Impact of non-cardiovascular disease burden on thirty-day hospital readmission in heart failure patients

Abstract

Background: Little is known about the impact of non-cardiovascular disease (CVD) burden on 30--day readmission in heart failure (HF) patients. The aim of the study was to assess the role of non-CVD burden on 30-day readmission in HF patients. Methods: We analyzed the effect of non-CVD burden by frequency of ICD-9 code categories on readmissions of patients discharged with a primary diagnosis of HF. We first modeled the probability of readmission within 30 days as a function of demographic and clinical covariates in a randomly selected training dataset of the total cohort. Variable selection was carried out using a bootstrap LASSO procedure with 1000 bootstrap samples, the final model was tested on a validation dataset. Adjusted odds ratios and confidence intervals were reported in the validation dataset. Results: There were a total of 6228 HF hospitalizations, 1523 (24%) with readmission within 30 days of discharge. The strongest predictor for 30-day readmissions was any hospital admission in the prior year (p < 0.001). Cardiovascular risk factors did not enter the final model. However, digestive system diseases increased the risk for readmission by 17% for each diagnosis (p = 0.046), while respiratory diseases and genitourinary diseases showed a trend toward a higher risk of readmission (p = 0.07 and p = 0.09, respectively). Non-CVDs out-competed cardiovascular covariates previously reported to predict readmission. Conclusions: In patients with HF hospitalization, prior admissions predicted 30-day readmission. Diseases of the digestive system also increase 30-day readmission rates. Assessment of non-CVD burden in HF patients could serve as an important risk marker for 30-day readmissions.

Predicting 90 day acute heart failure readmission and death using machine learning‐supported decision analysis

“…Up to 25% of HF, patients may be readmitted within 30 days of discharge, with an additional mortality risk of ~10% 4‐7 . In the past few years, multiple HF risk‐prediction models have been proposed to identify HF patients at highest risk of 30‐day readmission or death–with the focus on 30‐day outcomes, in part related to reporting standards and reimbursement penalties in the U.S 8‐11 . However, window of vulnerability is likely longer, with the spike in event rates occurring out 90 days post‐discharge and plateauing thereafter 5,7 .…”

Slow breathing improves cardiovascular reactivity to mental stress and health-related quality of life in heart failure patients with reduced ejection fraction

“…Chronic heart failure (HF) is considered a global pandemic with survival outcomes as malignant as some common cancers [1]. The progression of HF, worsening of symptoms and increasingly fre-quent hospitalizations negatively influence patient functional ability and health-related quality of life (QoL) [2][3][4][5]. Depression is one of the most important factors determining QoL in HF patients, this also contributes to social isolation [6,7].…”

Readmitted Patients With Heart Failure Sick, Tired, and Symptomatic

“…18 Numerous study authors have identified both clinical and nonclinical factors associated with HF 30-day all-cause readmission. Clinical factors include multimorbidity, 8,19,20 respiratory disease, renal failure, diabetes, 8,[19][20][21] depression, and anxiety. 8,20 Heart failure in isolation is rare; multimorbidity complicates the clinical management 20 and poses challenges as patients attempt to recognize symptom exacerbation.…”

“…8,20 Heart failure in isolation is rare; multimorbidity complicates the clinical management 20 and poses challenges as patients attempt to recognize symptom exacerbation. 22 Nonclinical and sociodemographic factors that have been shown to predict 30-day all-cause readmission include previous hospitalization for HF, 8,21 longer length of stay on index admission, 19,23 male gender, 3,[19][20][21]23,24 age older than 60 years, 3,[19][20][21]23,24 Medicare coverage, 8,19,25 being retired or unemployed, 23,25 and living alone. 20,24,25 Authors of recent studies suggest that race may be dependent on the patient population studied; for example, some identify Black race 8,19,25 as a predictor of readmission, whereas others report White race 21,23,24 as a readmission predictor.…”