Utilizing longitudinal data in assessing all‐cause mortality in patients hospitalized with heart failure

Abstract

Abstract Aims Risk stratification in patients with a new onset or worsened heart failure (HF) is essential for clinical decision making. We have utilized a novel approach to enrich patient level prognostication using longitudinally gathered data to develop ML‐based algorithms predicting all‐cause 30, 90, 180, 360, and 720 day mortality. Methods and results In a cohort of 2449 HF patients hospitalized between 1 January 2011 and 31 December 2017, we utilized 422 parameters derived from 151 451 patient exams. They included clinical phenotyping, ECG, laboratory, echocardiography, catheterization data or percutaneous and surgical interventions reflecting the standard of care as captured in individual electronic records. The development of predictive models consisted of 101 iterations of repeated random subsampling splits into balanced training and validation sets. ML models yielded area under the receiver operating characteristic curve (AUC‐ROC) performance ranging from 0.83 to 0.89 on the outcome‐balanced validation set in predicting all‐cause mortality at aforementioned time‐limits. The 1 year mortality prediction model recorded an AUC of 0.85. We observed stable model performance across all HF phenotypes: HFpEF 0.83 AUC, HFmrEF 0.85 AUC, and HFrEF 0.86 AUC, respectively. Model performance improved when utilizing data from more hospital contacts compared with only data collected at baseline. Conclusions Our findings present a novel, patient‐level, comprehensive ML‐based algorithm for predicting all‐cause mortality in new or worsened heart failure. Its robust performance across phenotypes throughout the longitudinal patient follow‐up suggests its potential in point‐of‐care clinical risk stratification.

Keywords

• Heart failure
• Machine learning
• Mortality prediction
• Risk stratification
• Big data analysis
• Precision medicine