I Sokoreli1, J G Cleland2, S C Pauws3, E W Steyerberg4, J J G de Vries5, J M Riistama5, K Dobbs6, J Bulemfu6, A L Clark7. 1. Philips Research - Healthcare, Eindhoven, the Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: ioanna.sokoreli@philips.com. 2. University of Hull, Hull, UK; National Heart & Lung Institute, Imperial College, London, UK; London and Robertson Centre for Biostatistics & Clinical Trials, University of Glasgow, UK. 3. Philips Research - Healthcare, Eindhoven, the Netherlands; TiCC - University of Tilburg, Tilburg, the Netherlands. 4. Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands; Department of Public Health, Erasmus MC, Rotterdam, the Netherlands. 5. Philips Research - Healthcare, Eindhoven, the Netherlands. 6. Castle Hill Hospital, Hull, UK. 7. University of Hull, Hull, UK.
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.
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.
Authors: Hanzhang Xu; Heather R Farmer; Bradi B Granger; Kevin L Thomas; Eric D Peterson; Matthew E Dupre Journal: Circ Cardiovasc Qual Outcomes Date: 2021-01-12
Authors: Pei-Pei Zheng; Si-Min Yao; Jing Shi; Yu-Hao Wan; Di Guo; Ling-Ling Cui; Ning Sun; Hua Wang; Jie-Fu Yang Journal: Front Cardiovasc Med Date: 2020-12-10