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• W2904647079 abstract "Background Models for predicting the outcome of patients hospitalized for heart failure (HF) rarely take a holistic view. We assessed the ability of measures 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." @default.
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• W2904647079 date "2019-03-01" @default.
• W2904647079 modified "2023-09-23" @default.
• W2904647079 title "Added value of frailty and social support in predicting risk of 30-day unplanned re-admission or death for patients with heart failure: An analysis from OPERA-HF" @default.
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• W2904647079 doi "https://doi.org/10.1016/j.ijcard.2018.12.030" @default.
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