Utilizing Longitudinal Data in Assessing All-Cause Mortality in Patients Hospitalized with Heart Failure

Overview

In collaboration with the Cardiovascular Center Aalst in Belgium, Powerful Medical developed a machine learning algorithm to improve risk stratification in patients hospitalized with new or worsening heart failure. Trained on 2,449 patients and 151,451 exams, the model accurately predicts mortality across multiple time points (AUC-ROC 0.83–0.89), enabling proactive, yet personalized clinical interventions.

Published in: ESC Heart Failure
Published on: June 13, 2022

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

In a cohort of 2,449 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.

Results

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.

Conclusion

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.

Figure 1: ML methodology in utilizing electronic records over time reflecting longitudinal clinical care. Bottom panel shows an example of two irregular time series of exams and endpoints for two different patients. Left panel shows the dimensionality of the preprocessed dataset created from different sources of data gathered in the electronic health records of both patients. In the centre, model training with 101 cross validation splits of training and validation sets. On the right, recalibrated predictions of the PM-Aalst HF system are shown at three different follow-up patient contacts for both example patients.

Figure 2: Receiver operating characteristic curves (ROC) for individual ML models predicting 30 day, 90 day, 180 day, 1 year, and 2 year all-cause mortality. Left panel: training set. Right panel validation set. AUC, area under curve.