hours of continuous monitoring (n=378), there was a median increase of 377 steps from the first 24 hours from admission to the unit to the final 24 hours prior to discharge (811 steps to 1188 steps, P<0.0001) (Table 3 and Figure 1). The average length of stay for these patients was 5.74.9 days. Despite the longer length of stay, the level of ambulation at discharge was similar to patients with shorter stays. This is further illustrated in Figure 2 in the spaghetti plots of total steps over 4, 24‐hour monitoring increments. Ignoring the outliers, the plots suggest the following: (1) step counts tended to increase or stay about the same over the course of a hospitalization; and (2) for the medicine service line, step counts in the final 24 hours prior to discharge for patients with longer lengths of stay (72 or 96 hours) did not appear to be substantially different from patients with shorter lengths of stay. The data for the surgical patients are either too sparse or erratic to make any firm conclusions. Patients accrued steps throughout the day with the highest percentage of steps logged at approximately 6 am and 6 pm; these data are based on time stamps from the device, not the time of data transfer or documentation in the EMR (Figure 3).

Total Step Count in the Last 24 Hours Prior to Discharge to Home for Patients With 24 Hours of Monitoring
Service Total Steps Last 24 Hours
Mean SD Median
  • NOTE: Abbreviations: SD, standard deviation.

Medicine
<65 years old (n=321) 1,972 1,995 1,284
65 years old (n=287) 1,367 1,396 968
Surgical
<65 years old (n=118) 2,238 2,082 1,378
65 years old (n=51) 1,485 1,647 890
Total (n=777) 1,757 1,818 1,158
Total Step Count in the First 24 Hours of Admission to the Medical‐Surgical Unit and Last 24 Hours Prior to Discharge to Home for Patients With 48 Hours of Monitoring
Service Total Steps
First 24 Hours Last 24 Hours
Mean SD Median Mean SD Median
  • NOTE: Abbreviations: SD, standard deviation.

Medicine
<65 years old (n=168) 1,427 1,690 953 2,005 2,006 1,287
65 years old (n=127) 1,004 1,098 676 1,260 1,291 904
Surgical
<65 years old (n=53) 1,722 1,696 1060 2,553 2,142 1,882
65 years old (n=30) 1,184 1,470 704 1,829 1,996 1,053
Total (n=378) 1,307 1,515 811 1,817 1,864 1,188
Figure 1
Box plots of total step counts in the first 24 hours of admission to the medical‐surgical unit and last 24 hours prior to discharge to home for patients with ≥48 hours of monitoring by age and service line.
Figure 2
Spaghetti plots of total step counts for each 24‐hour monitoring period by age (<65 and ≥65 years) and service line (medical or surgical). Sample sizes are as follows: 24 hours (black dots, n = 399), 48 hours (red lines, n = 190), 72 hours (green lines, n = 80), 96 hours (blue lines, n = 108).
Figure 3
Distribution of step counts by percentage of accrued steps over 24 hours prior to discharge.

More frequent documentation of step counts in the EMR (proxy for step count data retrieval and review from the vendor web site) by the care team was associated with higher total step counts after adjustments for relevant covariates (P0.001); 3 or more documentations over a 24‐hour period appears to be a minimal frequency to achieving approximately 200 steps more than the median value (Table 4).

Association Between Frequency of Step Count Documentation in the EMR and Total Step Counts in the Last 24 Hours Prior to Discharge to Home for Those With at Least 24 Hours of Observation
Service Frequency of Documentation of Step Counts in EMR Over 24 Hours P Value Trenda Adjusted P Valueb
0 1 2 3 4
  • NOTE: Abbreviations: EMR, electronic medical record; SD, standard deviation.

  • P value for trend (quantile regression for median step counts).

  • Adjusted for age, gender, LACE score (readmission risk score based on length of stay (L); acuity of the admission (A); comorbidity of the patient (measured with the Charlson comorbidity index score) (C); and emergency department use (measured as the number of visits in the six months before admission) (E), and service line (medicine/surgical) where relevant.

Medicine
<65 years old (n=321) MeanSD 1,4051,414 2,4152,037 2,0101,929 1,9811,907 2,7412,876
Median 1,056 1,514 1284 1,196 1,702 0.004 0.003
N (%) 83 (26%) 109 (34%) 71 (22%) 25 (8%) 33 (10%)
65 years old (n=287) MeanSD 1,3481,711 1,1991428 1,290951 1,5291,180 1,8781,214
Median 850 773 999 1,278 1,498 0.07 0.10
N (%) 85 (30%) 82 (28%) 66 (23%) 20 (7%) 34 (12%)
Surgical
<65 years old (n=118) MeanSD 2,0772,001 1,8591,598 2,6182,536 2,3122,031 3,8022,979
Median 1,361 1,250 1,181 1,719 3,149 0.06 0.05
N (%) 42 (35%) 36 (31%) 18 (15%) 14 (12%) 8 (7%)
65 years old (n=51) MeanSD 2,0032,254 1,4781,603 1,1651,246 478 1,219469
Median 1,028 820 672 478 1,426 0.20 0.15
N (%) 13 (26%) 19 (37%) 15 (29%) 1 (2%) 3 (6%)
Total (n=777) MeanSD 1,5441,717 1,7361,799 1,7201,699 1,8831720 2,4152,304
Median 1,012 1,116 1,124 1,314 1,557 <0.001 <0.001
N (%) 223 (29%) 246 (31%) 170 (22%) 60 (8%) 78 (10%)

DISCUSSION

We found that ambulatory medical‐surgical patients accrued a median of 1158 total steps in the 24 hours prior to their discharge home, which translates to walking approximately 500 meters; older patients accrued fewer steps compared to younger patients. In patients with longer length of stay, the level of ambulation at discharge was similar to patients with shorter stays, suggesting there may be an ambulation threshold (1100 steps) that patients achieve regardless of the length of stay before they are discharged home. In addition, patients whose care team reviewed and documented step counts at least 3 times over a 24‐hour period accrued significantly more steps than patients whose care team made fewer documentations.

The median step counts accrued by surgical patients in our study are similar to that found in Cook and colleagues'[14] report of patients after elective cardiac surgery using another popular consumer‐grade accelerometer. The providers in that study also had access to the data via a dashboard, but it was not clear how this information was used. Brown et al.[12] conducted the first study to objectively monitor mobility using 2 accelerometers in 45 older male veterans who had no prior mobility impairment, and found that patients spent 83% of their hospitalization lying in bed. The veterans spent about 3% of the time (43 minutes per day) standing or walking over a mean length of stay of 5 days. In a similar study with 43 older Dutch patients who had an average length of stay of 7 days, Pedersen et al.[10] found that patients spent 71% of their time lying, 21% sitting, and 4% standing or walking. Unfortunately, neither the Brown et al. nor Pedersen et al. studies were able to distinguish between standing and ambulatory activities. In a more recent study of 47 patients on medical‐surgical units at 2 hospitals that relied on time and motion observation methods, the mean duration for ambulation was <2 minutes during an 8‐hour period.[13]

We took advantage of the variability in the nursing documentation of step counts in the EMR to determine if there was a dose‐response relationship between the frequency of nursing documentation in a 24‐hour period and number of steps patients accrued. We hypothesized that if nurses make an effort to retrieve data from the vendor website and manually key in the step counts in the EMR, they are more likely to incorporate this information in their nursing care, share the information with patients and other clinicians, and therefore create a positive feedback loop for greater ambulation. Although our findings suggest a positive association between more frequent documentation and increased step counts, we cannot exclude the possibility that nurses naturally modulate the frequency with which they review and document step counts based on their overall judgment of the patients' mobility status (ie, patients who are more functionally impaired are assumed to accrue fewer steps over a shift, and therefore, nurses are less inclined to retrieve and document the information frequently). Future studies could prospectively examine what the optimal frequency for review and feedback of step counts is during a typical 8‐ or 12‐hour nursing shift for both patients and the nursing care team to promote ambulation.

A major strength of our study is the collection of objective ambulation data on a large inpatient sample by clinical staff as part of routine nursing care. This strength is balanced with several limitations. Due to the temporal pattern associated with ambulation, we were only able to analyze data for patients who had at least 24 hours of continuous monitoring. This could affect the generalizability of our findings, though we believe there is limited pragmatic value in closely tracking ambulation in patients who have such short stays. There was substantial variability in the step counts, reflecting the mix of medical versus surgical patients and their age, with very small samples available for meaningful subgroup analyses other than what we have presented. We were not able to measure other dimensions of mobility such as transfers or sitting in a chair, because the sensor is designed to only measure steps. In addition, we lost a large number of devices, mostly due to staff forgetting to remove the devices from patients' ankles at discharge. Finally, because we did not blind the nurses and patients to the step count data, the preliminary normative step counts that we present in this article may be higher than expected in patients cared for on medical‐surgical units.

In summary, we found that it is possible to measure ambulation objectively and reliably in hospitalized patients, and have provided preliminary normative step counts for a representative but heterogeneous medical‐surgical population. We also found that most patients who were discharged were ambulating at least 1100 steps over the 24 hours prior to leaving the hospital, regardless of their length of stay. This might suggest that step counts could be a useful parameter in determining readiness for hospital discharge. Our data also suggest that more frequent, objective monitoring of step counts by the nursing care team was associated with patients ambulating more. Both of these findings deserve further exploration. Future studies will need to be conducted on larger samples of medical and surgical hospitalized patients to adequately establish more refined step count norms for specific clinical populations, but especially for older patients, because this age group is at a particularly higher risk of poor functional outcomes with hospitalization. Having accurate and reliable information on ambulation is fundamental to any effort to improve ambulation in hospitalized patients. Moreover, knowing the normative range for step counts in the last 24 hours prior to discharge across specific clinical and age subgroups, could assist with discharge planning and provision of appropriate rehabilitative services in the home or community for safe transitions out of the hospital.[17]

Acknowledgements

The authors express their gratitude to the patients and nurses at the Kaiser Permanente Southern California, Ontario Medical Center.

Disclosures: Funded by the Kaiser Permanente Southern California Care Improvement Research Team. Dr. Sallis contributed substantially to the study design, interpretation, and preparation of this article. Ms. Sturm and Chijioke contributed to the interpretation and preparation of this article. Dr. Kanter contributed to study design, interpretation, and preparation of this article. Mr. Huang contributed to the analysis, interpretation, and preparation of this article. Dr. Shen contributed to study design, analysis, interpretation, and preparation of this article. Dr. Nguyen had full access to the data and led the design, analysis, interpretation, and preparation of this article. Dr. Nguyen had full access to the data and will vouch for the integrity of the work as a whole, from inception to published article. The authors have no funding, financial relationships, or conflicts of interest to disclose.

References
  1. Brown CJ, Friedkin RJ, Inouye SK. Prevalence and outcomes of low mobility in hospitalized older patients. J Am Geriatr Soc. 2004;52(8):12631270.
  2. Zisberg A, Shadmi E, Sinoff G, Gur‐Yaish N, Srulovici E, Admi H. Low mobility during hospitalization and functional decline in older adults. J Am Geriatr Soc. 2011;59(2):266273.
  3. Hirsch CH, Sommers L, Olsen A, Mullen L, Winograd CH. The natural history of functional morbidity in hospitalized older patients. J Am Geriatr Soc. 1990;38(12):12961303.
  4. Fisher SR, Kuo YF, Graham JE, Ottenbacher KJ, Ostir GV. Early ambulation and length of stay in older adults hospitalized for acute illness. Arch Intern Med. 2010;170(21):19421943.
  5. Adler J, Malone D. Early mobilization in the intensive care unit: a systematic review. Cardiopulm Phys Ther J. 2012;23(1):513.
  6. Kalisch BJ, Lee S, Dabney BW. Outcomes of inpatient mobilization: a literature review. J Clin Nurs. 2014;23(11–12):14861501.
  7. Greening NJ, Williams JE, Hussain SF, et al. An early rehabilitation intervention to enhance recovery during hospital admission for an exacerbation of chronic respiratory disease: randomised controlled trial. BMJ. 2014;349:g4315.
  8. Morton NA, Keating JL, Berlowitz DJ, Jackson B, Lim WK. Additional exercise does not change hospital or patient outcomes in older medical patients: a controlled clinical trial. Aust J Physiother. 2007;53(2):105111.
  9. Morton NA, Keating JL, Jeffs K. Exercise for acutely hospitalised older medical patients. Cochrane Database Syst Rev. 2007;(1):CD005955.
  10. Pedersen MM, Bodilsen AC, Petersen J, et al. Twenty‐four‐hour mobility during acute hospitalization in older medical patients. J Gerontol A Biol Sci Med Sci. 2013;68(3):331337.
  11. Ostir GV, Berges IM, Kuo YF, Goodwin JS, Fisher SR, Guralnik JM. Mobility activity and its value as a prognostic indicator of survival in hospitalized older adults. J Am Geriatr Soc. 2013;61(4):551557.
  12. Brown CJ, Redden DT, Flood KL, Allman RM. The underrecognized epidemic of low mobility during hospitalization of older adults. J Am Geriatr Soc. 2009;57(9):16601665.
  13. Doherty‐King B, Yoon JY, Pecanac K, Brown R, Mahoney J. Frequency and duration of nursing care related to older patient mobility. J Nurs Scholarsh. 2014;46(1):2027.
  14. Cook DJ, Thompson JE, Prinsen SK, Dearani JA, Deschamps C. Functional recovery in the elderly after major surgery: assessment of mobility recovery using wireless technology. Ann Thorac Surg. 2013;96(3):10571061.
  15. Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551557.
  16. Koenker R, Hallock K. Quantile regression: an introduction. J Econ Perspect. 2001;15(4):4356.
  17. Krumholz HM. Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100102.
References
  1. Brown CJ, Friedkin RJ, Inouye SK. Prevalence and outcomes of low mobility in hospitalized older patients. J Am Geriatr Soc. 2004;52(8):12631270.
  2. Zisberg A, Shadmi E, Sinoff G, Gur‐Yaish N, Srulovici E, Admi H. Low mobility during hospitalization and functional decline in older adults. J Am Geriatr Soc. 2011;59(2):266273.
  3. Hirsch CH, Sommers L, Olsen A, Mullen L, Winograd CH. The natural history of functional morbidity in hospitalized older patients. J Am Geriatr Soc. 1990;38(12):12961303.
  4. Fisher SR, Kuo YF, Graham JE, Ottenbacher KJ, Ostir GV. Early ambulation and length of stay in older adults hospitalized for acute illness. Arch Intern Med. 2010;170(21):19421943.
  5. Adler J, Malone D. Early mobilization in the intensive care unit: a systematic review. Cardiopulm Phys Ther J. 2012;23(1):513.
  6. Kalisch BJ, Lee S, Dabney BW. Outcomes of inpatient mobilization: a literature review. J Clin Nurs. 2014;23(11–12):14861501.
  7. Greening NJ, Williams JE, Hussain SF, et al. An early rehabilitation intervention to enhance recovery during hospital admission for an exacerbation of chronic respiratory disease: randomised controlled trial. BMJ. 2014;349:g4315.
  8. Morton NA, Keating JL, Berlowitz DJ, Jackson B, Lim WK. Additional exercise does not change hospital or patient outcomes in older medical patients: a controlled clinical trial. Aust J Physiother. 2007;53(2):105111.
  9. Morton NA, Keating JL, Jeffs K. Exercise for acutely hospitalised older medical patients. Cochrane Database Syst Rev. 2007;(1):CD005955.
  10. Pedersen MM, Bodilsen AC, Petersen J, et al. Twenty‐four‐hour mobility during acute hospitalization in older medical patients. J Gerontol A Biol Sci Med Sci. 2013;68(3):331337.
  11. Ostir GV, Berges IM, Kuo YF, Goodwin JS, Fisher SR, Guralnik JM. Mobility activity and its value as a prognostic indicator of survival in hospitalized older adults. J Am Geriatr Soc. 2013;61(4):551557.
  12. Brown CJ, Redden DT, Flood KL, Allman RM. The underrecognized epidemic of low mobility during hospitalization of older adults. J Am Geriatr Soc. 2009;57(9):16601665.
  13. Doherty‐King B, Yoon JY, Pecanac K, Brown R, Mahoney J. Frequency and duration of nursing care related to older patient mobility. J Nurs Scholarsh. 2014;46(1):2027.
  14. Cook DJ, Thompson JE, Prinsen SK, Dearani JA, Deschamps C. Functional recovery in the elderly after major surgery: assessment of mobility recovery using wireless technology. Ann Thorac Surg. 2013;96(3):10571061.
  15. Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551557.
  16. Koenker R, Hallock K. Quantile regression: an introduction. J Econ Perspect. 2001;15(4):4356.
  17. Krumholz HM. Post‐hospital syndrome—an acquired, transient condition of generalized risk. N Engl J Med. 2013;368(2):100102.
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Stepping toward discharge: Level of ambulation in hospitalized patients
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Stepping toward discharge: Level of ambulation in hospitalized patients
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Address for correspondence and reprint requests: Huong Nguyen, PhD, Department of Research and Evaluation, Kaiser Permanente Southern California, 100 S. Los Robles, Pasadena, CA 91101; Telephone: 626‐564‐3935; Fax: 626‐564‐39648; E‐mail: [email protected]
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Inpatients With Poor Vision

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Insights into inpatients with poor vision: A high value proposition

Vision impairment is an under‐recognized risk factor for adverse events among hospitalized patients.[1, 2, 3] Inpatients with poor vision are at increased risk for falls and delirium[1, 3] and have more difficulty taking medications.[4, 5] They may also be at risk for being unable to read critical health information, including consent forms and discharge instructions, or decreased quality of life such as simply ordering food from menus. However, vision is neither routinely tested nor documented for inpatients. Low‐cost ($8 and up) nonprescription reading glasses, known as readers may be a simple, high‐value intervention to improve inpatients' vision. We aimed to study initial feasibility and efficacy of screening and correcting inpatients' vision.

METHODS

From June 2012 through January 2014, research assistants (RAs) identified eligible (adults [18 years], English speaking) participants daily from electronic medical records as part of an ongoing study of general medicine inpatients measuring quality‐of‐care at the University of Chicago Medicine.[6] RAs tested visual acuity using Snellen pocket charts (participants wore corrective lenses if available). For eligible participants, readers were tested with sequential fitting (+2/+2.25/+2.75/+3.25) until vision was corrected (sufficient vision: at least 20/50 acuity in at least 1 eye).[7] Eligible participants included those with insufficient vision who were not already wearing corrective lenses and had no documented blindness or medically severe vision loss, for whom nonprescription readers would be unlikely to correct vision deficiencies such as cataracts or glaucoma. The study was approved by the University of Chicago Institutional Review Board (IRB #9967).

Of note, although readers are typically used in populations over 40 years of age, readers were fitted for all participants to assess their utility for any hospitalized adult patient. Upon completing the vision screening and readers interventions, participants received instruction on how to access vision care and how to obtain readers (if they corrected vision) after hospital discharge.

Descriptive statistics and tests of comparison, including t tests and [2] tests, were used when appropriate. All analyses were performed using Stata version 12 (StataCorp, College Station, TX).

RESULTS

Over 800 participants' vision was screened (n=853); the majority were female (56%, 480/853), African American (76%, 650/853), with a mean age of 53.4 years (standard deviation 18.7), consistent with our study site's demographics. Over one‐third (36%, 304/853) of participants had insufficient vision. Older (65 years) participants (56%, 136/244) were more likely to have insufficient vision than younger participants (28%, 168/608; P<0.001).

Participants with insufficient vision were wearing their own corrective lenses during the testing (150/304, 49%), did not use corrective lenses (53/304, 17%), or were without available corrective lenses (99/304, 33%) (Figure 1A).

Figure 1
(A) The proportion of patients screened with insufficient vision. (B) The proportion of eligible patients with vision corrected by readers. Note: percentages may not add to 100 due to rounding.

One‐hundred sixteen of 304 participants approached for the readers intervention were eligible (112 reported medical eye disease, 65 were wearing lenses, and 11 refused or were discharged before intervention implementation).

Nonprescription readers corrected the majority of eligible participants' vision (82%, 95/116). Most participants' (81/116, 70%) vision was corrected using the 2 lowest calibration readers (+2/+2.25); another 14 participants' (12%) vision was corrected with higher‐strength lenses (+2.75/+3.25) (Figure 1B)

DISCUSSION

We found that over one‐third of the inpatients we examined have poor vision. Furthermore, among an easily identified subgroup of inpatients with poor vision, low‐cost readers successfully corrected most participants' vision. Although preventive health is not commonly considered an inpatient issue, hospitalists and other clinicians working in the inpatient setting can play an important role in identifying opportunities to provide high‐value care related to patients' vision.

Several important ethical, safety, and cost considerations related to these findings exist. Hospitalized patients commonly sign written informed consent; therefore, due diligence to ensure patients' ability to read and understand the forms is imperative. Further, inpatient delirium is common, particularly among older patients.[8] Existing or new onset delirium occurs in up to 24% to 35% of elderly inpatients.[8] Vision is an important risk factor for multifactorial inpatient delirium, and early vision correction has been shown to improve delirium rates, as part of a multicomponent intervention.[9] Hospital‐related patient costs per delirium episode have been estimated at $16,303 to $64,421.[10] The cost of a multicomponent intervention was $6341 per case of delirium prevented,[9] whereas only 1 potentially critical component, the cost of readers ($8+), would pale in comparison.[1] Vision screening takes approximately 2.25 minutes plus 2 to 6 minutes for the readers' assessment, with little training and high fidelity. Therefore, this easily implemented, potentially cost saving, intervention targeting inpatients with poor vision may improve patient safety and quality of life in the hospital and even after discharge.

Limitations of the study include considerations of generalizability, as participants were from a single, urban, academic medical center. Additionally, long‐term benefits of the readers intervention were not assessed in this study. Finally, RAs provided the assessments; therefore, further work is required to determine costs of efficient large‐scale clinical implementation through nurse‐led programs.

Despite these study limitations, the surprisingly high prevalence of poor vision among inpatients is a call to action for hospitalists. Future work should investigate the impact and cost of vision correction on hospital outcomes such as patient satisfaction, reduced rehospitalizations, and decreased delirium.[11]

Acknowledgements

The authors thank several individuals for their assistance with this project. Andrea Flores, MA, Senior Programmer, helped with programming and data support. Kristin Constantine, BA, Project Manager, helped with developing and implementing the database for this project. Edward Kim, BA, Project Manager, helped with management of the database and data collection. The authors also thank Ainoa Coltri and the Hospitalist Project research assistants for assistance with data collection, Frank Zadravecz, MPH, for assistance with the creation of figures, and Nicole Twu, MS, for assistance with the project. The authors thank other students who helped to collect data for this project, including Allison Louis, Victoria Moreira, and Esther Schoenfeld.

Disclosures: Dr. Press is supported by a career development award from the National Heart Lung and Blood Institute (NIH K23HL118151). A pilot award from The Center on the Demography and Economics of Aging (CoA, National Institute of Aging P30 AG012857) supported this project. Dr. Matthiesen and Ms. Ranadive received support from the Summer Research Program funded by the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795). Dr. Matthiesen also received funding from the Calvin Fentress Fellowship Program. Dr. Hariprasad reports being a consultant or participating on a speaker's bureau for Alcon, Allergan, Regeneron, Genentech, Optos, OD‐OS, Bayer, Clearside Biomedical, and Ocular Therapeutix. Dr. Meltzer received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795), and from the Agency for Healthcare Quality and Research through the Hospital Medicine and Economics Center for Education and Research in Therapeutics (U18 HS016967‐01), and from the National Institute of Aging through a Midcareer Career Development Award (K24 AG031326‐01), from the National Cancer Institute (KM1 CA156717), and from the National Center for Advancing Translational Science (2UL1TR000430‐06). Dr. Arora received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795) and National Institutes on Aging (K23AG033763).

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References
  1. Oliver D, Daly F, Martin FC, McMurdo ME. Risk factors and risk assessment tools for falls in hospital in‐patients: a systematic review. Age Ageing. 2004;33(2):122130.
  2. Press VG, Shapiro MI, Mayo AM, Meltzer DO, Arora VM. More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18(suppl 1):197204.
  3. Inouye SK, Zhang Y, Jones RN, Kiely DK, Yang F, Marcantonio ER. Risk factors for delirium at discharge: development and validation of a predictive model. Arch Intern Med. 2007;167(13):14061413.
  4. Press VG, Arora VM, Shah LM, et al. Misuse of respiratory inhalers in hospitalized patients with asthma or COPD. J Gen Intern Med. 2011;26(6):635642.
  5. Beckman AG, Parker MG, Thorslund M. Can elderly people take their medicine? Patient Educ Couns. 2005;59(2):186191.
  6. Meltzer D, Manning WG, Morrison J, et al. Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists. Ann Intern Med. 2002;137(11):866874.
  7. Kaiser PK. Prospective evaluation of visual acuity assessment: a comparison of Snellen versus ETDRS charts in clinical practice (An AOS Thesis). Trans Am Ophthalmol Soc. 2009;107:311324.
  8. Levkoff SE, Evans DA, Liptzin B, et al. Delirium. The occurrence and persistence of symptoms among elderly hospitalized patients. Arch Intern Med. 1992;152(2):334340.
  9. Inouye SK, Bogardus ST, Charpentier PA, et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340(9):669676.
  10. Leslie DL, Marcantonio ER, Zhang Y, Leo‐Summers L, Inouye SK. One‐year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008;168(1):2732.
  11. Whitson HE, Whitaker D, Potter G, et al. A low‐vision rehabilitation program for patients with mild cognitive deficits. JAMA Ophthalmol. 2013;131(7):912919.
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Vision impairment is an under‐recognized risk factor for adverse events among hospitalized patients.[1, 2, 3] Inpatients with poor vision are at increased risk for falls and delirium[1, 3] and have more difficulty taking medications.[4, 5] They may also be at risk for being unable to read critical health information, including consent forms and discharge instructions, or decreased quality of life such as simply ordering food from menus. However, vision is neither routinely tested nor documented for inpatients. Low‐cost ($8 and up) nonprescription reading glasses, known as readers may be a simple, high‐value intervention to improve inpatients' vision. We aimed to study initial feasibility and efficacy of screening and correcting inpatients' vision.

METHODS

From June 2012 through January 2014, research assistants (RAs) identified eligible (adults [18 years], English speaking) participants daily from electronic medical records as part of an ongoing study of general medicine inpatients measuring quality‐of‐care at the University of Chicago Medicine.[6] RAs tested visual acuity using Snellen pocket charts (participants wore corrective lenses if available). For eligible participants, readers were tested with sequential fitting (+2/+2.25/+2.75/+3.25) until vision was corrected (sufficient vision: at least 20/50 acuity in at least 1 eye).[7] Eligible participants included those with insufficient vision who were not already wearing corrective lenses and had no documented blindness or medically severe vision loss, for whom nonprescription readers would be unlikely to correct vision deficiencies such as cataracts or glaucoma. The study was approved by the University of Chicago Institutional Review Board (IRB #9967).

Of note, although readers are typically used in populations over 40 years of age, readers were fitted for all participants to assess their utility for any hospitalized adult patient. Upon completing the vision screening and readers interventions, participants received instruction on how to access vision care and how to obtain readers (if they corrected vision) after hospital discharge.

Descriptive statistics and tests of comparison, including t tests and [2] tests, were used when appropriate. All analyses were performed using Stata version 12 (StataCorp, College Station, TX).

RESULTS

Over 800 participants' vision was screened (n=853); the majority were female (56%, 480/853), African American (76%, 650/853), with a mean age of 53.4 years (standard deviation 18.7), consistent with our study site's demographics. Over one‐third (36%, 304/853) of participants had insufficient vision. Older (65 years) participants (56%, 136/244) were more likely to have insufficient vision than younger participants (28%, 168/608; P<0.001).

Participants with insufficient vision were wearing their own corrective lenses during the testing (150/304, 49%), did not use corrective lenses (53/304, 17%), or were without available corrective lenses (99/304, 33%) (Figure 1A).

Figure 1
(A) The proportion of patients screened with insufficient vision. (B) The proportion of eligible patients with vision corrected by readers. Note: percentages may not add to 100 due to rounding.

One‐hundred sixteen of 304 participants approached for the readers intervention were eligible (112 reported medical eye disease, 65 were wearing lenses, and 11 refused or were discharged before intervention implementation).

Nonprescription readers corrected the majority of eligible participants' vision (82%, 95/116). Most participants' (81/116, 70%) vision was corrected using the 2 lowest calibration readers (+2/+2.25); another 14 participants' (12%) vision was corrected with higher‐strength lenses (+2.75/+3.25) (Figure 1B)

DISCUSSION

We found that over one‐third of the inpatients we examined have poor vision. Furthermore, among an easily identified subgroup of inpatients with poor vision, low‐cost readers successfully corrected most participants' vision. Although preventive health is not commonly considered an inpatient issue, hospitalists and other clinicians working in the inpatient setting can play an important role in identifying opportunities to provide high‐value care related to patients' vision.

Several important ethical, safety, and cost considerations related to these findings exist. Hospitalized patients commonly sign written informed consent; therefore, due diligence to ensure patients' ability to read and understand the forms is imperative. Further, inpatient delirium is common, particularly among older patients.[8] Existing or new onset delirium occurs in up to 24% to 35% of elderly inpatients.[8] Vision is an important risk factor for multifactorial inpatient delirium, and early vision correction has been shown to improve delirium rates, as part of a multicomponent intervention.[9] Hospital‐related patient costs per delirium episode have been estimated at $16,303 to $64,421.[10] The cost of a multicomponent intervention was $6341 per case of delirium prevented,[9] whereas only 1 potentially critical component, the cost of readers ($8+), would pale in comparison.[1] Vision screening takes approximately 2.25 minutes plus 2 to 6 minutes for the readers' assessment, with little training and high fidelity. Therefore, this easily implemented, potentially cost saving, intervention targeting inpatients with poor vision may improve patient safety and quality of life in the hospital and even after discharge.

Limitations of the study include considerations of generalizability, as participants were from a single, urban, academic medical center. Additionally, long‐term benefits of the readers intervention were not assessed in this study. Finally, RAs provided the assessments; therefore, further work is required to determine costs of efficient large‐scale clinical implementation through nurse‐led programs.

Despite these study limitations, the surprisingly high prevalence of poor vision among inpatients is a call to action for hospitalists. Future work should investigate the impact and cost of vision correction on hospital outcomes such as patient satisfaction, reduced rehospitalizations, and decreased delirium.[11]

Acknowledgements

The authors thank several individuals for their assistance with this project. Andrea Flores, MA, Senior Programmer, helped with programming and data support. Kristin Constantine, BA, Project Manager, helped with developing and implementing the database for this project. Edward Kim, BA, Project Manager, helped with management of the database and data collection. The authors also thank Ainoa Coltri and the Hospitalist Project research assistants for assistance with data collection, Frank Zadravecz, MPH, for assistance with the creation of figures, and Nicole Twu, MS, for assistance with the project. The authors thank other students who helped to collect data for this project, including Allison Louis, Victoria Moreira, and Esther Schoenfeld.

Disclosures: Dr. Press is supported by a career development award from the National Heart Lung and Blood Institute (NIH K23HL118151). A pilot award from The Center on the Demography and Economics of Aging (CoA, National Institute of Aging P30 AG012857) supported this project. Dr. Matthiesen and Ms. Ranadive received support from the Summer Research Program funded by the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795). Dr. Matthiesen also received funding from the Calvin Fentress Fellowship Program. Dr. Hariprasad reports being a consultant or participating on a speaker's bureau for Alcon, Allergan, Regeneron, Genentech, Optos, OD‐OS, Bayer, Clearside Biomedical, and Ocular Therapeutix. Dr. Meltzer received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795), and from the Agency for Healthcare Quality and Research through the Hospital Medicine and Economics Center for Education and Research in Therapeutics (U18 HS016967‐01), and from the National Institute of Aging through a Midcareer Career Development Award (K24 AG031326‐01), from the National Cancer Institute (KM1 CA156717), and from the National Center for Advancing Translational Science (2UL1TR000430‐06). Dr. Arora received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795) and National Institutes on Aging (K23AG033763).

Vision impairment is an under‐recognized risk factor for adverse events among hospitalized patients.[1, 2, 3] Inpatients with poor vision are at increased risk for falls and delirium[1, 3] and have more difficulty taking medications.[4, 5] They may also be at risk for being unable to read critical health information, including consent forms and discharge instructions, or decreased quality of life such as simply ordering food from menus. However, vision is neither routinely tested nor documented for inpatients. Low‐cost ($8 and up) nonprescription reading glasses, known as readers may be a simple, high‐value intervention to improve inpatients' vision. We aimed to study initial feasibility and efficacy of screening and correcting inpatients' vision.

METHODS

From June 2012 through January 2014, research assistants (RAs) identified eligible (adults [18 years], English speaking) participants daily from electronic medical records as part of an ongoing study of general medicine inpatients measuring quality‐of‐care at the University of Chicago Medicine.[6] RAs tested visual acuity using Snellen pocket charts (participants wore corrective lenses if available). For eligible participants, readers were tested with sequential fitting (+2/+2.25/+2.75/+3.25) until vision was corrected (sufficient vision: at least 20/50 acuity in at least 1 eye).[7] Eligible participants included those with insufficient vision who were not already wearing corrective lenses and had no documented blindness or medically severe vision loss, for whom nonprescription readers would be unlikely to correct vision deficiencies such as cataracts or glaucoma. The study was approved by the University of Chicago Institutional Review Board (IRB #9967).

Of note, although readers are typically used in populations over 40 years of age, readers were fitted for all participants to assess their utility for any hospitalized adult patient. Upon completing the vision screening and readers interventions, participants received instruction on how to access vision care and how to obtain readers (if they corrected vision) after hospital discharge.

Descriptive statistics and tests of comparison, including t tests and [2] tests, were used when appropriate. All analyses were performed using Stata version 12 (StataCorp, College Station, TX).

RESULTS

Over 800 participants' vision was screened (n=853); the majority were female (56%, 480/853), African American (76%, 650/853), with a mean age of 53.4 years (standard deviation 18.7), consistent with our study site's demographics. Over one‐third (36%, 304/853) of participants had insufficient vision. Older (65 years) participants (56%, 136/244) were more likely to have insufficient vision than younger participants (28%, 168/608; P<0.001).

Participants with insufficient vision were wearing their own corrective lenses during the testing (150/304, 49%), did not use corrective lenses (53/304, 17%), or were without available corrective lenses (99/304, 33%) (Figure 1A).

Figure 1
(A) The proportion of patients screened with insufficient vision. (B) The proportion of eligible patients with vision corrected by readers. Note: percentages may not add to 100 due to rounding.

One‐hundred sixteen of 304 participants approached for the readers intervention were eligible (112 reported medical eye disease, 65 were wearing lenses, and 11 refused or were discharged before intervention implementation).

Nonprescription readers corrected the majority of eligible participants' vision (82%, 95/116). Most participants' (81/116, 70%) vision was corrected using the 2 lowest calibration readers (+2/+2.25); another 14 participants' (12%) vision was corrected with higher‐strength lenses (+2.75/+3.25) (Figure 1B)

DISCUSSION

We found that over one‐third of the inpatients we examined have poor vision. Furthermore, among an easily identified subgroup of inpatients with poor vision, low‐cost readers successfully corrected most participants' vision. Although preventive health is not commonly considered an inpatient issue, hospitalists and other clinicians working in the inpatient setting can play an important role in identifying opportunities to provide high‐value care related to patients' vision.

Several important ethical, safety, and cost considerations related to these findings exist. Hospitalized patients commonly sign written informed consent; therefore, due diligence to ensure patients' ability to read and understand the forms is imperative. Further, inpatient delirium is common, particularly among older patients.[8] Existing or new onset delirium occurs in up to 24% to 35% of elderly inpatients.[8] Vision is an important risk factor for multifactorial inpatient delirium, and early vision correction has been shown to improve delirium rates, as part of a multicomponent intervention.[9] Hospital‐related patient costs per delirium episode have been estimated at $16,303 to $64,421.[10] The cost of a multicomponent intervention was $6341 per case of delirium prevented,[9] whereas only 1 potentially critical component, the cost of readers ($8+), would pale in comparison.[1] Vision screening takes approximately 2.25 minutes plus 2 to 6 minutes for the readers' assessment, with little training and high fidelity. Therefore, this easily implemented, potentially cost saving, intervention targeting inpatients with poor vision may improve patient safety and quality of life in the hospital and even after discharge.

Limitations of the study include considerations of generalizability, as participants were from a single, urban, academic medical center. Additionally, long‐term benefits of the readers intervention were not assessed in this study. Finally, RAs provided the assessments; therefore, further work is required to determine costs of efficient large‐scale clinical implementation through nurse‐led programs.

Despite these study limitations, the surprisingly high prevalence of poor vision among inpatients is a call to action for hospitalists. Future work should investigate the impact and cost of vision correction on hospital outcomes such as patient satisfaction, reduced rehospitalizations, and decreased delirium.[11]

Acknowledgements

The authors thank several individuals for their assistance with this project. Andrea Flores, MA, Senior Programmer, helped with programming and data support. Kristin Constantine, BA, Project Manager, helped with developing and implementing the database for this project. Edward Kim, BA, Project Manager, helped with management of the database and data collection. The authors also thank Ainoa Coltri and the Hospitalist Project research assistants for assistance with data collection, Frank Zadravecz, MPH, for assistance with the creation of figures, and Nicole Twu, MS, for assistance with the project. The authors thank other students who helped to collect data for this project, including Allison Louis, Victoria Moreira, and Esther Schoenfeld.

Disclosures: Dr. Press is supported by a career development award from the National Heart Lung and Blood Institute (NIH K23HL118151). A pilot award from The Center on the Demography and Economics of Aging (CoA, National Institute of Aging P30 AG012857) supported this project. Dr. Matthiesen and Ms. Ranadive received support from the Summer Research Program funded by the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795). Dr. Matthiesen also received funding from the Calvin Fentress Fellowship Program. Dr. Hariprasad reports being a consultant or participating on a speaker's bureau for Alcon, Allergan, Regeneron, Genentech, Optos, OD‐OS, Bayer, Clearside Biomedical, and Ocular Therapeutix. Dr. Meltzer received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795), and from the Agency for Healthcare Quality and Research through the Hospital Medicine and Economics Center for Education and Research in Therapeutics (U18 HS016967‐01), and from the National Institute of Aging through a Midcareer Career Development Award (K24 AG031326‐01), from the National Cancer Institute (KM1 CA156717), and from the National Center for Advancing Translational Science (2UL1TR000430‐06). Dr. Arora received funding from the National Institutes on Aging Short‐Term Aging‐Related Research Program (T35AG029795) and National Institutes on Aging (K23AG033763).

References
  1. Oliver D, Daly F, Martin FC, McMurdo ME. Risk factors and risk assessment tools for falls in hospital in‐patients: a systematic review. Age Ageing. 2004;33(2):122130.
  2. Press VG, Shapiro MI, Mayo AM, Meltzer DO, Arora VM. More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18(suppl 1):197204.
  3. Inouye SK, Zhang Y, Jones RN, Kiely DK, Yang F, Marcantonio ER. Risk factors for delirium at discharge: development and validation of a predictive model. Arch Intern Med. 2007;167(13):14061413.
  4. Press VG, Arora VM, Shah LM, et al. Misuse of respiratory inhalers in hospitalized patients with asthma or COPD. J Gen Intern Med. 2011;26(6):635642.
  5. Beckman AG, Parker MG, Thorslund M. Can elderly people take their medicine? Patient Educ Couns. 2005;59(2):186191.
  6. Meltzer D, Manning WG, Morrison J, et al. Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists. Ann Intern Med. 2002;137(11):866874.
  7. Kaiser PK. Prospective evaluation of visual acuity assessment: a comparison of Snellen versus ETDRS charts in clinical practice (An AOS Thesis). Trans Am Ophthalmol Soc. 2009;107:311324.
  8. Levkoff SE, Evans DA, Liptzin B, et al. Delirium. The occurrence and persistence of symptoms among elderly hospitalized patients. Arch Intern Med. 1992;152(2):334340.
  9. Inouye SK, Bogardus ST, Charpentier PA, et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340(9):669676.
  10. Leslie DL, Marcantonio ER, Zhang Y, Leo‐Summers L, Inouye SK. One‐year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008;168(1):2732.
  11. Whitson HE, Whitaker D, Potter G, et al. A low‐vision rehabilitation program for patients with mild cognitive deficits. JAMA Ophthalmol. 2013;131(7):912919.
References
  1. Oliver D, Daly F, Martin FC, McMurdo ME. Risk factors and risk assessment tools for falls in hospital in‐patients: a systematic review. Age Ageing. 2004;33(2):122130.
  2. Press VG, Shapiro MI, Mayo AM, Meltzer DO, Arora VM. More than meets the eye: relationship between low health literacy and poor vision in hospitalized patients. J Health Commun. 2013;18(suppl 1):197204.
  3. Inouye SK, Zhang Y, Jones RN, Kiely DK, Yang F, Marcantonio ER. Risk factors for delirium at discharge: development and validation of a predictive model. Arch Intern Med. 2007;167(13):14061413.
  4. Press VG, Arora VM, Shah LM, et al. Misuse of respiratory inhalers in hospitalized patients with asthma or COPD. J Gen Intern Med. 2011;26(6):635642.
  5. Beckman AG, Parker MG, Thorslund M. Can elderly people take their medicine? Patient Educ Couns. 2005;59(2):186191.
  6. Meltzer D, Manning WG, Morrison J, et al. Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists. Ann Intern Med. 2002;137(11):866874.
  7. Kaiser PK. Prospective evaluation of visual acuity assessment: a comparison of Snellen versus ETDRS charts in clinical practice (An AOS Thesis). Trans Am Ophthalmol Soc. 2009;107:311324.
  8. Levkoff SE, Evans DA, Liptzin B, et al. Delirium. The occurrence and persistence of symptoms among elderly hospitalized patients. Arch Intern Med. 1992;152(2):334340.
  9. Inouye SK, Bogardus ST, Charpentier PA, et al. A multicomponent intervention to prevent delirium in hospitalized older patients. N Engl J Med. 1999;340(9):669676.
  10. Leslie DL, Marcantonio ER, Zhang Y, Leo‐Summers L, Inouye SK. One‐year health care costs associated with delirium in the elderly population. Arch Intern Med. 2008;168(1):2732.
  11. Whitson HE, Whitaker D, Potter G, et al. A low‐vision rehabilitation program for patients with mild cognitive deficits. JAMA Ophthalmol. 2013;131(7):912919.
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Address for correspondence and reprint requests: Valerie G. Press, MD, 5841 S. Maryland Avenue, MC 5000, Chicago, IL 60637; Telephone: 773‐702‐5170; Fax: 773‐795‐7398; E‐mail: [email protected]
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NICE recommends apixaban for VTE

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NICE recommends apixaban for VTE

Thrombus

Image by Andre E.X. Brown

The UK’s National Institute for Health and Care Excellence (NICE) has issued a draft guidance recommending the anticoagulant apixaban (Eliquis) as an option for treating and preventing venous thromboembolism (VTE) in adults.

A NICE committee concluded that apixaban is clinically and cost-effective for this indication.

The draft guidance is now with consultees, who can appeal against it. Once NICE issues its final guidance on a technology, it replaces local recommendations.

“Apixaban, like the other newer oral anticoagulants already recommended by NICE for the treatment and secondary prevention of VTE, does not require frequent blood tests to monitor treatment and so represents a potential benefit for many people who have had a VTE,” said Carole Longson, NICE Health Technology Evaluation Centre Director.

“The committee also heard that apixaban is the only oral anticoagulant for which the licensed dose is lower for secondary prevention than for initial treatment of VTE. This could also be of potential benefit in terms of reducing the risk of bleeding where treatment is continued and therefore increase the chance that a person would take apixaban long-term.”

Clinical effectiveness

The NICE committee assessed the clinical effectiveness of apixaban based on results of the AMPLIFY and AMPLIFY-EXT studies.

Results of the AMPLIFY study indicated that apixaban is noninferior to standard treatment for recurrent VTE—initial parenteral enoxaparin overlapped with warfarin. Apixaban was comparable in efficacy to standard therapy and induced significantly less bleeding.

In AMPLIFY-EXT, researchers compared 12 months of treatment with apixaban at 2 doses—2.5 mg and 5 mg—to placebo in patients who had previously received anticoagulant therapy for 6 to 12 months to treat a prior VTE.

Both doses of apixaban effectively prevented VTE, VTE-related events, and death. And the incidence of bleeding events was low in all treatment arms.

The NICE committee noted that there were limited data in these trials pertaining to patients who needed less than 6 months of treatment and for patients still at high risk of recurrent VTE after 6 months of treatment.

However, the committee concluded that, despite these limitations, the AMPLIFY trials were the pivotal trials that informed the marketing authorization for apixaban. As such, they were sufficient to inform a recommendation for the whole population covered by the marketing authorization.

The committee did point out that there were no head-to-head trials evaluating the relative effectiveness of apixaban compared with rivaroxaban and dabigatran etexilate for treating and preventing VTE.

In addition, there were insufficient data to assess the effectiveness and safety of apixaban in patients with active cancer who had VTE, so it was not possible to make a specific recommendation for this group.

Cost-effectiveness

The recommended dose of apixaban as VTE treatment is 10 mg twice a day for the first 7 days, followed by 5 mg twice a day for at least 3 months. To prevent recurrent VTE, patients who have completed 6 months of VTE treatment should take apixaban at 2.5 mg twice a day.

The cost of apixaban is £1.10 per tablet for either the 2.5 mg or 5 mg dose (excluding tax). The daily cost of apixaban is £2.20. (Costs may vary in different settings because of negotiated procurement discounts.)

Analyses suggested that the incremental cost-effectiveness ratio of apixaban was less than £20,000 per quality-adjusted life-year gained for either 6 months or life-long treatment. Therefore, NICE concluded that apixaban is a cost-effective use of National Health Service resources. 

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Thrombus

Image by Andre E.X. Brown

The UK’s National Institute for Health and Care Excellence (NICE) has issued a draft guidance recommending the anticoagulant apixaban (Eliquis) as an option for treating and preventing venous thromboembolism (VTE) in adults.

A NICE committee concluded that apixaban is clinically and cost-effective for this indication.

The draft guidance is now with consultees, who can appeal against it. Once NICE issues its final guidance on a technology, it replaces local recommendations.

“Apixaban, like the other newer oral anticoagulants already recommended by NICE for the treatment and secondary prevention of VTE, does not require frequent blood tests to monitor treatment and so represents a potential benefit for many people who have had a VTE,” said Carole Longson, NICE Health Technology Evaluation Centre Director.

“The committee also heard that apixaban is the only oral anticoagulant for which the licensed dose is lower for secondary prevention than for initial treatment of VTE. This could also be of potential benefit in terms of reducing the risk of bleeding where treatment is continued and therefore increase the chance that a person would take apixaban long-term.”

Clinical effectiveness

The NICE committee assessed the clinical effectiveness of apixaban based on results of the AMPLIFY and AMPLIFY-EXT studies.

Results of the AMPLIFY study indicated that apixaban is noninferior to standard treatment for recurrent VTE—initial parenteral enoxaparin overlapped with warfarin. Apixaban was comparable in efficacy to standard therapy and induced significantly less bleeding.

In AMPLIFY-EXT, researchers compared 12 months of treatment with apixaban at 2 doses—2.5 mg and 5 mg—to placebo in patients who had previously received anticoagulant therapy for 6 to 12 months to treat a prior VTE.

Both doses of apixaban effectively prevented VTE, VTE-related events, and death. And the incidence of bleeding events was low in all treatment arms.

The NICE committee noted that there were limited data in these trials pertaining to patients who needed less than 6 months of treatment and for patients still at high risk of recurrent VTE after 6 months of treatment.

However, the committee concluded that, despite these limitations, the AMPLIFY trials were the pivotal trials that informed the marketing authorization for apixaban. As such, they were sufficient to inform a recommendation for the whole population covered by the marketing authorization.

The committee did point out that there were no head-to-head trials evaluating the relative effectiveness of apixaban compared with rivaroxaban and dabigatran etexilate for treating and preventing VTE.

In addition, there were insufficient data to assess the effectiveness and safety of apixaban in patients with active cancer who had VTE, so it was not possible to make a specific recommendation for this group.

Cost-effectiveness

The recommended dose of apixaban as VTE treatment is 10 mg twice a day for the first 7 days, followed by 5 mg twice a day for at least 3 months. To prevent recurrent VTE, patients who have completed 6 months of VTE treatment should take apixaban at 2.5 mg twice a day.

The cost of apixaban is £1.10 per tablet for either the 2.5 mg or 5 mg dose (excluding tax). The daily cost of apixaban is £2.20. (Costs may vary in different settings because of negotiated procurement discounts.)

Analyses suggested that the incremental cost-effectiveness ratio of apixaban was less than £20,000 per quality-adjusted life-year gained for either 6 months or life-long treatment. Therefore, NICE concluded that apixaban is a cost-effective use of National Health Service resources. 

Thrombus

Image by Andre E.X. Brown

The UK’s National Institute for Health and Care Excellence (NICE) has issued a draft guidance recommending the anticoagulant apixaban (Eliquis) as an option for treating and preventing venous thromboembolism (VTE) in adults.

A NICE committee concluded that apixaban is clinically and cost-effective for this indication.

The draft guidance is now with consultees, who can appeal against it. Once NICE issues its final guidance on a technology, it replaces local recommendations.

“Apixaban, like the other newer oral anticoagulants already recommended by NICE for the treatment and secondary prevention of VTE, does not require frequent blood tests to monitor treatment and so represents a potential benefit for many people who have had a VTE,” said Carole Longson, NICE Health Technology Evaluation Centre Director.

“The committee also heard that apixaban is the only oral anticoagulant for which the licensed dose is lower for secondary prevention than for initial treatment of VTE. This could also be of potential benefit in terms of reducing the risk of bleeding where treatment is continued and therefore increase the chance that a person would take apixaban long-term.”

Clinical effectiveness

The NICE committee assessed the clinical effectiveness of apixaban based on results of the AMPLIFY and AMPLIFY-EXT studies.

Results of the AMPLIFY study indicated that apixaban is noninferior to standard treatment for recurrent VTE—initial parenteral enoxaparin overlapped with warfarin. Apixaban was comparable in efficacy to standard therapy and induced significantly less bleeding.

In AMPLIFY-EXT, researchers compared 12 months of treatment with apixaban at 2 doses—2.5 mg and 5 mg—to placebo in patients who had previously received anticoagulant therapy for 6 to 12 months to treat a prior VTE.

Both doses of apixaban effectively prevented VTE, VTE-related events, and death. And the incidence of bleeding events was low in all treatment arms.

The NICE committee noted that there were limited data in these trials pertaining to patients who needed less than 6 months of treatment and for patients still at high risk of recurrent VTE after 6 months of treatment.

However, the committee concluded that, despite these limitations, the AMPLIFY trials were the pivotal trials that informed the marketing authorization for apixaban. As such, they were sufficient to inform a recommendation for the whole population covered by the marketing authorization.

The committee did point out that there were no head-to-head trials evaluating the relative effectiveness of apixaban compared with rivaroxaban and dabigatran etexilate for treating and preventing VTE.

In addition, there were insufficient data to assess the effectiveness and safety of apixaban in patients with active cancer who had VTE, so it was not possible to make a specific recommendation for this group.

Cost-effectiveness

The recommended dose of apixaban as VTE treatment is 10 mg twice a day for the first 7 days, followed by 5 mg twice a day for at least 3 months. To prevent recurrent VTE, patients who have completed 6 months of VTE treatment should take apixaban at 2.5 mg twice a day.

The cost of apixaban is £1.10 per tablet for either the 2.5 mg or 5 mg dose (excluding tax). The daily cost of apixaban is £2.20. (Costs may vary in different settings because of negotiated procurement discounts.)

Analyses suggested that the incremental cost-effectiveness ratio of apixaban was less than £20,000 per quality-adjusted life-year gained for either 6 months or life-long treatment. Therefore, NICE concluded that apixaban is a cost-effective use of National Health Service resources. 

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