Background: Models for predicting the outcome of patients hospitalized for heart failure (HF) rarely take a holistic view. We assessed the ability ofmeasures of frailty and social support in addition to demographic, clinical, imaging and laboratory variables to predict short-term outcome for patients discharged after a hospitalization for HF.
Methods: OPERA-HF is a prospective observational cohort, enrolling patients hospitalized for HF in a single center in Hull, UK. Variables were combined in a logistic regression model after multiple imputation of missing data to predict the composite outcome of death or readmission at 30 days. Comparisons were made to a model using clinical variables alone. The discriminative performance of each model was internally validated with bootstrap re-sampling.
Results: 1094 patients were included (mean age 77 [interquartile range 68-83] years; 40% women; 56% with moderate to severe left ventricular systolic dysfunction) of whom 213 (19%) had an unplanned re-admission and 60 (5%) died within 30 days. For the composite outcome, a model containing clinical variables alone had an area under the receiver-operating characteristic curve (AUC) of 0.68 [95% CI 0.64-0.72]. Adding marital status, support from family and measures of physical frailty increased the AUC (p <0.05) to 0.70 [95% CI 0.66-0.74].
Conclusions: Measures of physical frailty and social support improve prediction of 30-day outcome after an admission for HF but predicting near-term events remains imperfect. Further external validation and improvement of the model is required. (c) 2018 Elsevier B. V. All rights reserved.
- 30-day re-admission
- Heart failure
- Psychosocial factors