MONTREAL – A randomized trial designed to definitively test the efficacy of mechanical thrombectomy for treating acute ischemic strokes caused by basilar artery occlusion fell victim to slow recruitment and crossovers that muddied the intention-to-treat results, but the per-protocol and as-treated analyses both showed that thrombectomy was superior to best medical therapy in a multicenter, randomized study with 131 Chinese patients.

Mitchel L. Zoler/MDedge News

“Our findings should be considered in the context of the best evidence currently available, and progressive loss of equipoise for endovascular therapy for severe, large-vessel occlusion strokes,” Raul G. Nogueira, MD, said at the World Stroke Congress. “This was not a perfect trial, but it’s the best data we have, by far, at least for now” on the value of mechanical thrombectomy for treating acute ischemic stroke caused by a basilar artery occlusion, added Dr. Nogueira, professor of neurology and director of the neuroendovascular service at Emory University, Atlanta.

In the study’s per-protocol analysis, which considered patients who received their randomized treatment, the study’s primary endpoint of a modified Rankin Scale (mRS) score of 0-3 at 90 days after treatment was 44% in 63 patients who underwent thrombectomy and 26% in 51 patients randomized to best medical therapy who remained on that regimen, a statistically significant difference, Dr. Nogueira reported. In the as-treated analysis, which considered all enrolled patients based on the treatment they actually received regardless of randomization group, 77 patients treated with thrombectomy had a 47% rate of achieving the primary outcome, compared with 24% of 54 controls, also a statistically significant difference.

In contrast, the prespecified primary analysis for the study, the intention-to-treat analysis that considered patients based on their randomization assignment regardless of the treatment they actually received, showed that after 90 days the rate of patients with a mRS score of 0-3 was 42% in 66 thrombectomy patients and 32% among 65 controls, a difference that was not significant; this is a finding that, from a purist’s standpoint, makes the trial’s result neutral. The per-protocol and as-treated analyses were also prespecified steps in the study’s design, but not primary endpoints.

Despite the shortcoming for the primary analysis, Dr. Nogueira said that he found the per-protocol and as-treated findings very persuasive. “I personally could not randomize these patients” in the future to not receive mechanical thrombectomy, he confessed from the podium.

The BEST trial randomized 131 patients at any of 28 Chinese sites between April 2015 and September 2017. Patients had to enter within 8 hours of stroke onset. The original trial design called for enrolling 344 patients, but the steering committee decided in 2017 to prematurely stop the study because of a progressive drop in enrollment of patients, and “excessive” crossovers from the control arm to thrombectomy, a total of 14 patients. During the final month of the trial, 6 of 10 patients assigned by randomization to receive best medical care instead underwent thrombectomy. “At that point, we pretty much had to stop,” Dr. Nogueira said. Enrolled patients averaged about 65 years old, about 90% had a basilar artery occlusion and about 10% a vertebral artery occlusion, about 30% received intravenous alteplase, and the median National Institutes of Health Stroke Scale score at entry was about 30.

The major adverse effect from thrombectomy seen in the study was symptomatic intracranial hemorrhage, which occurred in 5 of the 77 patients (6%) actually treated with thrombectomy, compared with none of the 54 patients not treated with thrombectomy. This modest rate of intracranial hemorrhages was “not unexpected,” Dr. Nogueira noted.

Acute ischemic strokes caused by a basilar artery occlusion are relatively uncommon, accounting for about 1% of all acute ischemic strokes and 5%-10% of acute ischemic strokes caused by occlusion of a proximal intracranial artery. But when these strokes occur, they are a “neurological catastrophe,” Dr. Nogueira said, causing severe disability or mortality in about 70% of patients.

BEST had no commercial funding. Dr. Nogueira reported no disclosures.

[email protected]

SOURCE: Nogueira RG et al. Int J Stroke. 2018;13(2_suppl):227, Abstract 978.


 

MONTREAL – A randomized trial designed to definitively test the efficacy of mechanical thrombectomy for treating acute ischemic strokes caused by basilar artery occlusion fell victim to slow recruitment and crossovers that muddied the intention-to-treat results, but the per-protocol and as-treated analyses both showed that thrombectomy was superior to best medical therapy in a multicenter, randomized study with 131 Chinese patients.

Mitchel L. Zoler/MDedge News

“Our findings should be considered in the context of the best evidence currently available, and progressive loss of equipoise for endovascular therapy for severe, large-vessel occlusion strokes,” Raul G. Nogueira, MD, said at the World Stroke Congress. “This was not a perfect trial, but it’s the best data we have, by far, at least for now” on the value of mechanical thrombectomy for treating acute ischemic stroke caused by a basilar artery occlusion, added Dr. Nogueira, professor of neurology and director of the neuroendovascular service at Emory University, Atlanta.

In the study’s per-protocol analysis, which considered patients who received their randomized treatment, the study’s primary endpoint of a modified Rankin Scale (mRS) score of 0-3 at 90 days after treatment was 44% in 63 patients who underwent thrombectomy and 26% in 51 patients randomized to best medical therapy who remained on that regimen, a statistically significant difference, Dr. Nogueira reported. In the as-treated analysis, which considered all enrolled patients based on the treatment they actually received regardless of randomization group, 77 patients treated with thrombectomy had a 47% rate of achieving the primary outcome, compared with 24% of 54 controls, also a statistically significant difference.

In contrast, the prespecified primary analysis for the study, the intention-to-treat analysis that considered patients based on their randomization assignment regardless of the treatment they actually received, showed that after 90 days the rate of patients with a mRS score of 0-3 was 42% in 66 thrombectomy patients and 32% among 65 controls, a difference that was not significant; this is a finding that, from a purist’s standpoint, makes the trial’s result neutral. The per-protocol and as-treated analyses were also prespecified steps in the study’s design, but not primary endpoints.

Despite the shortcoming for the primary analysis, Dr. Nogueira said that he found the per-protocol and as-treated findings very persuasive. “I personally could not randomize these patients” in the future to not receive mechanical thrombectomy, he confessed from the podium.

The BEST trial randomized 131 patients at any of 28 Chinese sites between April 2015 and September 2017. Patients had to enter within 8 hours of stroke onset. The original trial design called for enrolling 344 patients, but the steering committee decided in 2017 to prematurely stop the study because of a progressive drop in enrollment of patients, and “excessive” crossovers from the control arm to thrombectomy, a total of 14 patients. During the final month of the trial, 6 of 10 patients assigned by randomization to receive best medical care instead underwent thrombectomy. “At that point, we pretty much had to stop,” Dr. Nogueira said. Enrolled patients averaged about 65 years old, about 90% had a basilar artery occlusion and about 10% a vertebral artery occlusion, about 30% received intravenous alteplase, and the median National Institutes of Health Stroke Scale score at entry was about 30.

The major adverse effect from thrombectomy seen in the study was symptomatic intracranial hemorrhage, which occurred in 5 of the 77 patients (6%) actually treated with thrombectomy, compared with none of the 54 patients not treated with thrombectomy. This modest rate of intracranial hemorrhages was “not unexpected,” Dr. Nogueira noted.

Acute ischemic strokes caused by a basilar artery occlusion are relatively uncommon, accounting for about 1% of all acute ischemic strokes and 5%-10% of acute ischemic strokes caused by occlusion of a proximal intracranial artery. But when these strokes occur, they are a “neurological catastrophe,” Dr. Nogueira said, causing severe disability or mortality in about 70% of patients.

BEST had no commercial funding. Dr. Nogueira reported no disclosures.

[email protected]

SOURCE: Nogueira RG et al. Int J Stroke. 2018;13(2_suppl):227, Abstract 978.


 

MONTREAL – A randomized trial designed to definitively test the efficacy of mechanical thrombectomy for treating acute ischemic strokes caused by basilar artery occlusion fell victim to slow recruitment and crossovers that muddied the intention-to-treat results, but the per-protocol and as-treated analyses both showed that thrombectomy was superior to best medical therapy in a multicenter, randomized study with 131 Chinese patients.

Mitchel L. Zoler/MDedge News

“Our findings should be considered in the context of the best evidence currently available, and progressive loss of equipoise for endovascular therapy for severe, large-vessel occlusion strokes,” Raul G. Nogueira, MD, said at the World Stroke Congress. “This was not a perfect trial, but it’s the best data we have, by far, at least for now” on the value of mechanical thrombectomy for treating acute ischemic stroke caused by a basilar artery occlusion, added Dr. Nogueira, professor of neurology and director of the neuroendovascular service at Emory University, Atlanta.

In the study’s per-protocol analysis, which considered patients who received their randomized treatment, the study’s primary endpoint of a modified Rankin Scale (mRS) score of 0-3 at 90 days after treatment was 44% in 63 patients who underwent thrombectomy and 26% in 51 patients randomized to best medical therapy who remained on that regimen, a statistically significant difference, Dr. Nogueira reported. In the as-treated analysis, which considered all enrolled patients based on the treatment they actually received regardless of randomization group, 77 patients treated with thrombectomy had a 47% rate of achieving the primary outcome, compared with 24% of 54 controls, also a statistically significant difference.

In contrast, the prespecified primary analysis for the study, the intention-to-treat analysis that considered patients based on their randomization assignment regardless of the treatment they actually received, showed that after 90 days the rate of patients with a mRS score of 0-3 was 42% in 66 thrombectomy patients and 32% among 65 controls, a difference that was not significant; this is a finding that, from a purist’s standpoint, makes the trial’s result neutral. The per-protocol and as-treated analyses were also prespecified steps in the study’s design, but not primary endpoints.

Despite the shortcoming for the primary analysis, Dr. Nogueira said that he found the per-protocol and as-treated findings very persuasive. “I personally could not randomize these patients” in the future to not receive mechanical thrombectomy, he confessed from the podium.

The BEST trial randomized 131 patients at any of 28 Chinese sites between April 2015 and September 2017. Patients had to enter within 8 hours of stroke onset. The original trial design called for enrolling 344 patients, but the steering committee decided in 2017 to prematurely stop the study because of a progressive drop in enrollment of patients, and “excessive” crossovers from the control arm to thrombectomy, a total of 14 patients. During the final month of the trial, 6 of 10 patients assigned by randomization to receive best medical care instead underwent thrombectomy. “At that point, we pretty much had to stop,” Dr. Nogueira said. Enrolled patients averaged about 65 years old, about 90% had a basilar artery occlusion and about 10% a vertebral artery occlusion, about 30% received intravenous alteplase, and the median National Institutes of Health Stroke Scale score at entry was about 30.

The major adverse effect from thrombectomy seen in the study was symptomatic intracranial hemorrhage, which occurred in 5 of the 77 patients (6%) actually treated with thrombectomy, compared with none of the 54 patients not treated with thrombectomy. This modest rate of intracranial hemorrhages was “not unexpected,” Dr. Nogueira noted.

Acute ischemic strokes caused by a basilar artery occlusion are relatively uncommon, accounting for about 1% of all acute ischemic strokes and 5%-10% of acute ischemic strokes caused by occlusion of a proximal intracranial artery. But when these strokes occur, they are a “neurological catastrophe,” Dr. Nogueira said, causing severe disability or mortality in about 70% of patients.

BEST had no commercial funding. Dr. Nogueira reported no disclosures.

[email protected]

SOURCE: Nogueira RG et al. Int J Stroke. 2018;13(2_suppl):227, Abstract 978.


 

CHICAGO – A recent history of GI bleeding or malignancy may not be a valid contraindication to thrombolytic therapy in patients with an acute ischemic stroke, based on a review of outcomes from more than 40,000 U.S. stroke patients.

The analysis showed that, among 40,396 U.S. patients who had an acute ischemic stroke during 2009-2015 and received timely treatment with alteplase, “we did not find statistically significant increased rates of in-hospital mortality or bleeding” in the small number of patients who received alteplase (Activase) despite a recent GI bleed or diagnosed GI malignancy, Taku Inohara, MD, said at the American Heart Association scientific sessions. The 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke deemed thrombolytic therapy with alteplase in these types of patients contraindicated, based on consensus expert opinion (Stroke. 2018 March;49[3]:e66-e110).

“Further study is needed to evaluate the safety of recombinant tissue–type plasminogen activator [alteplase] in this specific population,” suggested Dr. Inohara, a cardiologist and research fellow at Duke University, Durham, N.C.

His analysis used data collected by the Get With the Guidelines–Stroke program, a voluntary quality promotion and improvement program that during 2009-2015 included records for more than 633,000 U.S. stroke patients that could be linked with records kept by the Centers for Medicare & Medicaid Services. From this database, 40,396 patients (6%) treated with alteplase within 4.5 hours of stroke onset were identified. The alteplase-treated patients included 93 with a diagnosis code during the prior year for a GI malignancy and 43 with a diagnostic code within the prior 21 days for a GI bleed.


Dr. Inohara and his associates determined patients’ mortality during their stroke hospitalization, as well as several measures of functional recovery at hospital discharge and thrombolysis-related complications. For each of these endpoints, the rate among patients with a GI malignancy, a GI bleed, or the rate among a combined group of both patients showed no statistically significant differences, compared with the more than 40,000 other patients without a GI complication after adjustment for several demographic and clinical between-group differences. However, Dr. Inohara cautioned that residual or unmeasured confounding may exist that distorts these findings. The rate of in-hospital mortality, the prespecified primary endpoint for the analysis, was 10% among patients with either type of GI complication and 9% in those without. The rate of serious thrombolysis-related complications was 7% in the patients with GI disease and 9% in those without.

In a separate analysis of the complete database of more than 633,000 patients, Dr. Inohara and his associates found 148 patients who had either a GI bleed or malignancy and otherwise qualified for thrombolytic therapy but did not receive this treatment. This meant that overall, in this large U.S. experience, 136 of 284 (48%) acute ischemic stroke patients who qualified for thrombolysis but had a GI complication nonetheless received thrombolysis. Further analysis showed that the patients not treated with thrombolysis had at admission an average National Institutes of Health Stroke Scale score of 11, compared with an average score of 14 among patients who received thrombolysis.

This apparent selection for thrombolytic treatment of patients with more severe strokes “may have overestimated risk in the patients with GI disease,” Dr. Inohara said.

Dr. Inohara reported receiving research funding from Boston Scientific.

[email protected]

SOURCE: Inohara T et al. Circulation. 2018 Nov 6;138[suppl 1], Abstract 12291.

CHICAGO – A recent history of GI bleeding or malignancy may not be a valid contraindication to thrombolytic therapy in patients with an acute ischemic stroke, based on a review of outcomes from more than 40,000 U.S. stroke patients.

The analysis showed that, among 40,396 U.S. patients who had an acute ischemic stroke during 2009-2015 and received timely treatment with alteplase, “we did not find statistically significant increased rates of in-hospital mortality or bleeding” in the small number of patients who received alteplase (Activase) despite a recent GI bleed or diagnosed GI malignancy, Taku Inohara, MD, said at the American Heart Association scientific sessions. The 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke deemed thrombolytic therapy with alteplase in these types of patients contraindicated, based on consensus expert opinion (Stroke. 2018 March;49[3]:e66-e110).

“Further study is needed to evaluate the safety of recombinant tissue–type plasminogen activator [alteplase] in this specific population,” suggested Dr. Inohara, a cardiologist and research fellow at Duke University, Durham, N.C.

His analysis used data collected by the Get With the Guidelines–Stroke program, a voluntary quality promotion and improvement program that during 2009-2015 included records for more than 633,000 U.S. stroke patients that could be linked with records kept by the Centers for Medicare & Medicaid Services. From this database, 40,396 patients (6%) treated with alteplase within 4.5 hours of stroke onset were identified. The alteplase-treated patients included 93 with a diagnosis code during the prior year for a GI malignancy and 43 with a diagnostic code within the prior 21 days for a GI bleed.


Dr. Inohara and his associates determined patients’ mortality during their stroke hospitalization, as well as several measures of functional recovery at hospital discharge and thrombolysis-related complications. For each of these endpoints, the rate among patients with a GI malignancy, a GI bleed, or the rate among a combined group of both patients showed no statistically significant differences, compared with the more than 40,000 other patients without a GI complication after adjustment for several demographic and clinical between-group differences. However, Dr. Inohara cautioned that residual or unmeasured confounding may exist that distorts these findings. The rate of in-hospital mortality, the prespecified primary endpoint for the analysis, was 10% among patients with either type of GI complication and 9% in those without. The rate of serious thrombolysis-related complications was 7% in the patients with GI disease and 9% in those without.

In a separate analysis of the complete database of more than 633,000 patients, Dr. Inohara and his associates found 148 patients who had either a GI bleed or malignancy and otherwise qualified for thrombolytic therapy but did not receive this treatment. This meant that overall, in this large U.S. experience, 136 of 284 (48%) acute ischemic stroke patients who qualified for thrombolysis but had a GI complication nonetheless received thrombolysis. Further analysis showed that the patients not treated with thrombolysis had at admission an average National Institutes of Health Stroke Scale score of 11, compared with an average score of 14 among patients who received thrombolysis.

This apparent selection for thrombolytic treatment of patients with more severe strokes “may have overestimated risk in the patients with GI disease,” Dr. Inohara said.

Dr. Inohara reported receiving research funding from Boston Scientific.

[email protected]

SOURCE: Inohara T et al. Circulation. 2018 Nov 6;138[suppl 1], Abstract 12291.

CHICAGO – A recent history of GI bleeding or malignancy may not be a valid contraindication to thrombolytic therapy in patients with an acute ischemic stroke, based on a review of outcomes from more than 40,000 U.S. stroke patients.

The analysis showed that, among 40,396 U.S. patients who had an acute ischemic stroke during 2009-2015 and received timely treatment with alteplase, “we did not find statistically significant increased rates of in-hospital mortality or bleeding” in the small number of patients who received alteplase (Activase) despite a recent GI bleed or diagnosed GI malignancy, Taku Inohara, MD, said at the American Heart Association scientific sessions. The 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke deemed thrombolytic therapy with alteplase in these types of patients contraindicated, based on consensus expert opinion (Stroke. 2018 March;49[3]:e66-e110).

“Further study is needed to evaluate the safety of recombinant tissue–type plasminogen activator [alteplase] in this specific population,” suggested Dr. Inohara, a cardiologist and research fellow at Duke University, Durham, N.C.

His analysis used data collected by the Get With the Guidelines–Stroke program, a voluntary quality promotion and improvement program that during 2009-2015 included records for more than 633,000 U.S. stroke patients that could be linked with records kept by the Centers for Medicare & Medicaid Services. From this database, 40,396 patients (6%) treated with alteplase within 4.5 hours of stroke onset were identified. The alteplase-treated patients included 93 with a diagnosis code during the prior year for a GI malignancy and 43 with a diagnostic code within the prior 21 days for a GI bleed.


Dr. Inohara and his associates determined patients’ mortality during their stroke hospitalization, as well as several measures of functional recovery at hospital discharge and thrombolysis-related complications. For each of these endpoints, the rate among patients with a GI malignancy, a GI bleed, or the rate among a combined group of both patients showed no statistically significant differences, compared with the more than 40,000 other patients without a GI complication after adjustment for several demographic and clinical between-group differences. However, Dr. Inohara cautioned that residual or unmeasured confounding may exist that distorts these findings. The rate of in-hospital mortality, the prespecified primary endpoint for the analysis, was 10% among patients with either type of GI complication and 9% in those without. The rate of serious thrombolysis-related complications was 7% in the patients with GI disease and 9% in those without.

In a separate analysis of the complete database of more than 633,000 patients, Dr. Inohara and his associates found 148 patients who had either a GI bleed or malignancy and otherwise qualified for thrombolytic therapy but did not receive this treatment. This meant that overall, in this large U.S. experience, 136 of 284 (48%) acute ischemic stroke patients who qualified for thrombolysis but had a GI complication nonetheless received thrombolysis. Further analysis showed that the patients not treated with thrombolysis had at admission an average National Institutes of Health Stroke Scale score of 11, compared with an average score of 14 among patients who received thrombolysis.

This apparent selection for thrombolytic treatment of patients with more severe strokes “may have overestimated risk in the patients with GI disease,” Dr. Inohara said.

Dr. Inohara reported receiving research funding from Boston Scientific.

[email protected]

SOURCE: Inohara T et al. Circulation. 2018 Nov 6;138[suppl 1], Abstract 12291.

Carol Bernstein, MD, joins Nick Andrews to talk about the difference between physician burnout and depression. Dr. Bernstein is a professor at NYU Langone in New York City and has been a guest on the MDedge Psychcast.
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Carol Bernstein, MD, joins Nick Andrews to talk about the difference between physician burnout and depression. Dr. Bernstein is a professor at NYU Langone in New York City and has been a guest on the MDedge Psychcast.
Apple Podcasts
Google Podcasts


 

Carol Bernstein, MD, joins Nick Andrews to talk about the difference between physician burnout and depression. Dr. Bernstein is a professor at NYU Langone in New York City and has been a guest on the MDedge Psychcast.
Apple Podcasts
Google Podcasts


 

A new proposal aims to move clinicians away from the keyboard and back to face time with patients. Also today, diuretics are linked to diabetic amputations in type 2 diabetes, pausing direct acting oral anticoagulants show favorable outcomes for atrial fibrillation, and while therapy has matured for patients with HCV, there are still issues with access.

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Apple Podcasts

Google Podcasts

Spotify
 

A new proposal aims to move clinicians away from the keyboard and back to face time with patients. Also today, diuretics are linked to diabetic amputations in type 2 diabetes, pausing direct acting oral anticoagulants show favorable outcomes for atrial fibrillation, and while therapy has matured for patients with HCV, there are still issues with access.

Amazon Alexa

Apple Podcasts

Google Podcasts

Spotify
 

A new proposal aims to move clinicians away from the keyboard and back to face time with patients. Also today, diuretics are linked to diabetic amputations in type 2 diabetes, pausing direct acting oral anticoagulants show favorable outcomes for atrial fibrillation, and while therapy has matured for patients with HCV, there are still issues with access.

Amazon Alexa

Apple Podcasts

Google Podcasts

Spotify
 

Photo by Jen Smith

SAN DIEGO—An algorithm has proven effective for identifying differentiation syndrome (DS) in patients taking ivosidenib or enasidenib, according to a speaker at the 2018 ASH Annual Meeting.

The U.S. Food and Drug Administration (FDA) recently announced that DS is going unnoticed in some patients with acute myeloid leukemia (AML) who are taking the IDH2 inhibitor enasidenib (Idhifa) or the IDH1 inhibitor ivosidenib (Tibsovo).

Though both drug labels include boxed warnings detailing the risk of DS, the FDA found evidence to suggest that DS is underdiagnosed, which can result in fatalities.

The FDA performed a systematic analysis of DS in AML patients taking either drug to determine if an algorithm could uncover a higher incidence of DS than was previously reported.

Kelly J. Norsworthy, MD, of the FDA, described the results of this analysis at ASH as abstract 288.

The analysis included patients with relapsed/refractory AML treated on a phase 1 study of ivosidenib (NCT02074839, AG120-C-001) and a phase 1/2 study of enasidenib (NCT01915498, AG221-C-001).

There were 179 patients treated with the approved dose of ivosidenib and 214 treated with the approved dose of enasidenib.

The researchers searched for DS events in the first 90 days of therapy. Patients were categorized as having DS if they had at least one investigator-reported DS event (IDH DS or retinoic acid syndrome) or if they had at least two signs or symptoms of DS, according to revised Montesinos criteria, within 7 days.

The signs/symptoms included:

• Dyspnea
• Unexplained fever
• Weight gain
• Unexplained hypotension
• Acute renal failure
• Pulmonary infiltrates or pleuropericardial effusion
• Multiple organ dysfunction.

“We added an event for multiple organ dysfunction since this adverse event could satisfy multiple Montesinos criteria,” Dr. Norsworthy said.

“Although leukocytosis is not a diagnostic criterion for DS, it is frequently seen in association with DS, so we performed an additional query for concomitant leukocytosis,” she added.

The researchers looked for adverse events of leukocytosis, hyperleukocytosis, white blood cell count increase, and leukocyte count greater than 10 Gi/L within 7 days of clinical signs/symptoms.

DS incidence

The algorithm suggested 40% of patients in each treatment group had potential DS—72 of 179 patients treated with ivosidenib and 86 of 214 patients treated with enasidenib.

“We reviewed case narratives and laboratory data from the algorithmically defined cases of DS to adjudicate whether cases were DS or unlikely DS due to an alternative explanation, most commonly due to a clinical course inconsistent with DS or confirmed infection,” Dr. Norsworthy said.

The reviewer-adjudicated incidence of DS was 19% in both groups—34 patients on ivosidenib and 41 patients on enasidenib.

“This contrasts with the DS incidence of 11% to 14% reported by investigators,” Dr. Norsworthy said. “Thus, there was a subset of patients where the syndrome was not recognized by investigators.”

Characteristics of DS

The median time to DS onset in this analysis was 20 days (range, 1 to 78) in the ivosidenib group and 19 days in the enasidenib group (range, 1 to 86).

In both treatment groups, most patients had moderate DS—71% (n=24) in the ivosidenib group and 80% (n=33) in the enasidenib group. Moderate DS was defined as meeting two to three of the aforementioned criteria for DS.

Fewer patients had severe DS (four or more criteria)—24% (n=8) in the ivosidenib group and 12% (n=5) in the enasidenib group.

For the remaining patients, DS severity could not be determined—6% (n=2) in the ivosidenib group and 10% (n=4) in the enasidenib group. These were investigator-reported cases of DS.

Most DS cases in the ivosidenib and enasidenib groups—68% (n=23) and 66% (n=27), respectively— included grade 3 or higher adverse reactions.

Two patients in each group died of DS—6% and 5%, respectively. Only one of these cases was recognized as DS and treated with steroids, Dr. Norsworthy noted.

She also pointed out that most patients with DS had leukocytosis—79% (n=27) in the ivosidenib group and 61% (n=25) in the enasidenib group.

In addition, rates of complete response (CR) and CR with incomplete hematologic recovery (CRh) were numerically lower among patients with DS, although the confidence intervals (CI) overlap.

Among patients on ivosidenib, the CR/CRh rate was 18% (95% CI, 7-35) in those with DS and 36% (95% CI, 28-45) in those without DS.

Among patients on enasidenib, the CR/CRh rate was 18% (95% CI, 7-33) in those with DS and 25% (95% CI, 18-32) in those without DS.

“[F]irm conclusions regarding the impact on response cannot be inferred based on this post-hoc subgroup analysis,” Dr. Norsworthy stressed.

Predicting DS

Dr. Norsworthy noted that baseline patient and disease characteristics were similar between patients with and without DS.

The researchers did see a trend toward higher blasts in the marrow and peripheral blood as well as higher white blood cell counts at baseline among patients with DS.

“However, there did not appear to be a distinct baseline white blood cell count or absolute blast cell count cutoff above which DS was more common,” Dr. Norsworthy said.

She added that the patient numbers are small, so it’s not possible to make firm conclusions about prognostic factors for DS.

In closing, Dr. Norsworthy said the algorithmic approach used here “led to the recognition of additional cases of DS not identified by investigators or review committee determination for patients treated with the IDH inhibitors ivo and ena.”

“Increased recognition of the signs and symptoms of DS through the framework of the Montesinos criteria may lead to early diagnosis and treatment, which may decrease severe complications and mortality. Furthermore, integration of the algorithm into clinical trials of differentiating therapies, in a prospective fashion, may help to systematically monitor the incidence and severity of DS.”

Dr. Norsworthy declared no conflicts of interest.

Photo by Jen Smith

SAN DIEGO—An algorithm has proven effective for identifying differentiation syndrome (DS) in patients taking ivosidenib or enasidenib, according to a speaker at the 2018 ASH Annual Meeting.

The U.S. Food and Drug Administration (FDA) recently announced that DS is going unnoticed in some patients with acute myeloid leukemia (AML) who are taking the IDH2 inhibitor enasidenib (Idhifa) or the IDH1 inhibitor ivosidenib (Tibsovo).

Though both drug labels include boxed warnings detailing the risk of DS, the FDA found evidence to suggest that DS is underdiagnosed, which can result in fatalities.

The FDA performed a systematic analysis of DS in AML patients taking either drug to determine if an algorithm could uncover a higher incidence of DS than was previously reported.

Kelly J. Norsworthy, MD, of the FDA, described the results of this analysis at ASH as abstract 288.

The analysis included patients with relapsed/refractory AML treated on a phase 1 study of ivosidenib (NCT02074839, AG120-C-001) and a phase 1/2 study of enasidenib (NCT01915498, AG221-C-001).

There were 179 patients treated with the approved dose of ivosidenib and 214 treated with the approved dose of enasidenib.

The researchers searched for DS events in the first 90 days of therapy. Patients were categorized as having DS if they had at least one investigator-reported DS event (IDH DS or retinoic acid syndrome) or if they had at least two signs or symptoms of DS, according to revised Montesinos criteria, within 7 days.

The signs/symptoms included:

• Dyspnea
• Unexplained fever
• Weight gain
• Unexplained hypotension
• Acute renal failure
• Pulmonary infiltrates or pleuropericardial effusion
• Multiple organ dysfunction.

“We added an event for multiple organ dysfunction since this adverse event could satisfy multiple Montesinos criteria,” Dr. Norsworthy said.

“Although leukocytosis is not a diagnostic criterion for DS, it is frequently seen in association with DS, so we performed an additional query for concomitant leukocytosis,” she added.

The researchers looked for adverse events of leukocytosis, hyperleukocytosis, white blood cell count increase, and leukocyte count greater than 10 Gi/L within 7 days of clinical signs/symptoms.

DS incidence

The algorithm suggested 40% of patients in each treatment group had potential DS—72 of 179 patients treated with ivosidenib and 86 of 214 patients treated with enasidenib.

“We reviewed case narratives and laboratory data from the algorithmically defined cases of DS to adjudicate whether cases were DS or unlikely DS due to an alternative explanation, most commonly due to a clinical course inconsistent with DS or confirmed infection,” Dr. Norsworthy said.

The reviewer-adjudicated incidence of DS was 19% in both groups—34 patients on ivosidenib and 41 patients on enasidenib.

“This contrasts with the DS incidence of 11% to 14% reported by investigators,” Dr. Norsworthy said. “Thus, there was a subset of patients where the syndrome was not recognized by investigators.”

Characteristics of DS

The median time to DS onset in this analysis was 20 days (range, 1 to 78) in the ivosidenib group and 19 days in the enasidenib group (range, 1 to 86).

In both treatment groups, most patients had moderate DS—71% (n=24) in the ivosidenib group and 80% (n=33) in the enasidenib group. Moderate DS was defined as meeting two to three of the aforementioned criteria for DS.

Fewer patients had severe DS (four or more criteria)—24% (n=8) in the ivosidenib group and 12% (n=5) in the enasidenib group.

For the remaining patients, DS severity could not be determined—6% (n=2) in the ivosidenib group and 10% (n=4) in the enasidenib group. These were investigator-reported cases of DS.

Most DS cases in the ivosidenib and enasidenib groups—68% (n=23) and 66% (n=27), respectively— included grade 3 or higher adverse reactions.

Two patients in each group died of DS—6% and 5%, respectively. Only one of these cases was recognized as DS and treated with steroids, Dr. Norsworthy noted.

She also pointed out that most patients with DS had leukocytosis—79% (n=27) in the ivosidenib group and 61% (n=25) in the enasidenib group.

In addition, rates of complete response (CR) and CR with incomplete hematologic recovery (CRh) were numerically lower among patients with DS, although the confidence intervals (CI) overlap.

Among patients on ivosidenib, the CR/CRh rate was 18% (95% CI, 7-35) in those with DS and 36% (95% CI, 28-45) in those without DS.

Among patients on enasidenib, the CR/CRh rate was 18% (95% CI, 7-33) in those with DS and 25% (95% CI, 18-32) in those without DS.

“[F]irm conclusions regarding the impact on response cannot be inferred based on this post-hoc subgroup analysis,” Dr. Norsworthy stressed.

Predicting DS

Dr. Norsworthy noted that baseline patient and disease characteristics were similar between patients with and without DS.

The researchers did see a trend toward higher blasts in the marrow and peripheral blood as well as higher white blood cell counts at baseline among patients with DS.

“However, there did not appear to be a distinct baseline white blood cell count or absolute blast cell count cutoff above which DS was more common,” Dr. Norsworthy said.

She added that the patient numbers are small, so it’s not possible to make firm conclusions about prognostic factors for DS.

In closing, Dr. Norsworthy said the algorithmic approach used here “led to the recognition of additional cases of DS not identified by investigators or review committee determination for patients treated with the IDH inhibitors ivo and ena.”

“Increased recognition of the signs and symptoms of DS through the framework of the Montesinos criteria may lead to early diagnosis and treatment, which may decrease severe complications and mortality. Furthermore, integration of the algorithm into clinical trials of differentiating therapies, in a prospective fashion, may help to systematically monitor the incidence and severity of DS.”

Dr. Norsworthy declared no conflicts of interest.

Photo by Jen Smith

SAN DIEGO—An algorithm has proven effective for identifying differentiation syndrome (DS) in patients taking ivosidenib or enasidenib, according to a speaker at the 2018 ASH Annual Meeting.

The U.S. Food and Drug Administration (FDA) recently announced that DS is going unnoticed in some patients with acute myeloid leukemia (AML) who are taking the IDH2 inhibitor enasidenib (Idhifa) or the IDH1 inhibitor ivosidenib (Tibsovo).

Though both drug labels include boxed warnings detailing the risk of DS, the FDA found evidence to suggest that DS is underdiagnosed, which can result in fatalities.

The FDA performed a systematic analysis of DS in AML patients taking either drug to determine if an algorithm could uncover a higher incidence of DS than was previously reported.

Kelly J. Norsworthy, MD, of the FDA, described the results of this analysis at ASH as abstract 288.

The analysis included patients with relapsed/refractory AML treated on a phase 1 study of ivosidenib (NCT02074839, AG120-C-001) and a phase 1/2 study of enasidenib (NCT01915498, AG221-C-001).

There were 179 patients treated with the approved dose of ivosidenib and 214 treated with the approved dose of enasidenib.

The researchers searched for DS events in the first 90 days of therapy. Patients were categorized as having DS if they had at least one investigator-reported DS event (IDH DS or retinoic acid syndrome) or if they had at least two signs or symptoms of DS, according to revised Montesinos criteria, within 7 days.

The signs/symptoms included:

• Dyspnea
• Unexplained fever
• Weight gain
• Unexplained hypotension
• Acute renal failure
• Pulmonary infiltrates or pleuropericardial effusion
• Multiple organ dysfunction.

“We added an event for multiple organ dysfunction since this adverse event could satisfy multiple Montesinos criteria,” Dr. Norsworthy said.

“Although leukocytosis is not a diagnostic criterion for DS, it is frequently seen in association with DS, so we performed an additional query for concomitant leukocytosis,” she added.

The researchers looked for adverse events of leukocytosis, hyperleukocytosis, white blood cell count increase, and leukocyte count greater than 10 Gi/L within 7 days of clinical signs/symptoms.

DS incidence

The algorithm suggested 40% of patients in each treatment group had potential DS—72 of 179 patients treated with ivosidenib and 86 of 214 patients treated with enasidenib.

“We reviewed case narratives and laboratory data from the algorithmically defined cases of DS to adjudicate whether cases were DS or unlikely DS due to an alternative explanation, most commonly due to a clinical course inconsistent with DS or confirmed infection,” Dr. Norsworthy said.

The reviewer-adjudicated incidence of DS was 19% in both groups—34 patients on ivosidenib and 41 patients on enasidenib.

“This contrasts with the DS incidence of 11% to 14% reported by investigators,” Dr. Norsworthy said. “Thus, there was a subset of patients where the syndrome was not recognized by investigators.”

Characteristics of DS

The median time to DS onset in this analysis was 20 days (range, 1 to 78) in the ivosidenib group and 19 days in the enasidenib group (range, 1 to 86).

In both treatment groups, most patients had moderate DS—71% (n=24) in the ivosidenib group and 80% (n=33) in the enasidenib group. Moderate DS was defined as meeting two to three of the aforementioned criteria for DS.

Fewer patients had severe DS (four or more criteria)—24% (n=8) in the ivosidenib group and 12% (n=5) in the enasidenib group.

For the remaining patients, DS severity could not be determined—6% (n=2) in the ivosidenib group and 10% (n=4) in the enasidenib group. These were investigator-reported cases of DS.

Most DS cases in the ivosidenib and enasidenib groups—68% (n=23) and 66% (n=27), respectively— included grade 3 or higher adverse reactions.

Two patients in each group died of DS—6% and 5%, respectively. Only one of these cases was recognized as DS and treated with steroids, Dr. Norsworthy noted.

She also pointed out that most patients with DS had leukocytosis—79% (n=27) in the ivosidenib group and 61% (n=25) in the enasidenib group.

In addition, rates of complete response (CR) and CR with incomplete hematologic recovery (CRh) were numerically lower among patients with DS, although the confidence intervals (CI) overlap.

Among patients on ivosidenib, the CR/CRh rate was 18% (95% CI, 7-35) in those with DS and 36% (95% CI, 28-45) in those without DS.

Among patients on enasidenib, the CR/CRh rate was 18% (95% CI, 7-33) in those with DS and 25% (95% CI, 18-32) in those without DS.

“[F]irm conclusions regarding the impact on response cannot be inferred based on this post-hoc subgroup analysis,” Dr. Norsworthy stressed.

Predicting DS

Dr. Norsworthy noted that baseline patient and disease characteristics were similar between patients with and without DS.

The researchers did see a trend toward higher blasts in the marrow and peripheral blood as well as higher white blood cell counts at baseline among patients with DS.

“However, there did not appear to be a distinct baseline white blood cell count or absolute blast cell count cutoff above which DS was more common,” Dr. Norsworthy said.

She added that the patient numbers are small, so it’s not possible to make firm conclusions about prognostic factors for DS.

In closing, Dr. Norsworthy said the algorithmic approach used here “led to the recognition of additional cases of DS not identified by investigators or review committee determination for patients treated with the IDH inhibitors ivo and ena.”

“Increased recognition of the signs and symptoms of DS through the framework of the Montesinos criteria may lead to early diagnosis and treatment, which may decrease severe complications and mortality. Furthermore, integration of the algorithm into clinical trials of differentiating therapies, in a prospective fashion, may help to systematically monitor the incidence and severity of DS.”

Dr. Norsworthy declared no conflicts of interest.

Photo courtesy of ASH

SAN DIEGO—A recurrent mutation in BCL2, the therapeutic target of venetoclax, appears to be a major contributor to drug resistance in patients with chronic lymphocytic leukemia (CLL), investigators reported.

The mutation has been detected in some patients with CLL up to 2 years before resistance to venetoclax actually develops, according to Piers Blombery, MBBS, of the Peter MacCallum Cancer Center in Melbourne, Victoria, Australia.

“We have identified the first acquired BCL2 mutation developed in patients clinically treated with venetoclax,” he said during the late-breaking abstracts session at the 2018 ASH Annual Meeting.

The mutation, which the investigators have labeled BCL2 Gly101Val, “is a recurrent and frequent mediator of resistance and may be detected years before clinical relapse occurs,” Dr. Blombery added.

A paper on the mutation was published in Cancer Discovery to coincide with the presentation at ASH (abstract LBA-7).

Despite the demonstrated efficacy of venetoclax as continuous therapy in patients with relapsed or refractory CLL, the majority of patients experience disease progression, prompting the investigators to explore molecular mechanisms of secondary resistance.

To do this, they analyzed paired samples from 15 patients with CLL, enrolled in clinical trials of venetoclax, collected both before the start of venetoclax therapy and at the time of disease progression.

In seven patients, the investigators identified a novel mutation that showed up at the time of progression but was absent from the pre-venetoclax samples.

The mutation first became detectable from about 19 to 42 months after the start of therapy and preceded clinical progression by as much as 25 months, the investigators found.

They pinned the mutation down to the BH3-binding groove on BCL2, the same molecular site targeted by venetoclax. They found the mutation was not present in samples from 96 patients with venetoclax-naive CLL nor in any other B-cell malignancies.

Searches for references to the mutation in both a cancer database (COSMIC) and a population database (gnomAD) came up empty.

In other experiments, the investigators determined that cell lines overexpressing BCL2 Gly101Val are resistant to venetoclax, and, in the presence of venetoclax in vitro, BCL2 Gly101Val-expressing cells have a growth advantage compared with wild-type cells.

Additionally, they showed that the mutation results in impaired venetoclax binding in vitro.

“BCL2 Gly101Val is observed subclonally, implicating multiple mechanisms of venetoclax resistance in the same patient,” Dr. Blombery said.

He added that the identification of the resistance mutation is a strong rationale for using combination therapy to treat patients with relapsed or refractory CLL to help prevent or attenuate selection pressures that lead to resistance.

Dr. Blombery reported having no relevant disclosures. The investigators were supported by the Wilson Center for Lymphoma Genomics, Snowdome Foundation, National Health Medical Research Council, Leukemia and Lymphoma Society, Leukemia Foundation, Cancer Council of Victoria, and Australian Cancer Research Foundation.

Photo courtesy of ASH

SAN DIEGO—A recurrent mutation in BCL2, the therapeutic target of venetoclax, appears to be a major contributor to drug resistance in patients with chronic lymphocytic leukemia (CLL), investigators reported.

The mutation has been detected in some patients with CLL up to 2 years before resistance to venetoclax actually develops, according to Piers Blombery, MBBS, of the Peter MacCallum Cancer Center in Melbourne, Victoria, Australia.

“We have identified the first acquired BCL2 mutation developed in patients clinically treated with venetoclax,” he said during the late-breaking abstracts session at the 2018 ASH Annual Meeting.

The mutation, which the investigators have labeled BCL2 Gly101Val, “is a recurrent and frequent mediator of resistance and may be detected years before clinical relapse occurs,” Dr. Blombery added.

A paper on the mutation was published in Cancer Discovery to coincide with the presentation at ASH (abstract LBA-7).

Despite the demonstrated efficacy of venetoclax as continuous therapy in patients with relapsed or refractory CLL, the majority of patients experience disease progression, prompting the investigators to explore molecular mechanisms of secondary resistance.

To do this, they analyzed paired samples from 15 patients with CLL, enrolled in clinical trials of venetoclax, collected both before the start of venetoclax therapy and at the time of disease progression.

In seven patients, the investigators identified a novel mutation that showed up at the time of progression but was absent from the pre-venetoclax samples.

The mutation first became detectable from about 19 to 42 months after the start of therapy and preceded clinical progression by as much as 25 months, the investigators found.

They pinned the mutation down to the BH3-binding groove on BCL2, the same molecular site targeted by venetoclax. They found the mutation was not present in samples from 96 patients with venetoclax-naive CLL nor in any other B-cell malignancies.

Searches for references to the mutation in both a cancer database (COSMIC) and a population database (gnomAD) came up empty.

In other experiments, the investigators determined that cell lines overexpressing BCL2 Gly101Val are resistant to venetoclax, and, in the presence of venetoclax in vitro, BCL2 Gly101Val-expressing cells have a growth advantage compared with wild-type cells.

Additionally, they showed that the mutation results in impaired venetoclax binding in vitro.

“BCL2 Gly101Val is observed subclonally, implicating multiple mechanisms of venetoclax resistance in the same patient,” Dr. Blombery said.

He added that the identification of the resistance mutation is a strong rationale for using combination therapy to treat patients with relapsed or refractory CLL to help prevent or attenuate selection pressures that lead to resistance.

Dr. Blombery reported having no relevant disclosures. The investigators were supported by the Wilson Center for Lymphoma Genomics, Snowdome Foundation, National Health Medical Research Council, Leukemia and Lymphoma Society, Leukemia Foundation, Cancer Council of Victoria, and Australian Cancer Research Foundation.

Photo courtesy of ASH

SAN DIEGO—A recurrent mutation in BCL2, the therapeutic target of venetoclax, appears to be a major contributor to drug resistance in patients with chronic lymphocytic leukemia (CLL), investigators reported.

The mutation has been detected in some patients with CLL up to 2 years before resistance to venetoclax actually develops, according to Piers Blombery, MBBS, of the Peter MacCallum Cancer Center in Melbourne, Victoria, Australia.

“We have identified the first acquired BCL2 mutation developed in patients clinically treated with venetoclax,” he said during the late-breaking abstracts session at the 2018 ASH Annual Meeting.

The mutation, which the investigators have labeled BCL2 Gly101Val, “is a recurrent and frequent mediator of resistance and may be detected years before clinical relapse occurs,” Dr. Blombery added.

A paper on the mutation was published in Cancer Discovery to coincide with the presentation at ASH (abstract LBA-7).

Despite the demonstrated efficacy of venetoclax as continuous therapy in patients with relapsed or refractory CLL, the majority of patients experience disease progression, prompting the investigators to explore molecular mechanisms of secondary resistance.

To do this, they analyzed paired samples from 15 patients with CLL, enrolled in clinical trials of venetoclax, collected both before the start of venetoclax therapy and at the time of disease progression.

In seven patients, the investigators identified a novel mutation that showed up at the time of progression but was absent from the pre-venetoclax samples.

The mutation first became detectable from about 19 to 42 months after the start of therapy and preceded clinical progression by as much as 25 months, the investigators found.

They pinned the mutation down to the BH3-binding groove on BCL2, the same molecular site targeted by venetoclax. They found the mutation was not present in samples from 96 patients with venetoclax-naive CLL nor in any other B-cell malignancies.

Searches for references to the mutation in both a cancer database (COSMIC) and a population database (gnomAD) came up empty.

In other experiments, the investigators determined that cell lines overexpressing BCL2 Gly101Val are resistant to venetoclax, and, in the presence of venetoclax in vitro, BCL2 Gly101Val-expressing cells have a growth advantage compared with wild-type cells.

Additionally, they showed that the mutation results in impaired venetoclax binding in vitro.

“BCL2 Gly101Val is observed subclonally, implicating multiple mechanisms of venetoclax resistance in the same patient,” Dr. Blombery said.

He added that the identification of the resistance mutation is a strong rationale for using combination therapy to treat patients with relapsed or refractory CLL to help prevent or attenuate selection pressures that lead to resistance.

Dr. Blombery reported having no relevant disclosures. The investigators were supported by the Wilson Center for Lymphoma Genomics, Snowdome Foundation, National Health Medical Research Council, Leukemia and Lymphoma Society, Leukemia Foundation, Cancer Council of Victoria, and Australian Cancer Research Foundation.

Photo by Jen Smith

SAN DIEGO—Preliminary trial results suggest re-treatment with dasatinib is feasible and safe for a second attempt at tyrosine kinase inhibitor (TKI) discontinuation in chronic myeloid leukemia (CML) patients who fail to achieve treatment-free remission (TFR) after discontinuing imatinib.

However, investigators reported the rate of second TFR (TFR2) was 21% at 6 months, which was not enough to confirm, at this time, that dasatinib could improve the TFR2 rate after imatinib discontinuation failure.

Dennis Kim, MD, of the University of Toronto in Ontario, Canada, presented these results at the 2018 ASH Annual Meeting (abstract 787).

The design of this trial (NCT02268370) includes three phases: the imatinib discontinuation phase, the dasatinib re-challenge phase to achieve a molecular response of ≥ 4.5-log reduction in BCR-ABL1 transcripts (MR4.5), and the dasatinib discontinuation phase.

The primary objective of the trial is to determine the proportion of patients who remain in deep molecular remission (> MR4.5) after discontinuing dasatinib following a failed attempt at discontinuation of imatinib.

If patients had a confirmed molecular relapse after discontinuing imatinib, they were started on 100 mg of dasatinib daily, and, after achieving MR4.5 or greater for 12 months, they discontinued dasatinib for a try at the second TFR.

Investigators defined loss of molecular response, or relapse, as a loss of a major molecular response (MMR) once or loss of MR4.0 on two consecutive occasions.

Patient characteristics

The 131 enrolled CML patients were a median age of 61 (range, 21 to 84), and 62% were male.

Patients had a median 9.36 years of disease duration, 9.18 years of imatinib treatment, 6.82 years of MR4 duration, and 5.08 years of MR4.5 duration.

“The cohort has a very long history of imatinib treatment as well as MR4 duration,” Dr. Kim pointed out, “which also can affect our TFR1 rate, and I think, also, it can affect our TFR2 rate.”

TFR1 and TFR2 rates

As of October 25, the TFR1 rate using loss of MMR as the measure was 69.9% at 12 months from imatinib discontinuation. Relapse-free survival was 57.2% at that time.

Of the 53 patients who lost response, 51 patients received dasatinib. At 3 months of treatment, 97.7% achieved an MMR, 89.9% achieved MR4, and 84.6% achieved MR4.5.

Twenty-five of 51 patients treated with dasatinib attained MR4.5 for 12 months or longer and discontinued treatment for a second attempt at TFR.

Twenty-one patients are still receiving dasatinib and have attained MR4.5, but not for the 12-month duration yet.

Dr. Kim noted that the median time to achievement of molecular response after dasatinib re-challenge ranged from 2.76 months for MR4.5 to 1.71 months for MMR.

Twenty-one of 25 patients (84.0%) who discontinued dasatinib lost their molecular response at a median of 3.7 months.

The estimated TFR2 rate after dasatinib discontinuation is 21.0% to 24.4% at 6 months, which means the investigators cannot reject the null hypothesis of 28% or more patients remaining in remission.

Patients who lost response rapidly after dasatinib discontinuation also tended to lose response rapidly after imatinib discontinuation, Dr. Kim pointed out.

“However, you see some patients who do not lose their response after dasatinib discontinuation or who lose the response but later after the dasatinib discontinuation, they tend to lose their imatinib response also in a later time point,” he said. “So we started to look at the risk factors.”

Risk factor analysis

Out of seven potential risk factors, the investigators were able to demonstrate that time to molecular relapse after imatinib discontinuation, molecular relapse pattern after imatinib discontinuation, and BCR-ABL1 quantitative polymerase chain reaction (qPCR) value prior to dasatinib discontinuation “seemed to be very significant,” Dr. Kim said.

Time to molecular relapse after discontinuation of imatinib correlates with TFR2. The group of patients who relapsed in 3 to 6 months of stopping imatinib had a significantly longer TFR2 than patients who relapsed within 3 months of stopping imatinib (P=0.018).

The molecular relapse pattern also correlates with TFR2. The group with a single loss of MMR after imatinib discontinuation had a significantly shorter TFR2 than those who lost MR4 twice after imatinib discontinuation (P=0.043).

And 0% of the patients who had qPCR transcript levels between a 4.5 and 5.4 log reduction maintained TFR2 at 6 months. However, 28.7% who had qPCR deeper than 5.5 logs prior to dasatinib discontinuation had TFR2 at 6 months (P=0.017).

The risk factor analysis shed light, in part, on the reason the trial thus far failed to satisfy the null hypothesis.

“In other words, because we have selected a really good-risk group for TFR1, the remaining patients are actually a high-risk group for TFR2,” Dr. Kim said. “Because of that, the TFR2 rate might be somewhat lower than we had expected.”

“Or is it related to our conservative treatment with dasatinib, which is 12 months after achieving MR4.5 or deeper response? That may affect our TFR2 rate. We still have to think about that.”

Dr. Kim suggested stricter criteria be considered for attempting TFR2, such as achieving a 5.5 log reduction or deeper in BCR-ABL1 qPCR levels prior to the second TKI discontinuation attempt, and/or an MR4 duration of more than 12 months.

Dr. Kim disclosed receiving honoraria and research funding from Novartis and Bristol-Myers Squibb and serving as a consultant for Pfizer, Paladin, Novartis, and Bristol-Myers Squibb. 

Photo by Jen Smith

SAN DIEGO—Preliminary trial results suggest re-treatment with dasatinib is feasible and safe for a second attempt at tyrosine kinase inhibitor (TKI) discontinuation in chronic myeloid leukemia (CML) patients who fail to achieve treatment-free remission (TFR) after discontinuing imatinib.

However, investigators reported the rate of second TFR (TFR2) was 21% at 6 months, which was not enough to confirm, at this time, that dasatinib could improve the TFR2 rate after imatinib discontinuation failure.

Dennis Kim, MD, of the University of Toronto in Ontario, Canada, presented these results at the 2018 ASH Annual Meeting (abstract 787).

The design of this trial (NCT02268370) includes three phases: the imatinib discontinuation phase, the dasatinib re-challenge phase to achieve a molecular response of ≥ 4.5-log reduction in BCR-ABL1 transcripts (MR4.5), and the dasatinib discontinuation phase.

The primary objective of the trial is to determine the proportion of patients who remain in deep molecular remission (> MR4.5) after discontinuing dasatinib following a failed attempt at discontinuation of imatinib.

If patients had a confirmed molecular relapse after discontinuing imatinib, they were started on 100 mg of dasatinib daily, and, after achieving MR4.5 or greater for 12 months, they discontinued dasatinib for a try at the second TFR.

Investigators defined loss of molecular response, or relapse, as a loss of a major molecular response (MMR) once or loss of MR4.0 on two consecutive occasions.

Patient characteristics

The 131 enrolled CML patients were a median age of 61 (range, 21 to 84), and 62% were male.

Patients had a median 9.36 years of disease duration, 9.18 years of imatinib treatment, 6.82 years of MR4 duration, and 5.08 years of MR4.5 duration.

“The cohort has a very long history of imatinib treatment as well as MR4 duration,” Dr. Kim pointed out, “which also can affect our TFR1 rate, and I think, also, it can affect our TFR2 rate.”

TFR1 and TFR2 rates

As of October 25, the TFR1 rate using loss of MMR as the measure was 69.9% at 12 months from imatinib discontinuation. Relapse-free survival was 57.2% at that time.

Of the 53 patients who lost response, 51 patients received dasatinib. At 3 months of treatment, 97.7% achieved an MMR, 89.9% achieved MR4, and 84.6% achieved MR4.5.

Twenty-five of 51 patients treated with dasatinib attained MR4.5 for 12 months or longer and discontinued treatment for a second attempt at TFR.

Twenty-one patients are still receiving dasatinib and have attained MR4.5, but not for the 12-month duration yet.

Dr. Kim noted that the median time to achievement of molecular response after dasatinib re-challenge ranged from 2.76 months for MR4.5 to 1.71 months for MMR.

Twenty-one of 25 patients (84.0%) who discontinued dasatinib lost their molecular response at a median of 3.7 months.

The estimated TFR2 rate after dasatinib discontinuation is 21.0% to 24.4% at 6 months, which means the investigators cannot reject the null hypothesis of 28% or more patients remaining in remission.

Patients who lost response rapidly after dasatinib discontinuation also tended to lose response rapidly after imatinib discontinuation, Dr. Kim pointed out.

“However, you see some patients who do not lose their response after dasatinib discontinuation or who lose the response but later after the dasatinib discontinuation, they tend to lose their imatinib response also in a later time point,” he said. “So we started to look at the risk factors.”

Risk factor analysis

Out of seven potential risk factors, the investigators were able to demonstrate that time to molecular relapse after imatinib discontinuation, molecular relapse pattern after imatinib discontinuation, and BCR-ABL1 quantitative polymerase chain reaction (qPCR) value prior to dasatinib discontinuation “seemed to be very significant,” Dr. Kim said.

Time to molecular relapse after discontinuation of imatinib correlates with TFR2. The group of patients who relapsed in 3 to 6 months of stopping imatinib had a significantly longer TFR2 than patients who relapsed within 3 months of stopping imatinib (P=0.018).

The molecular relapse pattern also correlates with TFR2. The group with a single loss of MMR after imatinib discontinuation had a significantly shorter TFR2 than those who lost MR4 twice after imatinib discontinuation (P=0.043).

And 0% of the patients who had qPCR transcript levels between a 4.5 and 5.4 log reduction maintained TFR2 at 6 months. However, 28.7% who had qPCR deeper than 5.5 logs prior to dasatinib discontinuation had TFR2 at 6 months (P=0.017).

The risk factor analysis shed light, in part, on the reason the trial thus far failed to satisfy the null hypothesis.

“In other words, because we have selected a really good-risk group for TFR1, the remaining patients are actually a high-risk group for TFR2,” Dr. Kim said. “Because of that, the TFR2 rate might be somewhat lower than we had expected.”

“Or is it related to our conservative treatment with dasatinib, which is 12 months after achieving MR4.5 or deeper response? That may affect our TFR2 rate. We still have to think about that.”

Dr. Kim suggested stricter criteria be considered for attempting TFR2, such as achieving a 5.5 log reduction or deeper in BCR-ABL1 qPCR levels prior to the second TKI discontinuation attempt, and/or an MR4 duration of more than 12 months.

Dr. Kim disclosed receiving honoraria and research funding from Novartis and Bristol-Myers Squibb and serving as a consultant for Pfizer, Paladin, Novartis, and Bristol-Myers Squibb. 

Photo by Jen Smith

SAN DIEGO—Preliminary trial results suggest re-treatment with dasatinib is feasible and safe for a second attempt at tyrosine kinase inhibitor (TKI) discontinuation in chronic myeloid leukemia (CML) patients who fail to achieve treatment-free remission (TFR) after discontinuing imatinib.

However, investigators reported the rate of second TFR (TFR2) was 21% at 6 months, which was not enough to confirm, at this time, that dasatinib could improve the TFR2 rate after imatinib discontinuation failure.

Dennis Kim, MD, of the University of Toronto in Ontario, Canada, presented these results at the 2018 ASH Annual Meeting (abstract 787).

The design of this trial (NCT02268370) includes three phases: the imatinib discontinuation phase, the dasatinib re-challenge phase to achieve a molecular response of ≥ 4.5-log reduction in BCR-ABL1 transcripts (MR4.5), and the dasatinib discontinuation phase.

The primary objective of the trial is to determine the proportion of patients who remain in deep molecular remission (> MR4.5) after discontinuing dasatinib following a failed attempt at discontinuation of imatinib.

If patients had a confirmed molecular relapse after discontinuing imatinib, they were started on 100 mg of dasatinib daily, and, after achieving MR4.5 or greater for 12 months, they discontinued dasatinib for a try at the second TFR.

Investigators defined loss of molecular response, or relapse, as a loss of a major molecular response (MMR) once or loss of MR4.0 on two consecutive occasions.

Patient characteristics

The 131 enrolled CML patients were a median age of 61 (range, 21 to 84), and 62% were male.

Patients had a median 9.36 years of disease duration, 9.18 years of imatinib treatment, 6.82 years of MR4 duration, and 5.08 years of MR4.5 duration.

“The cohort has a very long history of imatinib treatment as well as MR4 duration,” Dr. Kim pointed out, “which also can affect our TFR1 rate, and I think, also, it can affect our TFR2 rate.”

TFR1 and TFR2 rates

As of October 25, the TFR1 rate using loss of MMR as the measure was 69.9% at 12 months from imatinib discontinuation. Relapse-free survival was 57.2% at that time.

Of the 53 patients who lost response, 51 patients received dasatinib. At 3 months of treatment, 97.7% achieved an MMR, 89.9% achieved MR4, and 84.6% achieved MR4.5.

Twenty-five of 51 patients treated with dasatinib attained MR4.5 for 12 months or longer and discontinued treatment for a second attempt at TFR.

Twenty-one patients are still receiving dasatinib and have attained MR4.5, but not for the 12-month duration yet.

Dr. Kim noted that the median time to achievement of molecular response after dasatinib re-challenge ranged from 2.76 months for MR4.5 to 1.71 months for MMR.

Twenty-one of 25 patients (84.0%) who discontinued dasatinib lost their molecular response at a median of 3.7 months.

The estimated TFR2 rate after dasatinib discontinuation is 21.0% to 24.4% at 6 months, which means the investigators cannot reject the null hypothesis of 28% or more patients remaining in remission.

Patients who lost response rapidly after dasatinib discontinuation also tended to lose response rapidly after imatinib discontinuation, Dr. Kim pointed out.

“However, you see some patients who do not lose their response after dasatinib discontinuation or who lose the response but later after the dasatinib discontinuation, they tend to lose their imatinib response also in a later time point,” he said. “So we started to look at the risk factors.”

Risk factor analysis

Out of seven potential risk factors, the investigators were able to demonstrate that time to molecular relapse after imatinib discontinuation, molecular relapse pattern after imatinib discontinuation, and BCR-ABL1 quantitative polymerase chain reaction (qPCR) value prior to dasatinib discontinuation “seemed to be very significant,” Dr. Kim said.

Time to molecular relapse after discontinuation of imatinib correlates with TFR2. The group of patients who relapsed in 3 to 6 months of stopping imatinib had a significantly longer TFR2 than patients who relapsed within 3 months of stopping imatinib (P=0.018).

The molecular relapse pattern also correlates with TFR2. The group with a single loss of MMR after imatinib discontinuation had a significantly shorter TFR2 than those who lost MR4 twice after imatinib discontinuation (P=0.043).

And 0% of the patients who had qPCR transcript levels between a 4.5 and 5.4 log reduction maintained TFR2 at 6 months. However, 28.7% who had qPCR deeper than 5.5 logs prior to dasatinib discontinuation had TFR2 at 6 months (P=0.017).

The risk factor analysis shed light, in part, on the reason the trial thus far failed to satisfy the null hypothesis.

“In other words, because we have selected a really good-risk group for TFR1, the remaining patients are actually a high-risk group for TFR2,” Dr. Kim said. “Because of that, the TFR2 rate might be somewhat lower than we had expected.”

“Or is it related to our conservative treatment with dasatinib, which is 12 months after achieving MR4.5 or deeper response? That may affect our TFR2 rate. We still have to think about that.”

Dr. Kim suggested stricter criteria be considered for attempting TFR2, such as achieving a 5.5 log reduction or deeper in BCR-ABL1 qPCR levels prior to the second TKI discontinuation attempt, and/or an MR4 duration of more than 12 months.

Dr. Kim disclosed receiving honoraria and research funding from Novartis and Bristol-Myers Squibb and serving as a consultant for Pfizer, Paladin, Novartis, and Bristol-Myers Squibb. 

We live in an ever-changing, fast-paced, and transitional world, and our health care system is no different. It’s hardly surprising, then, that digital health apps are becoming more commonplace in clinical practice. Need a useful tool to help you manage or monitor your patient’s chronic condition or educate them on preventive health and wellness measures? There’s an app for that.

If you question or lament this continual digital creep—or think it has no bearing on your patient population—you may be surprised to know that 77% of Americans have a smartphone with texting and/or mobile application abilities, creating innovative opportunities for health care providers to incorporate health apps into patient care.¹ And the benefits are not just a sales pitch on the part of manufacturers—in fact, the American Diabetes Association’s 2018 Standards of Care include a recommendation for use of mobile apps for the prevention and delayed progression of type 2 diabetes.² Of course, research shows that clinicians are more likely to adopt digital health tools if those tools improve practice efficiency, increase patient safety, improve diagnostic ability, help reduce burnout, and enhance patient-provider relationships.³

So maybe you see a role for apps in patient care. But the sheer volume and continuous proliferation of apps present an obstacle to effective evaluation and recommendation. With more than 318,000 health apps on the market and another 200 added every day, how do you know which ones are clinically sound and useful for your patients?⁴ Fortunately, there are two strategies that can help you integrate digital health apps into patient care.

1 HEALTH APPS AS MEDICATIONS

Viewing health apps as if they were medications can be helpful. Think about the process we as clinicians use when we’re thinking about prescribing a medication to a particular patient: we evaluate, manage, and prescribe.

Evaluate: As clinicians, we learn about the newest biopharmaceutical agents on the market to effectively govern our personal repertoire of medications and provide the best care for our patients. In this process, we evaluate clinical efficacy, safety, costs, benefits, barriers, contextual elements, caregiver impact, clinical studies, and more. This type of vetting process is also an effective approach to selecting and recommending health apps for your patients.

Manage: We each have a personal catalog of medications with which we become well versed, and comfortable, to effectively manage and help our patients with a multitude of medical conditions. This registry of medications represents our very special and individual “favorites,” per se. So, create a personal repertoire of health apps to improve and manage patient care.

Prescribe: Similar to medications, many digital health apps have demonstrated impressive patient outcomes with supporting clinical evidence. So why not get comfortable with prescribing digital health applications for behavior modifications or common medical conditions, just as you would with a medication?

Continue to: 2 BUILD YOUR PERSONAL APP LIBRARY

2 BUILD YOUR PERSONAL APP LIBRARY

Another strategy—touched upon in the “Manage” section earlier—is to create a personal library of highly regarded, well-vetted health apps to address common patient care matters. These could be recommended to a broad audience and will form the cornerstone of your digital compendium.

To get you started, Table 1 outlines a handful of health apps every primary care clinician should know about. These apps are supported by clinical research, endorsed or ranked by health care/industry expert organizations, and come recommended by clinical colleagues, students, or myself. The presented health apps are easily accessible via the App Store or Google Play and offer free versions, so you can assess and recommend them to your patients at no cost.

I hope you find these apps helpful with your future patient care efforts.

The author would like to acknowledge The Pace-Lenox Hill Hospital PA Program Class of 2019 for their research, evaluation, and feedback on a variety of digital health apps, and Jean Covino, DHSc, MPA , PA-C , for her encouragement to write and teach about my passion for health innovation.

We live in an ever-changing, fast-paced, and transitional world, and our health care system is no different. It’s hardly surprising, then, that digital health apps are becoming more commonplace in clinical practice. Need a useful tool to help you manage or monitor your patient’s chronic condition or educate them on preventive health and wellness measures? There’s an app for that.

If you question or lament this continual digital creep—or think it has no bearing on your patient population—you may be surprised to know that 77% of Americans have a smartphone with texting and/or mobile application abilities, creating innovative opportunities for health care providers to incorporate health apps into patient care.¹ And the benefits are not just a sales pitch on the part of manufacturers—in fact, the American Diabetes Association’s 2018 Standards of Care include a recommendation for use of mobile apps for the prevention and delayed progression of type 2 diabetes.² Of course, research shows that clinicians are more likely to adopt digital health tools if those tools improve practice efficiency, increase patient safety, improve diagnostic ability, help reduce burnout, and enhance patient-provider relationships.³

So maybe you see a role for apps in patient care. But the sheer volume and continuous proliferation of apps present an obstacle to effective evaluation and recommendation. With more than 318,000 health apps on the market and another 200 added every day, how do you know which ones are clinically sound and useful for your patients?⁴ Fortunately, there are two strategies that can help you integrate digital health apps into patient care.

1 HEALTH APPS AS MEDICATIONS

Viewing health apps as if they were medications can be helpful. Think about the process we as clinicians use when we’re thinking about prescribing a medication to a particular patient: we evaluate, manage, and prescribe.

Evaluate: As clinicians, we learn about the newest biopharmaceutical agents on the market to effectively govern our personal repertoire of medications and provide the best care for our patients. In this process, we evaluate clinical efficacy, safety, costs, benefits, barriers, contextual elements, caregiver impact, clinical studies, and more. This type of vetting process is also an effective approach to selecting and recommending health apps for your patients.

Manage: We each have a personal catalog of medications with which we become well versed, and comfortable, to effectively manage and help our patients with a multitude of medical conditions. This registry of medications represents our very special and individual “favorites,” per se. So, create a personal repertoire of health apps to improve and manage patient care.

Prescribe: Similar to medications, many digital health apps have demonstrated impressive patient outcomes with supporting clinical evidence. So why not get comfortable with prescribing digital health applications for behavior modifications or common medical conditions, just as you would with a medication?

Continue to: 2 BUILD YOUR PERSONAL APP LIBRARY

2 BUILD YOUR PERSONAL APP LIBRARY

Another strategy—touched upon in the “Manage” section earlier—is to create a personal library of highly regarded, well-vetted health apps to address common patient care matters. These could be recommended to a broad audience and will form the cornerstone of your digital compendium.

To get you started, Table 1 outlines a handful of health apps every primary care clinician should know about. These apps are supported by clinical research, endorsed or ranked by health care/industry expert organizations, and come recommended by clinical colleagues, students, or myself. The presented health apps are easily accessible via the App Store or Google Play and offer free versions, so you can assess and recommend them to your patients at no cost.

I hope you find these apps helpful with your future patient care efforts.

The author would like to acknowledge The Pace-Lenox Hill Hospital PA Program Class of 2019 for their research, evaluation, and feedback on a variety of digital health apps, and Jean Covino, DHSc, MPA , PA-C , for her encouragement to write and teach about my passion for health innovation.

We live in an ever-changing, fast-paced, and transitional world, and our health care system is no different. It’s hardly surprising, then, that digital health apps are becoming more commonplace in clinical practice. Need a useful tool to help you manage or monitor your patient’s chronic condition or educate them on preventive health and wellness measures? There’s an app for that.

If you question or lament this continual digital creep—or think it has no bearing on your patient population—you may be surprised to know that 77% of Americans have a smartphone with texting and/or mobile application abilities, creating innovative opportunities for health care providers to incorporate health apps into patient care.¹ And the benefits are not just a sales pitch on the part of manufacturers—in fact, the American Diabetes Association’s 2018 Standards of Care include a recommendation for use of mobile apps for the prevention and delayed progression of type 2 diabetes.² Of course, research shows that clinicians are more likely to adopt digital health tools if those tools improve practice efficiency, increase patient safety, improve diagnostic ability, help reduce burnout, and enhance patient-provider relationships.³

So maybe you see a role for apps in patient care. But the sheer volume and continuous proliferation of apps present an obstacle to effective evaluation and recommendation. With more than 318,000 health apps on the market and another 200 added every day, how do you know which ones are clinically sound and useful for your patients?⁴ Fortunately, there are two strategies that can help you integrate digital health apps into patient care.

1 HEALTH APPS AS MEDICATIONS

Viewing health apps as if they were medications can be helpful. Think about the process we as clinicians use when we’re thinking about prescribing a medication to a particular patient: we evaluate, manage, and prescribe.

Evaluate: As clinicians, we learn about the newest biopharmaceutical agents on the market to effectively govern our personal repertoire of medications and provide the best care for our patients. In this process, we evaluate clinical efficacy, safety, costs, benefits, barriers, contextual elements, caregiver impact, clinical studies, and more. This type of vetting process is also an effective approach to selecting and recommending health apps for your patients.

Manage: We each have a personal catalog of medications with which we become well versed, and comfortable, to effectively manage and help our patients with a multitude of medical conditions. This registry of medications represents our very special and individual “favorites,” per se. So, create a personal repertoire of health apps to improve and manage patient care.

Prescribe: Similar to medications, many digital health apps have demonstrated impressive patient outcomes with supporting clinical evidence. So why not get comfortable with prescribing digital health applications for behavior modifications or common medical conditions, just as you would with a medication?

Continue to: 2 BUILD YOUR PERSONAL APP LIBRARY

2 BUILD YOUR PERSONAL APP LIBRARY

Another strategy—touched upon in the “Manage” section earlier—is to create a personal library of highly regarded, well-vetted health apps to address common patient care matters. These could be recommended to a broad audience and will form the cornerstone of your digital compendium.

To get you started, Table 1 outlines a handful of health apps every primary care clinician should know about. These apps are supported by clinical research, endorsed or ranked by health care/industry expert organizations, and come recommended by clinical colleagues, students, or myself. The presented health apps are easily accessible via the App Store or Google Play and offer free versions, so you can assess and recommend them to your patients at no cost.

I hope you find these apps helpful with your future patient care efforts.

The author would like to acknowledge The Pace-Lenox Hill Hospital PA Program Class of 2019 for their research, evaluation, and feedback on a variety of digital health apps, and Jean Covino, DHSc, MPA , PA-C , for her encouragement to write and teach about my passion for health innovation.

The American College of Rheumatology and the National Psoriasis Foundation have released a joint treatment guideline for psoriatic arthritis that, for the first time, includes a conditional recommendation to use tumor necrosis factor–inhibitor(TNFi) biologics over methotrexate and other oral small molecules as a first-line therapy in patients with active disease.

“The available low-quality evidence is inconclusive regarding the efficacy of OSMs [oral small molecules] in management of PsA, whereas there is moderate-quality evidence of the benefits of TNFi biologics, in particular regarding their impact on the prevention of disease progression and joint damage,” wrote the panel of authors, who were led by Jasvinder A. Singh, MD, of the University of Alabama at Birmingham. “In making their recommendation, the panel recognized the cost implications, but put considerations of quality of evidence for benefit over other considerations. This guideline provides recommendations for early and aggressive therapy in patients with newly diagnosed PsA.”

The 28-page guideline, published online Nov. 30 in the Journal of Psoriasis and Psoriatic Arthritis and also in Arthritis Care & Research and Arthritis & Rheumatology, is the first set of PsA-specific recommendations to be assembled using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology that the ACR has used for RA and other conditions. GRADE uses systematic reviews of the scientific literature available to evaluate and grade the quality of evidence in a particular domain. The evidence reviews are then used to create guideline recommendations for or against particular therapy options that range from strong to conditional, depending on the quality of evidence available.

Based on the GRADE methodology and consensus building, the guideline authors crafted recommendations for eight different clinical scenarios, including the initial treatment of patients with active PsA who have not received either OSMs or other treatments; treatment of patients with active PsA despite treatment with an OSM; treatment of patients with active PsA despite treatment with a TNFi biologic either as monotherapy or in combination with methotrexate; treatment of patients with active PsA despite treatment with an interleukin (IL)-17 inhibitor or IL-12/23 inhibitor monotherapy; treatment of patients with active PsA including treat-to-target, active axial disease, enthesitis, or active inflammatory bowel disease; treatment of patients with active PsA and comorbidities, including concomitant diabetes and recurrent serious infections; vaccination in patients with active PsA; and treatment of patients with active PsA with nonpharmacologic interventions such as yoga and weight loss. Most of the treatment recommendations are conditional based on very low to moderate quality evidence. “Health care providers and patients must take into consideration all active disease domains, comorbidities, and the patient’s functional status in choosing the optimal therapy for an individual at a given point in time,” the authors emphasized.

Only five of the recommendations are listed as strong, including smoking cessation. Three of the strong recommendations concern adult patients with active PsA and concomitant active inflammatory bowel disease despite treatment with an OSM. They are “switch to a monoclonal antibody TNFi biologic over a TNFi biologic soluble receptor biologic,” “switch to a TNFi monoclonal antibody biologic over an IL-7i biologic,” and “switch to an IL-12/23i biologic over switching to an IL-17i biologic.”

The process of creating the guideline included input from a panel of nine adults who provided the authors with perspective on their values and preferences. “The concept of treat-to-target was challenging for patients,” the authors noted. “Although they saw value in improved outcomes, they also thought this strategy could increase costs to the patient (e.g., copayments, time traveling to more frequent appointments, etc.) and potentially increase adverse events. Therefore, a detailed conversation with the patient is needed to make decisions regarding treat-to-target.”

The authors concluded the guideline by calling for more comparative data to inform treatment selection in the future. “Several ongoing trials, including a trial to compare a TNFi biologic combination therapy with a TNFi biologic monotherapy and MTX monotherapy, will inform treatment decisions,” they wrote. “We anticipate future updates to the guideline when new evidence is available.”

Dr. Singh, who is also a staff rheumatologist at the Birmingham (Ala.) Veterans Affairs Medical Center, led development of the 2012 and 2015 ACR treatment guidelines for RA. He has received consulting fees from a variety of companies marketing rheumatologic drugs as well as research support from Takeda and Savient. The other guideline authors reported having numerous financial ties to industry.

[email protected]

SOURCE: Singh J et al. Arthritis Care Res. 2018 Nov 30. doi: 10.1002/acr.23789.

The American College of Rheumatology and the National Psoriasis Foundation have released a joint treatment guideline for psoriatic arthritis that, for the first time, includes a conditional recommendation to use tumor necrosis factor–inhibitor(TNFi) biologics over methotrexate and other oral small molecules as a first-line therapy in patients with active disease.

“The available low-quality evidence is inconclusive regarding the efficacy of OSMs [oral small molecules] in management of PsA, whereas there is moderate-quality evidence of the benefits of TNFi biologics, in particular regarding their impact on the prevention of disease progression and joint damage,” wrote the panel of authors, who were led by Jasvinder A. Singh, MD, of the University of Alabama at Birmingham. “In making their recommendation, the panel recognized the cost implications, but put considerations of quality of evidence for benefit over other considerations. This guideline provides recommendations for early and aggressive therapy in patients with newly diagnosed PsA.”

The 28-page guideline, published online Nov. 30 in the Journal of Psoriasis and Psoriatic Arthritis and also in Arthritis Care & Research and Arthritis & Rheumatology, is the first set of PsA-specific recommendations to be assembled using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology that the ACR has used for RA and other conditions. GRADE uses systematic reviews of the scientific literature available to evaluate and grade the quality of evidence in a particular domain. The evidence reviews are then used to create guideline recommendations for or against particular therapy options that range from strong to conditional, depending on the quality of evidence available.

Based on the GRADE methodology and consensus building, the guideline authors crafted recommendations for eight different clinical scenarios, including the initial treatment of patients with active PsA who have not received either OSMs or other treatments; treatment of patients with active PsA despite treatment with an OSM; treatment of patients with active PsA despite treatment with a TNFi biologic either as monotherapy or in combination with methotrexate; treatment of patients with active PsA despite treatment with an interleukin (IL)-17 inhibitor or IL-12/23 inhibitor monotherapy; treatment of patients with active PsA including treat-to-target, active axial disease, enthesitis, or active inflammatory bowel disease; treatment of patients with active PsA and comorbidities, including concomitant diabetes and recurrent serious infections; vaccination in patients with active PsA; and treatment of patients with active PsA with nonpharmacologic interventions such as yoga and weight loss. Most of the treatment recommendations are conditional based on very low to moderate quality evidence. “Health care providers and patients must take into consideration all active disease domains, comorbidities, and the patient’s functional status in choosing the optimal therapy for an individual at a given point in time,” the authors emphasized.

Only five of the recommendations are listed as strong, including smoking cessation. Three of the strong recommendations concern adult patients with active PsA and concomitant active inflammatory bowel disease despite treatment with an OSM. They are “switch to a monoclonal antibody TNFi biologic over a TNFi biologic soluble receptor biologic,” “switch to a TNFi monoclonal antibody biologic over an IL-7i biologic,” and “switch to an IL-12/23i biologic over switching to an IL-17i biologic.”

The process of creating the guideline included input from a panel of nine adults who provided the authors with perspective on their values and preferences. “The concept of treat-to-target was challenging for patients,” the authors noted. “Although they saw value in improved outcomes, they also thought this strategy could increase costs to the patient (e.g., copayments, time traveling to more frequent appointments, etc.) and potentially increase adverse events. Therefore, a detailed conversation with the patient is needed to make decisions regarding treat-to-target.”

The authors concluded the guideline by calling for more comparative data to inform treatment selection in the future. “Several ongoing trials, including a trial to compare a TNFi biologic combination therapy with a TNFi biologic monotherapy and MTX monotherapy, will inform treatment decisions,” they wrote. “We anticipate future updates to the guideline when new evidence is available.”

Dr. Singh, who is also a staff rheumatologist at the Birmingham (Ala.) Veterans Affairs Medical Center, led development of the 2012 and 2015 ACR treatment guidelines for RA. He has received consulting fees from a variety of companies marketing rheumatologic drugs as well as research support from Takeda and Savient. The other guideline authors reported having numerous financial ties to industry.

[email protected]

SOURCE: Singh J et al. Arthritis Care Res. 2018 Nov 30. doi: 10.1002/acr.23789.

The American College of Rheumatology and the National Psoriasis Foundation have released a joint treatment guideline for psoriatic arthritis that, for the first time, includes a conditional recommendation to use tumor necrosis factor–inhibitor(TNFi) biologics over methotrexate and other oral small molecules as a first-line therapy in patients with active disease.

“The available low-quality evidence is inconclusive regarding the efficacy of OSMs [oral small molecules] in management of PsA, whereas there is moderate-quality evidence of the benefits of TNFi biologics, in particular regarding their impact on the prevention of disease progression and joint damage,” wrote the panel of authors, who were led by Jasvinder A. Singh, MD, of the University of Alabama at Birmingham. “In making their recommendation, the panel recognized the cost implications, but put considerations of quality of evidence for benefit over other considerations. This guideline provides recommendations for early and aggressive therapy in patients with newly diagnosed PsA.”

The 28-page guideline, published online Nov. 30 in the Journal of Psoriasis and Psoriatic Arthritis and also in Arthritis Care & Research and Arthritis & Rheumatology, is the first set of PsA-specific recommendations to be assembled using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology that the ACR has used for RA and other conditions. GRADE uses systematic reviews of the scientific literature available to evaluate and grade the quality of evidence in a particular domain. The evidence reviews are then used to create guideline recommendations for or against particular therapy options that range from strong to conditional, depending on the quality of evidence available.

Based on the GRADE methodology and consensus building, the guideline authors crafted recommendations for eight different clinical scenarios, including the initial treatment of patients with active PsA who have not received either OSMs or other treatments; treatment of patients with active PsA despite treatment with an OSM; treatment of patients with active PsA despite treatment with a TNFi biologic either as monotherapy or in combination with methotrexate; treatment of patients with active PsA despite treatment with an interleukin (IL)-17 inhibitor or IL-12/23 inhibitor monotherapy; treatment of patients with active PsA including treat-to-target, active axial disease, enthesitis, or active inflammatory bowel disease; treatment of patients with active PsA and comorbidities, including concomitant diabetes and recurrent serious infections; vaccination in patients with active PsA; and treatment of patients with active PsA with nonpharmacologic interventions such as yoga and weight loss. Most of the treatment recommendations are conditional based on very low to moderate quality evidence. “Health care providers and patients must take into consideration all active disease domains, comorbidities, and the patient’s functional status in choosing the optimal therapy for an individual at a given point in time,” the authors emphasized.

Only five of the recommendations are listed as strong, including smoking cessation. Three of the strong recommendations concern adult patients with active PsA and concomitant active inflammatory bowel disease despite treatment with an OSM. They are “switch to a monoclonal antibody TNFi biologic over a TNFi biologic soluble receptor biologic,” “switch to a TNFi monoclonal antibody biologic over an IL-7i biologic,” and “switch to an IL-12/23i biologic over switching to an IL-17i biologic.”

The process of creating the guideline included input from a panel of nine adults who provided the authors with perspective on their values and preferences. “The concept of treat-to-target was challenging for patients,” the authors noted. “Although they saw value in improved outcomes, they also thought this strategy could increase costs to the patient (e.g., copayments, time traveling to more frequent appointments, etc.) and potentially increase adverse events. Therefore, a detailed conversation with the patient is needed to make decisions regarding treat-to-target.”

The authors concluded the guideline by calling for more comparative data to inform treatment selection in the future. “Several ongoing trials, including a trial to compare a TNFi biologic combination therapy with a TNFi biologic monotherapy and MTX monotherapy, will inform treatment decisions,” they wrote. “We anticipate future updates to the guideline when new evidence is available.”

Dr. Singh, who is also a staff rheumatologist at the Birmingham (Ala.) Veterans Affairs Medical Center, led development of the 2012 and 2015 ACR treatment guidelines for RA. He has received consulting fees from a variety of companies marketing rheumatologic drugs as well as research support from Takeda and Savient. The other guideline authors reported having numerous financial ties to industry.

[email protected]

SOURCE: Singh J et al. Arthritis Care Res. 2018 Nov 30. doi: 10.1002/acr.23789.

There will be 53 million older adults in the US by 2020.¹ Increasing age often brings medical comorbidities and prescriptions for multiple medications. An increasing number of prescribed medications combined with age-related changes in the ability to metabolize drugs makes older adults highly vulnerable to adverse drug events (ADEs).² In addition, older adults often have difficulty self-managing their medications and adhering to prescribed regimens.³ As a result, ADEs can lead to poor health outcomes, including hospitalizations, in older adults.

Medication errors and ADEs are particularly common during transitions from hospital to home and can lead to unnecessary readmissions,a major cause of wasteful health care spending in the US.^4,5 More than $25 billion are estimated to be spent annually on hospital readmissions, with Medicare picking up the bill for $17 billion of the total.^6,7 Researchers have found that the majority of ADEs following hospital discharge are either entirely preventable or at least ameliorable (ie, the negative impact or harm resulting from the ADE could have been reduced).⁸

To address these issues, we undertook a clinical demonstration project that implemented a new transitional care program to improve the quality of care for older veterans transitioning from the Audie L. Murphy Veterans Memorial Hospital of the South Texas Veterans Health Care System (STVHCS) in San Antonio to home. The Geriatrics Medication Education at Discharge project (GMED) falls under the auspices of the San Antonio Geriatrics Research Education and Clinical Center (GRECC). Clinical demonstration projects are mandated for US Department of Veterans Affairs (VA) GRECCs to create and promote innovative models of care for older veterans. Dissemination of successful clinical demonstration projects to other VA sites is strongly encouraged. The GMED program was modeled after the Boston GRECC Pharmacological Intervention in Late Life (PILL) program.⁹ The PILL program, which focuses on serving older veterans with cognitive impairment, demonstrated that a postdischarge pharmacist telephone visit for medication reconciliation leads to a reduction in readmission within 60 days of discharge.⁹ The goals of the GMED program were to reduce polypharmacy, inappropriate prescribing and 30-day readmissions.

Methods

The project was conducted when a full-time clinical pharmacy specialist (CPS) was available (May-September 2013 and April 2014-March 2015). This project was approved as nonresearch/quality improvement by the University of Texas Health Science Center Institutional Review Board, which serves the STVHCS. Consent was not required.

Eligibility

Patients were identified via a daily hospital database query of all adults aged ≥ 65 years admitted to the hospital through Inpatient Medicine, Neurology, or Cardiology services within the prior 24 hours. Patients meeting any of the following criteria based on review of the Computerized Patient Record System (CPRS) by the team geriatrician and CPS were considered eligible: (1) aged ≥ 70 years prescribed ≥ 12 outpatient medications; (2) aged ≥ 65 years with a medical history of dementia; (3) aged ≥ 65 years prescribed outpatient medications meeting Beers criteria¹⁰; (4) age ≥ 65 years with ≥ 2 hospital admissions (including the current, index admission) within the past calendar year; or (5) aged ≥ 65 years with ≥ 3 emergency department visits within the past calendar year. For the first polypharmacy criterion, patients aged ≥ 70 years were selected instead of aged ≥ 65 years so as not to exceed the capacity of 1 CPS. Twelve or more medications were used as a cutoff for polypharmacy based on prior quality improvement information gathered from our VA geriatrics clinic examining the average number of medications taken by older veterans in the outpatient setting.

Related: Reducing COPD Readmission Rates: Using a COPD Care Service During Care Transitions

Patients were excluded if they were expected to be discharged to any facility where the patient and/or the caregiver were not primarily responsible for medication administration after discharge. Patients who met eligibility criteria but were not seen by the transitional program pharmacist (due to staff capacity) were included in this analysis as a convenience comparison group of patients who received usual care. Patients were not randomized. All communication occurred in English, but this project did not exclude patients with limited English proficiency.

A program support assistant conducted the daily query of the hospital database. The pharmacist conducted the chart review to determine eligibility and delivered the intervention. Eligible patients were selected at random for the intervention with the intention of providing the intervention to as many veterans as possible.

The GMED Intervention

The GMED program included 2 phases, which were both conducted by a CPS with oversight from a senior CPS with geriatric pharmacology expertise and an internist/geriatrician. 

The first phase of the transitional care program included an individual, face-to-face meeting between the CPS and the patient during the hospitalization. If a veteran was not present in the room at the time of an attempted visit, the pharmacist made 2 additional attempts (3 total) to include the patient in the transitional care program during the hospitalization. 

The second component of the transitional care program included a telephone visit within 2 to 3 days of discharge, conducted by the same CPS who performed the face-to-face visit. The purpose of the telephone visit was to perform medication reconciliation, identify and rectify medication errors, provide further patient education, and assist in facilitating appropriate follow-up by the patient’s primary care provider (PCP), if required. At a minimum, veterans were asked a series of questions pertaining to their concerns about medication regimens, receipt of newly prescribed medications at discharge, additional education regarding medications after the CPS encounter during hospitalization, and whether the veteran required assistance with the medication regimen in the home setting. Follow-up questions were asked as needed to clarify and identify potential medication problems. All information from this telephone encounter was communicated to the PCP through CPRS documentation and by telephone as needed.

Related: Initiative to Minimize Pharmaceutical Risk in Older Veterans (IMPROVE) Polypharmacy Clinic

Data Collection

A standardized questionnaire was used prospectively for patients in the transitional care program group to assess patient education, primary residence, presence of a caregiver, fall history, medication adherence, and cognitive status (using Mini-Cog).¹³ Additional information (patient age, number of outpatient medications prior to and following the admission, presence of Beers criteria outpatient medications prior to and following the admission, new outpatient prescriptions, and changes to existing prescriptions as a result of the hospitalization) was gathered prospectively from patient interviews or from chart review.

For patients included in the comparison group, a retrospective administrative chart review was conducted to collect information such as age, sex, ethnic group, admission within 1 year prior to index admission, frailty, and Charlson Comorbidity Index (CCI) score, a method of categorizing comorbidities of patients based on the diagnosis codes found in administrative data.¹⁴ Each comorbidity category has an associated weight (from 1 to 6), based on the adjusted risk of mortality or resource use, and the sum of all the weights results in a single comorbidity score for a patient (0 indicates no comorbidities; higher scores predict greater risk of mortality or increased resource use).

We used the index developed from 17 disease categories. The range for CCI was 0 to 25. Frailty was defined as the presence of any of the following frailty-related diagnoses: anemia; fall, head injury, other injury; coagulopathy; electrolyte disturbance; or gait disorder. These diagnoses are either primary frailty characteristics within the frailty phenotype or have been shown in prior studies to be associated with the frailty phenotype.^15-18 While more widely accepted frailty definitions exist,these other definitions require direct examination of the patient and could not be used in this project because we did not directly interact with the comparison group.^16,19 The frailty definition used has been previously identified as a predictor of health care utilization and 30-day readmission in a veteran population.²⁰ Whether or not the CPS detected a postdischarge medication error was recorded. All CPS recommendations were documented.

An index admission was defined as a hospital admission that occurred during the project period. Thirty-day readmission was defined as a hospital admission that occurred within 30 days of the discharge date of an index admission. Each index admission was considered individually for readmission (yes vs no) even if it occurred in the same patient over the project period. A 30-day readmission was not considered an index admission. An admission that occurred after a 30-day readmission was considered a subsequent index admission. Patients who died in the hospital were not included in this analysis, as they would not have participated in the entire intervention.

Statistical Analysis

We compared characteristics between patients who received GMED and patients who never received GMED (comparison group). Generalized estimating equations (GEE) were used to determine whether the rate of 30-day readmission (yes vs no) in the transitional care program group differed from that of the comparison group. In our GEE analysis, we assumed a binomial distribution and the logit link to model the log-odds of readmission as a linear function of transitional care program status (yes vs no) and other covariates, including age, frailty, hospital admission within 1 year prior to the index admission, and CCI score as covariates. Thirty-day readmission status associated with each index admission was coded as 1 for a readmission within 30 days of the discharge date of the index admission, or 0 for no readmission within 30 days.

Transitional care program status was determined whether or not the individual received the transitional care program for each index admission. This analysis allowed us to model repeated measures of index admissions as a function of the project period and whether the patient was seen by the GMED CPS during the index admission. The patient identifier was used as a cluster variable in the GEE analysis. Inverse propensity scores of receiving GMED at the index admission were adjusted as weights in the GEE analysis to minimize confounding and, hence, to strengthen the causal interpretation of the effect of the transitional care program. If there was ≥ 1 index admission, the GMED status (yes vs no) at the initial index admission was used as the dependent variable to calculate propensity scores. The propensity scores of transitional care program status were derived from the logistic regression analysis that modeled the log-odds of receiving the transitional care program at the index admission as a linear function of age, CCI, frailty, and prior hospitalization during the 1-year period prior to the index admission.

Related: Development and Implementation of a Geriatric Walking Clinic

Results

The GMED CPS saw 435 patients during the project period; 47 (10.8%) died prior to 30 days and were excluded, leaving 388 patients who received the transitional care program included in this evaluation. 

Data from the CPS-patient interviews and chart reviews were available for 378 of the 388 patients (Table 2). Patients were primarily male, non-Hispanic white, with a high school education. More than half (65%) the patients were admitted for a new diagnosis or clinical condition. 

The 30-day readmission rate was 15.6% for the transitional care program group and 21.9% for the comparison group. Three hundred seventy-one patients received the transitional care program only once, 16 patients received the transitional care program twice (ie, they had 2 index admissions during the study period and received the intervention both times), and 1 patient received the transitional care program 3 times.

In an unadjusted GEE model, the odds ratio (OR) for readmission in the transitional care program group was 0.74 (95% CI, 0.54-1.0, P = .06) compared with the usual care group (Table 3). 

Thirty-five percent of patients had ≥ 1 CPS-recommended change in their treatment at the time of the inpatient admission (Table 4). 

Discussion

We developed a transitional care program for hospitalized older veterans to improve the transition from hospital to home. After adjusting for clinical factors, GMED was associated with 26% lower odds of readmission within 30 days of discharge compared with that of the control group. The GMED CPS made changes to the medical regimen both during the inpatient admission as well as after discharge to correct medication errors and educate patients.

In addition, GMED led to a reduction in the number of prescribed medications, which impacts inappropriate polypharmacy—a significant problem in older adults, which contributes to ADEs.²¹ Our intervention was patient centered, as all decisions and education regarding medication management were tailored to each patient, taking into account medical and psychosocial factors.

Studies of similar programs have shown that a pharmacist-based program can improve outcomes in patients transitioning from hospital to home. A meta-analysis of 19 studies that evaluated the effectiveness of pharmacy-led medication reconciliation interventions at the time of a care transition showed that compared with usual care a pharmacist intervention led to reduced medication discrepancies.²² In this meta-analysis, medication discrepancies of higher clinical impact were more easily identified through pharmacy-led interventions than with usual care, suggesting improved safety. Although not all studies have shown a clear reduction in readmission rates or other health care utilization, the addition of clinical pharmacist services in the care of inpatients has generally resulted in improved care with no evidence of harm.²³

Based on these findings and collaboration with another GRECC, we designed our program to focus on older adults with polypharmacy, cognitive impairment, high-risk medication usage, and/or a history of high health care use.⁹ Our findings add to the growing body of evidence that a CPS-led transitional care program results in reduced polypharmacy and reduced unnecessary hospital readmissions. Further, our findings have demonstrated the effectiveness of this type of program in a practical, clinical setting with veteran patients.

At the time of project inception, we believed that the majority of our interventions would occur postdischarge. We were somewhat surprised that a major component of GMED was suggested interventions by our pharmacist at the time of admission. We believe that because the CPS made suggestions during admission, we prevented postdischarge ADEs. A frequent intervention corrected the medication reconciliation on file at admission. This finding also was seen in another study by Gleason and colleagues, which examined medication errors at admission for 651 adult medicine inpatients.²⁴ This study found that more than one-third of patients had medication reconciliation errors. Further, older age (≥ 65 years) was associated with increased odds of medication errors in this study.

Of note, a survey of hospital-based pharmacists indicated medication reconciliation is the most important role of the pharmacist in improving care transitions.²⁵ The pharmacists stated that detection of errors at the time of admission is very important. The pharmacists further reported that additional education and counseling for patients with poor understanding of their medications was also important. Our findings support these findings and the use of a pharmacist as part of the medical team to improve medication reconciliation and education.

Limitations

A limitation of GMED is that we monitored only admissions to our hospital; therefore, we did not account for any hospitalizations that may have occurred outside the STVHCS. Another limitation is that this was not a randomized controlled trial, and we used a convenience sample of patients who met our criteria for eligibility but were not seen due to time constraints. This introduces potential bias such that patients admitted and discharged on nights or weekends when the CPS was not available were not included in the transitional care program group, and these patients may fundamentally differ from those admitted and discharged Monday through Friday.

However, Khanna and colleagues found that night or weekend admission was not associated with 30-day readmission or other worse outcomes (such as length of stay, 30-day emergency department visit, or intensive care unit transfer) in 857 general medicine admissions at a tertiary care hospital.²⁶ Every effort was made to include as many eligible patients as possible in the transitional program group, and we were able to demonstrate that the patients in the 2 groups were similar. Frailty and prior hospital admission were more prevalent, although not significantly so, in the transitional program group, suggesting that any selection bias would have actually attenuated—not enhanced—the observed effect of the transitional program. Although the transitional program group patients were slightly younger by 0.3 years, they were similar in frailty status and CCI score.

Conclusion

The GMED program was associated with reduced 30-day hospital readmission, discontinuation of unnecessary medications, and corrected medication errors and discrepancies. We propose that a CPS-based transitional care program can improve the quality of care for older patients being discharged to home.

Acknowledgments

Supported by funding from the Veterans Health Administration T21 Non-Institutional Long-Term Care Initiative and VA Office of Rural Health and the San Antonio Geriatrics Research, Education, and Clinical Center. The sponsor did not have any role in the design, methods, data collection, or analysis, and preparation.

Author Contributions

R. Rottman-Sagebiel developed the transitional program concept and design and executed the program implementation, interpretation of data, and preparation of the manuscript. S. Pastewait, N. Cupples, A. Conde, M. Moris, and E. Gonzalez assisted with program design and implementation. S. Cope assisted with interpretation of data and preparation of the manuscript. H. Braden assisted with interpretation of data. D. MacCarthy assisted with data management and statistical analysis. C. Wang and S. Espinoza developed the program concept and design, performed statistical analysis and interpretation of data, and helped prepare the manuscript.

Advances in Geriatrics

Advances in Geriatrics features articles focused on quality improvement/quality assurance initiatives, pilot studies, best practices, research, patient education, and patient-centered care written by health care providers associated with Veteran Health Administration Geriatric Research Education and Clinical Centers. Interested authors can submit articles at editorialmanager.com/fedprac or send a brief 2 to 3 sentence abstract to [email protected] for feedback and publication recommendations.

There will be 53 million older adults in the US by 2020.¹ Increasing age often brings medical comorbidities and prescriptions for multiple medications. An increasing number of prescribed medications combined with age-related changes in the ability to metabolize drugs makes older adults highly vulnerable to adverse drug events (ADEs).² In addition, older adults often have difficulty self-managing their medications and adhering to prescribed regimens.³ As a result, ADEs can lead to poor health outcomes, including hospitalizations, in older adults.

Medication errors and ADEs are particularly common during transitions from hospital to home and can lead to unnecessary readmissions,a major cause of wasteful health care spending in the US.^4,5 More than $25 billion are estimated to be spent annually on hospital readmissions, with Medicare picking up the bill for $17 billion of the total.^6,7 Researchers have found that the majority of ADEs following hospital discharge are either entirely preventable or at least ameliorable (ie, the negative impact or harm resulting from the ADE could have been reduced).⁸

To address these issues, we undertook a clinical demonstration project that implemented a new transitional care program to improve the quality of care for older veterans transitioning from the Audie L. Murphy Veterans Memorial Hospital of the South Texas Veterans Health Care System (STVHCS) in San Antonio to home. The Geriatrics Medication Education at Discharge project (GMED) falls under the auspices of the San Antonio Geriatrics Research Education and Clinical Center (GRECC). Clinical demonstration projects are mandated for US Department of Veterans Affairs (VA) GRECCs to create and promote innovative models of care for older veterans. Dissemination of successful clinical demonstration projects to other VA sites is strongly encouraged. The GMED program was modeled after the Boston GRECC Pharmacological Intervention in Late Life (PILL) program.⁹ The PILL program, which focuses on serving older veterans with cognitive impairment, demonstrated that a postdischarge pharmacist telephone visit for medication reconciliation leads to a reduction in readmission within 60 days of discharge.⁹ The goals of the GMED program were to reduce polypharmacy, inappropriate prescribing and 30-day readmissions.

Methods

The project was conducted when a full-time clinical pharmacy specialist (CPS) was available (May-September 2013 and April 2014-March 2015). This project was approved as nonresearch/quality improvement by the University of Texas Health Science Center Institutional Review Board, which serves the STVHCS. Consent was not required.

Eligibility

Patients were identified via a daily hospital database query of all adults aged ≥ 65 years admitted to the hospital through Inpatient Medicine, Neurology, or Cardiology services within the prior 24 hours. Patients meeting any of the following criteria based on review of the Computerized Patient Record System (CPRS) by the team geriatrician and CPS were considered eligible: (1) aged ≥ 70 years prescribed ≥ 12 outpatient medications; (2) aged ≥ 65 years with a medical history of dementia; (3) aged ≥ 65 years prescribed outpatient medications meeting Beers criteria¹⁰; (4) age ≥ 65 years with ≥ 2 hospital admissions (including the current, index admission) within the past calendar year; or (5) aged ≥ 65 years with ≥ 3 emergency department visits within the past calendar year. For the first polypharmacy criterion, patients aged ≥ 70 years were selected instead of aged ≥ 65 years so as not to exceed the capacity of 1 CPS. Twelve or more medications were used as a cutoff for polypharmacy based on prior quality improvement information gathered from our VA geriatrics clinic examining the average number of medications taken by older veterans in the outpatient setting.

Related: Reducing COPD Readmission Rates: Using a COPD Care Service During Care Transitions

Patients were excluded if they were expected to be discharged to any facility where the patient and/or the caregiver were not primarily responsible for medication administration after discharge. Patients who met eligibility criteria but were not seen by the transitional program pharmacist (due to staff capacity) were included in this analysis as a convenience comparison group of patients who received usual care. Patients were not randomized. All communication occurred in English, but this project did not exclude patients with limited English proficiency.

A program support assistant conducted the daily query of the hospital database. The pharmacist conducted the chart review to determine eligibility and delivered the intervention. Eligible patients were selected at random for the intervention with the intention of providing the intervention to as many veterans as possible.

The GMED Intervention

The GMED program included 2 phases, which were both conducted by a CPS with oversight from a senior CPS with geriatric pharmacology expertise and an internist/geriatrician. 

The first phase of the transitional care program included an individual, face-to-face meeting between the CPS and the patient during the hospitalization. If a veteran was not present in the room at the time of an attempted visit, the pharmacist made 2 additional attempts (3 total) to include the patient in the transitional care program during the hospitalization. 

The second component of the transitional care program included a telephone visit within 2 to 3 days of discharge, conducted by the same CPS who performed the face-to-face visit. The purpose of the telephone visit was to perform medication reconciliation, identify and rectify medication errors, provide further patient education, and assist in facilitating appropriate follow-up by the patient’s primary care provider (PCP), if required. At a minimum, veterans were asked a series of questions pertaining to their concerns about medication regimens, receipt of newly prescribed medications at discharge, additional education regarding medications after the CPS encounter during hospitalization, and whether the veteran required assistance with the medication regimen in the home setting. Follow-up questions were asked as needed to clarify and identify potential medication problems. All information from this telephone encounter was communicated to the PCP through CPRS documentation and by telephone as needed.

Related: Initiative to Minimize Pharmaceutical Risk in Older Veterans (IMPROVE) Polypharmacy Clinic

Data Collection

A standardized questionnaire was used prospectively for patients in the transitional care program group to assess patient education, primary residence, presence of a caregiver, fall history, medication adherence, and cognitive status (using Mini-Cog).¹³ Additional information (patient age, number of outpatient medications prior to and following the admission, presence of Beers criteria outpatient medications prior to and following the admission, new outpatient prescriptions, and changes to existing prescriptions as a result of the hospitalization) was gathered prospectively from patient interviews or from chart review.

For patients included in the comparison group, a retrospective administrative chart review was conducted to collect information such as age, sex, ethnic group, admission within 1 year prior to index admission, frailty, and Charlson Comorbidity Index (CCI) score, a method of categorizing comorbidities of patients based on the diagnosis codes found in administrative data.¹⁴ Each comorbidity category has an associated weight (from 1 to 6), based on the adjusted risk of mortality or resource use, and the sum of all the weights results in a single comorbidity score for a patient (0 indicates no comorbidities; higher scores predict greater risk of mortality or increased resource use).

We used the index developed from 17 disease categories. The range for CCI was 0 to 25. Frailty was defined as the presence of any of the following frailty-related diagnoses: anemia; fall, head injury, other injury; coagulopathy; electrolyte disturbance; or gait disorder. These diagnoses are either primary frailty characteristics within the frailty phenotype or have been shown in prior studies to be associated with the frailty phenotype.^15-18 While more widely accepted frailty definitions exist,these other definitions require direct examination of the patient and could not be used in this project because we did not directly interact with the comparison group.^16,19 The frailty definition used has been previously identified as a predictor of health care utilization and 30-day readmission in a veteran population.²⁰ Whether or not the CPS detected a postdischarge medication error was recorded. All CPS recommendations were documented.

An index admission was defined as a hospital admission that occurred during the project period. Thirty-day readmission was defined as a hospital admission that occurred within 30 days of the discharge date of an index admission. Each index admission was considered individually for readmission (yes vs no) even if it occurred in the same patient over the project period. A 30-day readmission was not considered an index admission. An admission that occurred after a 30-day readmission was considered a subsequent index admission. Patients who died in the hospital were not included in this analysis, as they would not have participated in the entire intervention.

Statistical Analysis

We compared characteristics between patients who received GMED and patients who never received GMED (comparison group). Generalized estimating equations (GEE) were used to determine whether the rate of 30-day readmission (yes vs no) in the transitional care program group differed from that of the comparison group. In our GEE analysis, we assumed a binomial distribution and the logit link to model the log-odds of readmission as a linear function of transitional care program status (yes vs no) and other covariates, including age, frailty, hospital admission within 1 year prior to the index admission, and CCI score as covariates. Thirty-day readmission status associated with each index admission was coded as 1 for a readmission within 30 days of the discharge date of the index admission, or 0 for no readmission within 30 days.

Transitional care program status was determined whether or not the individual received the transitional care program for each index admission. This analysis allowed us to model repeated measures of index admissions as a function of the project period and whether the patient was seen by the GMED CPS during the index admission. The patient identifier was used as a cluster variable in the GEE analysis. Inverse propensity scores of receiving GMED at the index admission were adjusted as weights in the GEE analysis to minimize confounding and, hence, to strengthen the causal interpretation of the effect of the transitional care program. If there was ≥ 1 index admission, the GMED status (yes vs no) at the initial index admission was used as the dependent variable to calculate propensity scores. The propensity scores of transitional care program status were derived from the logistic regression analysis that modeled the log-odds of receiving the transitional care program at the index admission as a linear function of age, CCI, frailty, and prior hospitalization during the 1-year period prior to the index admission.

Related: Development and Implementation of a Geriatric Walking Clinic

Results

The GMED CPS saw 435 patients during the project period; 47 (10.8%) died prior to 30 days and were excluded, leaving 388 patients who received the transitional care program included in this evaluation. 

Data from the CPS-patient interviews and chart reviews were available for 378 of the 388 patients (Table 2). Patients were primarily male, non-Hispanic white, with a high school education. More than half (65%) the patients were admitted for a new diagnosis or clinical condition. 

The 30-day readmission rate was 15.6% for the transitional care program group and 21.9% for the comparison group. Three hundred seventy-one patients received the transitional care program only once, 16 patients received the transitional care program twice (ie, they had 2 index admissions during the study period and received the intervention both times), and 1 patient received the transitional care program 3 times.

In an unadjusted GEE model, the odds ratio (OR) for readmission in the transitional care program group was 0.74 (95% CI, 0.54-1.0, P = .06) compared with the usual care group (Table 3). 

Thirty-five percent of patients had ≥ 1 CPS-recommended change in their treatment at the time of the inpatient admission (Table 4). 

Discussion

We developed a transitional care program for hospitalized older veterans to improve the transition from hospital to home. After adjusting for clinical factors, GMED was associated with 26% lower odds of readmission within 30 days of discharge compared with that of the control group. The GMED CPS made changes to the medical regimen both during the inpatient admission as well as after discharge to correct medication errors and educate patients.

In addition, GMED led to a reduction in the number of prescribed medications, which impacts inappropriate polypharmacy—a significant problem in older adults, which contributes to ADEs.²¹ Our intervention was patient centered, as all decisions and education regarding medication management were tailored to each patient, taking into account medical and psychosocial factors.

Studies of similar programs have shown that a pharmacist-based program can improve outcomes in patients transitioning from hospital to home. A meta-analysis of 19 studies that evaluated the effectiveness of pharmacy-led medication reconciliation interventions at the time of a care transition showed that compared with usual care a pharmacist intervention led to reduced medication discrepancies.²² In this meta-analysis, medication discrepancies of higher clinical impact were more easily identified through pharmacy-led interventions than with usual care, suggesting improved safety. Although not all studies have shown a clear reduction in readmission rates or other health care utilization, the addition of clinical pharmacist services in the care of inpatients has generally resulted in improved care with no evidence of harm.²³

Based on these findings and collaboration with another GRECC, we designed our program to focus on older adults with polypharmacy, cognitive impairment, high-risk medication usage, and/or a history of high health care use.⁹ Our findings add to the growing body of evidence that a CPS-led transitional care program results in reduced polypharmacy and reduced unnecessary hospital readmissions. Further, our findings have demonstrated the effectiveness of this type of program in a practical, clinical setting with veteran patients.

At the time of project inception, we believed that the majority of our interventions would occur postdischarge. We were somewhat surprised that a major component of GMED was suggested interventions by our pharmacist at the time of admission. We believe that because the CPS made suggestions during admission, we prevented postdischarge ADEs. A frequent intervention corrected the medication reconciliation on file at admission. This finding also was seen in another study by Gleason and colleagues, which examined medication errors at admission for 651 adult medicine inpatients.²⁴ This study found that more than one-third of patients had medication reconciliation errors. Further, older age (≥ 65 years) was associated with increased odds of medication errors in this study.

Of note, a survey of hospital-based pharmacists indicated medication reconciliation is the most important role of the pharmacist in improving care transitions.²⁵ The pharmacists stated that detection of errors at the time of admission is very important. The pharmacists further reported that additional education and counseling for patients with poor understanding of their medications was also important. Our findings support these findings and the use of a pharmacist as part of the medical team to improve medication reconciliation and education.

Limitations

A limitation of GMED is that we monitored only admissions to our hospital; therefore, we did not account for any hospitalizations that may have occurred outside the STVHCS. Another limitation is that this was not a randomized controlled trial, and we used a convenience sample of patients who met our criteria for eligibility but were not seen due to time constraints. This introduces potential bias such that patients admitted and discharged on nights or weekends when the CPS was not available were not included in the transitional care program group, and these patients may fundamentally differ from those admitted and discharged Monday through Friday.

However, Khanna and colleagues found that night or weekend admission was not associated with 30-day readmission or other worse outcomes (such as length of stay, 30-day emergency department visit, or intensive care unit transfer) in 857 general medicine admissions at a tertiary care hospital.²⁶ Every effort was made to include as many eligible patients as possible in the transitional program group, and we were able to demonstrate that the patients in the 2 groups were similar. Frailty and prior hospital admission were more prevalent, although not significantly so, in the transitional program group, suggesting that any selection bias would have actually attenuated—not enhanced—the observed effect of the transitional program. Although the transitional program group patients were slightly younger by 0.3 years, they were similar in frailty status and CCI score.

Conclusion

The GMED program was associated with reduced 30-day hospital readmission, discontinuation of unnecessary medications, and corrected medication errors and discrepancies. We propose that a CPS-based transitional care program can improve the quality of care for older patients being discharged to home.

Acknowledgments

Supported by funding from the Veterans Health Administration T21 Non-Institutional Long-Term Care Initiative and VA Office of Rural Health and the San Antonio Geriatrics Research, Education, and Clinical Center. The sponsor did not have any role in the design, methods, data collection, or analysis, and preparation.

Author Contributions

R. Rottman-Sagebiel developed the transitional program concept and design and executed the program implementation, interpretation of data, and preparation of the manuscript. S. Pastewait, N. Cupples, A. Conde, M. Moris, and E. Gonzalez assisted with program design and implementation. S. Cope assisted with interpretation of data and preparation of the manuscript. H. Braden assisted with interpretation of data. D. MacCarthy assisted with data management and statistical analysis. C. Wang and S. Espinoza developed the program concept and design, performed statistical analysis and interpretation of data, and helped prepare the manuscript.

Advances in Geriatrics

Advances in Geriatrics features articles focused on quality improvement/quality assurance initiatives, pilot studies, best practices, research, patient education, and patient-centered care written by health care providers associated with Veteran Health Administration Geriatric Research Education and Clinical Centers. Interested authors can submit articles at editorialmanager.com/fedprac or send a brief 2 to 3 sentence abstract to [email protected] for feedback and publication recommendations.

There will be 53 million older adults in the US by 2020.¹ Increasing age often brings medical comorbidities and prescriptions for multiple medications. An increasing number of prescribed medications combined with age-related changes in the ability to metabolize drugs makes older adults highly vulnerable to adverse drug events (ADEs).² In addition, older adults often have difficulty self-managing their medications and adhering to prescribed regimens.³ As a result, ADEs can lead to poor health outcomes, including hospitalizations, in older adults.

Medication errors and ADEs are particularly common during transitions from hospital to home and can lead to unnecessary readmissions,a major cause of wasteful health care spending in the US.^4,5 More than $25 billion are estimated to be spent annually on hospital readmissions, with Medicare picking up the bill for $17 billion of the total.^6,7 Researchers have found that the majority of ADEs following hospital discharge are either entirely preventable or at least ameliorable (ie, the negative impact or harm resulting from the ADE could have been reduced).⁸

To address these issues, we undertook a clinical demonstration project that implemented a new transitional care program to improve the quality of care for older veterans transitioning from the Audie L. Murphy Veterans Memorial Hospital of the South Texas Veterans Health Care System (STVHCS) in San Antonio to home. The Geriatrics Medication Education at Discharge project (GMED) falls under the auspices of the San Antonio Geriatrics Research Education and Clinical Center (GRECC). Clinical demonstration projects are mandated for US Department of Veterans Affairs (VA) GRECCs to create and promote innovative models of care for older veterans. Dissemination of successful clinical demonstration projects to other VA sites is strongly encouraged. The GMED program was modeled after the Boston GRECC Pharmacological Intervention in Late Life (PILL) program.⁹ The PILL program, which focuses on serving older veterans with cognitive impairment, demonstrated that a postdischarge pharmacist telephone visit for medication reconciliation leads to a reduction in readmission within 60 days of discharge.⁹ The goals of the GMED program were to reduce polypharmacy, inappropriate prescribing and 30-day readmissions.

Methods

The project was conducted when a full-time clinical pharmacy specialist (CPS) was available (May-September 2013 and April 2014-March 2015). This project was approved as nonresearch/quality improvement by the University of Texas Health Science Center Institutional Review Board, which serves the STVHCS. Consent was not required.

Eligibility

Patients were identified via a daily hospital database query of all adults aged ≥ 65 years admitted to the hospital through Inpatient Medicine, Neurology, or Cardiology services within the prior 24 hours. Patients meeting any of the following criteria based on review of the Computerized Patient Record System (CPRS) by the team geriatrician and CPS were considered eligible: (1) aged ≥ 70 years prescribed ≥ 12 outpatient medications; (2) aged ≥ 65 years with a medical history of dementia; (3) aged ≥ 65 years prescribed outpatient medications meeting Beers criteria¹⁰; (4) age ≥ 65 years with ≥ 2 hospital admissions (including the current, index admission) within the past calendar year; or (5) aged ≥ 65 years with ≥ 3 emergency department visits within the past calendar year. For the first polypharmacy criterion, patients aged ≥ 70 years were selected instead of aged ≥ 65 years so as not to exceed the capacity of 1 CPS. Twelve or more medications were used as a cutoff for polypharmacy based on prior quality improvement information gathered from our VA geriatrics clinic examining the average number of medications taken by older veterans in the outpatient setting.

Related: Reducing COPD Readmission Rates: Using a COPD Care Service During Care Transitions

Patients were excluded if they were expected to be discharged to any facility where the patient and/or the caregiver were not primarily responsible for medication administration after discharge. Patients who met eligibility criteria but were not seen by the transitional program pharmacist (due to staff capacity) were included in this analysis as a convenience comparison group of patients who received usual care. Patients were not randomized. All communication occurred in English, but this project did not exclude patients with limited English proficiency.

A program support assistant conducted the daily query of the hospital database. The pharmacist conducted the chart review to determine eligibility and delivered the intervention. Eligible patients were selected at random for the intervention with the intention of providing the intervention to as many veterans as possible.

The GMED Intervention

The GMED program included 2 phases, which were both conducted by a CPS with oversight from a senior CPS with geriatric pharmacology expertise and an internist/geriatrician. 

The first phase of the transitional care program included an individual, face-to-face meeting between the CPS and the patient during the hospitalization. If a veteran was not present in the room at the time of an attempted visit, the pharmacist made 2 additional attempts (3 total) to include the patient in the transitional care program during the hospitalization. 

The second component of the transitional care program included a telephone visit within 2 to 3 days of discharge, conducted by the same CPS who performed the face-to-face visit. The purpose of the telephone visit was to perform medication reconciliation, identify and rectify medication errors, provide further patient education, and assist in facilitating appropriate follow-up by the patient’s primary care provider (PCP), if required. At a minimum, veterans were asked a series of questions pertaining to their concerns about medication regimens, receipt of newly prescribed medications at discharge, additional education regarding medications after the CPS encounter during hospitalization, and whether the veteran required assistance with the medication regimen in the home setting. Follow-up questions were asked as needed to clarify and identify potential medication problems. All information from this telephone encounter was communicated to the PCP through CPRS documentation and by telephone as needed.

Related: Initiative to Minimize Pharmaceutical Risk in Older Veterans (IMPROVE) Polypharmacy Clinic

Data Collection

A standardized questionnaire was used prospectively for patients in the transitional care program group to assess patient education, primary residence, presence of a caregiver, fall history, medication adherence, and cognitive status (using Mini-Cog).¹³ Additional information (patient age, number of outpatient medications prior to and following the admission, presence of Beers criteria outpatient medications prior to and following the admission, new outpatient prescriptions, and changes to existing prescriptions as a result of the hospitalization) was gathered prospectively from patient interviews or from chart review.

For patients included in the comparison group, a retrospective administrative chart review was conducted to collect information such as age, sex, ethnic group, admission within 1 year prior to index admission, frailty, and Charlson Comorbidity Index (CCI) score, a method of categorizing comorbidities of patients based on the diagnosis codes found in administrative data.¹⁴ Each comorbidity category has an associated weight (from 1 to 6), based on the adjusted risk of mortality or resource use, and the sum of all the weights results in a single comorbidity score for a patient (0 indicates no comorbidities; higher scores predict greater risk of mortality or increased resource use).

We used the index developed from 17 disease categories. The range for CCI was 0 to 25. Frailty was defined as the presence of any of the following frailty-related diagnoses: anemia; fall, head injury, other injury; coagulopathy; electrolyte disturbance; or gait disorder. These diagnoses are either primary frailty characteristics within the frailty phenotype or have been shown in prior studies to be associated with the frailty phenotype.^15-18 While more widely accepted frailty definitions exist,these other definitions require direct examination of the patient and could not be used in this project because we did not directly interact with the comparison group.^16,19 The frailty definition used has been previously identified as a predictor of health care utilization and 30-day readmission in a veteran population.²⁰ Whether or not the CPS detected a postdischarge medication error was recorded. All CPS recommendations were documented.

An index admission was defined as a hospital admission that occurred during the project period. Thirty-day readmission was defined as a hospital admission that occurred within 30 days of the discharge date of an index admission. Each index admission was considered individually for readmission (yes vs no) even if it occurred in the same patient over the project period. A 30-day readmission was not considered an index admission. An admission that occurred after a 30-day readmission was considered a subsequent index admission. Patients who died in the hospital were not included in this analysis, as they would not have participated in the entire intervention.

Statistical Analysis

We compared characteristics between patients who received GMED and patients who never received GMED (comparison group). Generalized estimating equations (GEE) were used to determine whether the rate of 30-day readmission (yes vs no) in the transitional care program group differed from that of the comparison group. In our GEE analysis, we assumed a binomial distribution and the logit link to model the log-odds of readmission as a linear function of transitional care program status (yes vs no) and other covariates, including age, frailty, hospital admission within 1 year prior to the index admission, and CCI score as covariates. Thirty-day readmission status associated with each index admission was coded as 1 for a readmission within 30 days of the discharge date of the index admission, or 0 for no readmission within 30 days.

Transitional care program status was determined whether or not the individual received the transitional care program for each index admission. This analysis allowed us to model repeated measures of index admissions as a function of the project period and whether the patient was seen by the GMED CPS during the index admission. The patient identifier was used as a cluster variable in the GEE analysis. Inverse propensity scores of receiving GMED at the index admission were adjusted as weights in the GEE analysis to minimize confounding and, hence, to strengthen the causal interpretation of the effect of the transitional care program. If there was ≥ 1 index admission, the GMED status (yes vs no) at the initial index admission was used as the dependent variable to calculate propensity scores. The propensity scores of transitional care program status were derived from the logistic regression analysis that modeled the log-odds of receiving the transitional care program at the index admission as a linear function of age, CCI, frailty, and prior hospitalization during the 1-year period prior to the index admission.

Related: Development and Implementation of a Geriatric Walking Clinic

Results

The GMED CPS saw 435 patients during the project period; 47 (10.8%) died prior to 30 days and were excluded, leaving 388 patients who received the transitional care program included in this evaluation. 

Data from the CPS-patient interviews and chart reviews were available for 378 of the 388 patients (Table 2). Patients were primarily male, non-Hispanic white, with a high school education. More than half (65%) the patients were admitted for a new diagnosis or clinical condition. 

The 30-day readmission rate was 15.6% for the transitional care program group and 21.9% for the comparison group. Three hundred seventy-one patients received the transitional care program only once, 16 patients received the transitional care program twice (ie, they had 2 index admissions during the study period and received the intervention both times), and 1 patient received the transitional care program 3 times.

In an unadjusted GEE model, the odds ratio (OR) for readmission in the transitional care program group was 0.74 (95% CI, 0.54-1.0, P = .06) compared with the usual care group (Table 3). 

Thirty-five percent of patients had ≥ 1 CPS-recommended change in their treatment at the time of the inpatient admission (Table 4). 

Discussion

We developed a transitional care program for hospitalized older veterans to improve the transition from hospital to home. After adjusting for clinical factors, GMED was associated with 26% lower odds of readmission within 30 days of discharge compared with that of the control group. The GMED CPS made changes to the medical regimen both during the inpatient admission as well as after discharge to correct medication errors and educate patients.

In addition, GMED led to a reduction in the number of prescribed medications, which impacts inappropriate polypharmacy—a significant problem in older adults, which contributes to ADEs.²¹ Our intervention was patient centered, as all decisions and education regarding medication management were tailored to each patient, taking into account medical and psychosocial factors.

Studies of similar programs have shown that a pharmacist-based program can improve outcomes in patients transitioning from hospital to home. A meta-analysis of 19 studies that evaluated the effectiveness of pharmacy-led medication reconciliation interventions at the time of a care transition showed that compared with usual care a pharmacist intervention led to reduced medication discrepancies.²² In this meta-analysis, medication discrepancies of higher clinical impact were more easily identified through pharmacy-led interventions than with usual care, suggesting improved safety. Although not all studies have shown a clear reduction in readmission rates or other health care utilization, the addition of clinical pharmacist services in the care of inpatients has generally resulted in improved care with no evidence of harm.²³

Based on these findings and collaboration with another GRECC, we designed our program to focus on older adults with polypharmacy, cognitive impairment, high-risk medication usage, and/or a history of high health care use.⁹ Our findings add to the growing body of evidence that a CPS-led transitional care program results in reduced polypharmacy and reduced unnecessary hospital readmissions. Further, our findings have demonstrated the effectiveness of this type of program in a practical, clinical setting with veteran patients.

At the time of project inception, we believed that the majority of our interventions would occur postdischarge. We were somewhat surprised that a major component of GMED was suggested interventions by our pharmacist at the time of admission. We believe that because the CPS made suggestions during admission, we prevented postdischarge ADEs. A frequent intervention corrected the medication reconciliation on file at admission. This finding also was seen in another study by Gleason and colleagues, which examined medication errors at admission for 651 adult medicine inpatients.²⁴ This study found that more than one-third of patients had medication reconciliation errors. Further, older age (≥ 65 years) was associated with increased odds of medication errors in this study.

Of note, a survey of hospital-based pharmacists indicated medication reconciliation is the most important role of the pharmacist in improving care transitions.²⁵ The pharmacists stated that detection of errors at the time of admission is very important. The pharmacists further reported that additional education and counseling for patients with poor understanding of their medications was also important. Our findings support these findings and the use of a pharmacist as part of the medical team to improve medication reconciliation and education.

Limitations

A limitation of GMED is that we monitored only admissions to our hospital; therefore, we did not account for any hospitalizations that may have occurred outside the STVHCS. Another limitation is that this was not a randomized controlled trial, and we used a convenience sample of patients who met our criteria for eligibility but were not seen due to time constraints. This introduces potential bias such that patients admitted and discharged on nights or weekends when the CPS was not available were not included in the transitional care program group, and these patients may fundamentally differ from those admitted and discharged Monday through Friday.

However, Khanna and colleagues found that night or weekend admission was not associated with 30-day readmission or other worse outcomes (such as length of stay, 30-day emergency department visit, or intensive care unit transfer) in 857 general medicine admissions at a tertiary care hospital.²⁶ Every effort was made to include as many eligible patients as possible in the transitional program group, and we were able to demonstrate that the patients in the 2 groups were similar. Frailty and prior hospital admission were more prevalent, although not significantly so, in the transitional program group, suggesting that any selection bias would have actually attenuated—not enhanced—the observed effect of the transitional program. Although the transitional program group patients were slightly younger by 0.3 years, they were similar in frailty status and CCI score.

Conclusion

The GMED program was associated with reduced 30-day hospital readmission, discontinuation of unnecessary medications, and corrected medication errors and discrepancies. We propose that a CPS-based transitional care program can improve the quality of care for older patients being discharged to home.

Acknowledgments

Supported by funding from the Veterans Health Administration T21 Non-Institutional Long-Term Care Initiative and VA Office of Rural Health and the San Antonio Geriatrics Research, Education, and Clinical Center. The sponsor did not have any role in the design, methods, data collection, or analysis, and preparation.

Author Contributions

R. Rottman-Sagebiel developed the transitional program concept and design and executed the program implementation, interpretation of data, and preparation of the manuscript. S. Pastewait, N. Cupples, A. Conde, M. Moris, and E. Gonzalez assisted with program design and implementation. S. Cope assisted with interpretation of data and preparation of the manuscript. H. Braden assisted with interpretation of data. D. MacCarthy assisted with data management and statistical analysis. C. Wang and S. Espinoza developed the program concept and design, performed statistical analysis and interpretation of data, and helped prepare the manuscript.

Advances in Geriatrics

Advances in Geriatrics features articles focused on quality improvement/quality assurance initiatives, pilot studies, best practices, research, patient education, and patient-centered care written by health care providers associated with Veteran Health Administration Geriatric Research Education and Clinical Centers. Interested authors can submit articles at editorialmanager.com/fedprac or send a brief 2 to 3 sentence abstract to [email protected] for feedback and publication recommendations.