Machine-Learning based Prediction Models for Healthcare Outcomes in Patients Participating in Cardiac Rehabilitation: A Systematic Review

Abstract

Purpose: CR has been proven to reduce mortality and morbidity in patients with CVD. ML techniques are increasingly used to predict healthcare outcomes in various fields of medicine including CR. This systemic review aims to perform critical appraisal of existing ML based prognosis predictive model within CR and identify key research gaps in this area. Review methods: A systematic literature search was conducted in Scopus, PubMed, Web of Science and Google Scholar from the inception of each database to 28th January 2024. The data extracted included clinical features, predicted outcomes, model development and validation as well as model performance metrics. Included studies underwent quality assessments using the IJMEDI. Summary: 22 ML-based clinical models from 7 studies across multiple phases of CR were included. Most models were developed using smaller patient cohorts from 41 to 227, with one exception involving 2280 patients. The prediction objectives ranged from patient intention to initiate CR to graduate from outpatient CR along with interval physiological and psychological response to CR. The best-performing ML models reported AUC between 0.82 and 0.91, sensitivity from 0.77 to 0.95, indicating good prediction capabilities. However, none of them underwent calibration or external validation. Most studies raised concerns for bias. Readiness of these models for implement into practice is questionable. External validation of existing models and development of new models with robust methodology based on larger populations and targeting diverse clinical overcomes in CR are needed.