Search

Background Sacubitril/Valsartan has been highly efficacious in randomized trials of heart failure with reduced ejection fraction (HFrEF). However, the effectiveness of sacubitril/valsartan in older patients hospitalized for HFrEF in real-world US practice is unclear. Methods and Results This study included Medicare beneficiaries age ≥65 years who were hospitalized for HFrEF ≤40% in the Get With The Guidelines-Heart Failure registry between October 2015 and December 2018, and eligible for sacubitril/valsartan. Associations between discharge prescription of sacubitril/valsartan and clinical outcomes were assessed after inverse probability of treatment weighting and adjustment for other HFrEF medications. Overall, 1551 (10.9%) patients were discharged on sacubitril/valsartan. Of those not prescribed sacubitril/valsartan, 7857 (62.0%) were prescribed an angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker. Over 12-month follow-up, compared with a discharge prescription of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker, sacubitril/valsartan was independently associated with lower all-cause mortality (adjusted hazard ratio [HR], 0.82; 95% CI, 0.72-0.94; P=0.004) but not all-cause hospitalization (adjusted HR, 0.97; 95% CI, 0.89-1.07; P=0.55) or heart failure hospitalization (adjusted HR, 1.04; 95% CI, 0.91-1.18; P=0.59). Patients prescribed sacubitril/valsartan versus those without a prescription had lower risk of all-cause mortality (adjusted HR, 0.69; 95% CI, 0.60-0.79; P

Background The angiotensin-receptor/neprilysin inhibitor ( ARNI ) sacubitril/valsartan reduces hospitalization and mortality for patients with heart failure with reduced ejection fraction. However, adoption of ARNI into clinical practice has been slow. Factors influencing use of ARNI have not been fully elucidated. Using data from the Get With The Guidelines-Heart Failure registry, Hospital Compare, Dartmouth Atlas, and the American Hospital Association Survey, we sought to identify hospital characteristics associated with patient-level receipt of an ARNI prescription. Methods and Results We analyzed patients with heart failure with reduced ejection fraction who were eligible for ARNI prescription (ejection fraction≤40%, no contraindications) and hospitalized from October 1, 2015 through December 31, 2016. We used logistic regression to estimate the associations between hospital characteristics and patient ARNI prescription at hospital discharge, accounting for clustering of patients within hospitals using generalized estimating equation methods and adjusting for patient-level covariates. Of 16 674 eligible hospitalizations from 210 hospitals, 1020 patients (6.1%) were prescribed ARNI at discharge. The median hospital-level proportion of patients prescribed ARNI was 3.3% (Q1, Q3: 0%, 12.6%). After adjustment for patient-level covariates, for-profit hospitals had significantly higher odds of ARNI prescription compared with not-for-profit hospitals (odds ratio, 2.53; 95% CI , 1.05-6.10; P=0.04), and hospitals located in the Western United States had lower odds of ARNI prescription compared with those in the Northeast (odds ratio, 0.33; 95% CI , 0.13-0.84; P=0.02). Conclusions Relatively few hospital characteristics were associated with ARNI prescription at hospital discharge, in contrast to what has been observed in early adoption in other disease areas. Additional evaluation of barriers to implementing new evidence into heart failure practice is needed.

BackgroundSurveys of patients with cardiovascular disease have suggested that "home-time"-being alive and out of any health care institution-is a prioritized outcome. This novel measure has not been studied among patients with heart failure (HF).ObjectivesThis study sought to characterize home-time following hospitalization for HF and assess its relationship with patient characteristics and traditionally reported clinical outcomes.MethodsUsing GWTG-HF (Get With The Guidelines-Heart Failure) registry data, patients discharged alive from an HF hospitalization between 2011 and 2014 and ≥65 years of age were identified. Using Medicare claims, post-discharge home-time over 30-day and 1-year follow-up was calculated for each patient as the number of days alive and spent outside of a hospital, skilled nursing facility (SNF), or rehabilitation facility.ResultsAmong 59,736 patients, 57,992 (97.1%) and 42,153 (70.6%) had complete follow-up for home-time calculation through 30 days and 1 year, respectively. The mean home-time was 21.6 ± 11.7 days at 30 days and 243.9 ± 137.6 days at 1 year. Contributions to reduced home-time varied by follow-up period, with days spent in SNF being the largest contributor though 30 days and death being the largest contributor through 1 year. Over 1 year, 2,044 (4.8%) patients had no home-time following index hospitalization discharge, whereas 8,194 (19.4%) had 365 days of home-time. In regression models, several conditions were associated with substantially reduced home-time, including chronic obstructive pulmonary disease, renal insufficiency, and dementia. Through 1 year, home-time was highly correlated with time-to-event endpoints of death (tau = 0.72) and the composite of death or HF readmission (tau = 0.59).ConclusionsHome-time, which can be readily calculated from administrative claims data, is substantially reduced for many patients following hospitalization for HF and is highly correlated with traditional time-to-event mortality and hospitalization outcomes. Home-time represents a novel, easily measured, patient-centered endpoint that may reflect effectiveness of interventions in future HF studies.

Reducing hospital readmissions for patients with heart failure is a national priority, and quality improvement campaigns are targeting reductions of ≥20%. However, there are limited data on whether such targets have been met. We analyzed data from the American Heart Association's Get With The Guidelines-Heart Failure registry linked to Medicare claims between 2009 and 2012 to describe trends and relative reduction of rates of 30-day all-cause readmission among patients with heart failure. A total of 21,264 patients with heart failure were included from 70 US sites from January 2009 to October 2012. Overall hospital-level, risk-adjusted, 30-day all-cause readmission rates declined slightly, from 20.0% (SD, 1.3%) in 2009 to 19.0% (SD, 1.2%) in 2012 (P=0.001). Only 1 in 70 (1.4%) hospitals achieved the 20% relative reduction in 30-day risk-adjusted readmission rates. A multivariable linear regression model was used to determine hospital-level factors associated with relative improvements in 30-day risk-adjusted readmissions between 2009 and 2012. Teaching hospitals had higher relative readmission rates as compared with their peers, and hospitals that used postdischarge heart failure disease management programs had lower relative readmission rates. Although there has been slight improvement in 30-day all-cause readmission rates during the past 4 years in patients with heart failure, few hospitals have seen large success.

BackgroundPeripheral artery disease (PAD) affects 8 to 10 million Americans, who face significantly elevated risks of both mortality and major limb events such as amputation. Unfortunately, PAD is relatively underdiagnosed, undertreated, and underresearched, leading to wide variations in treatment patterns and outcomes. Efforts to improve PAD care and outcomes have been hampered by persistent difficulties identifying patients with PAD for clinical and investigatory purposes. ObjectiveThe aim of this study is to develop and validate a model-based algorithm to detect patients with peripheral artery disease (PAD) using data from an electronic health record (EHR) system. MethodsAn initial query of the EHR in a large health system identified all patients with PAD-related diagnosis codes for any encounter during the study period. Clinical adjudication of PAD diagnosis was performed by chart review on a random subgroup. A binary logistic regression to predict PAD was built and validated using a least absolute shrinkage and selection operator (LASSO) approach in the adjudicated patients. The algorithm was then applied to the nonsampled records to further evaluate its performance. ResultsThe initial EHR data query using 406 diagnostic codes yielded 15,406 patients. Overall, 2500 patients were randomly selected for ground truth PAD status adjudication. In the end, 108 code flags remained after removing rarely- and never-used codes. We entered these code flags plus administrative encounter, imaging, procedure, and specialist flags into a LASSO model. The area under the curve for this model was 0.862. ConclusionsThe algorithm we constructed has two main advantages over other approaches to the identification of patients with PAD. First, it was derived from a broad population of patients with many different PAD manifestations and treatment pathways across a large health system. Second, our model does not rely on clinical notes and can be applied in situations in which only administrative billing data (eg, large administrative data sets) are available. A combination of diagnosis codes and administrative flags can accurately identify patients with PAD in large cohorts.

Objective: Cancer and its treatments disturb sleep-wake functioning; however, there is little information available on the characteristics and consequences of sleep problems associated with cancer. As part of an effort to improve measurement of sleep-wake functioning, we explored the scope of difficulties with sleep in a diverse group of patients diagnosed with cancer.Methods: We conducted 10 focus groups with patients recruited from the Duke University tumor registry and oncology/hematology clinics. Separate groups were held with patients scheduled to begin or currently undergoing treatment for breast, prostate, lung, colorectal, hematological, and other cancer types and with patients who were in posttreatment follow-up. The content of the focus group discussions was transcribed and analyzed for major themes by independent coders.Results: Participants not only reported causes of sleep disturbance common in other populations, such as pain and restless legs, but they also reported causes that may be unique to cancer populations, including abnormal dreams, anxiety about cancer diagnosis and recurrence, night sweats, and problems with sleep positioning. Many participants felt that sleep problems reduced their productivity, concentration, social interactions, and overall quality of life. Many also shared beliefs about the increased importance of sleep when fighting cancer.Conclusions: The findings underscore the need for interventions that minimize the negative impact of cancer and its treatments on sleep. This study will inform efforts now underway to develop a patient-reported measure of sleep-wake functioning that reflects the breadth of concepts considered important by patients with cancer. [ABSTRACT FROM AUTHOR]

Objective: Cancer and its treatments disturb sleep-wake functioning; however, there is little information available on the characteristics and consequences of sleep problems associated with cancer. As part of an effort to improve measurement of sleep-wake functioning, we explored the scope of difficulties with sleep in a diverse group of patients diagnosed with cancer. Methods: We conducted 10 focus groups with patients recruited from the Duke University tumor registry and oncology/hematology clinics. Separate groups were held with patients scheduled to begin or currently undergoing treatment for breast, prostate, lung, colorectal, hematological, and other cancer types and with patients who were in posttreatment follow-up. The content of the focus group discussions was transcribed and analyzed for major themes by independent coders. Results: Participants not only reported causes of sleep disturbance common in other populations, such as pain and restless legs, but they also reported causes that may be unique to cancer populations, including abnormal dreams, anxiety about cancer diagnosis and recurrence, night sweats, and problems with sleep positioning. Many participants felt that sleep problems reduced their productivity, concentration, social interactions, and overall quality of life. Many also shared beliefs about the increased importance of sleep when fighting cancer. Conclusions: The findings underscore the need for interventions that minimize the negative impact of cancer and its treatments on sleep. This study will inform efforts now underway to develop a patient-reported measure of sleep-wake functioning that reflects the breadth of concepts considered important by patients with cancer. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]