A machine learning model for predicting six-month rehospitalization in heart failure patients with multiple comorbidities

Abstract

This research (retrospective cohort study) investigates how tailored discharge instructions for heart failure (HF) patients with multiple health conditions can lower their emergency or urgent care readmission risk. Utilizing an AI/ML approach, this study develops a model that classifies HF patients' readmission risk at discharge by evaluating approximately 20 parameters across five categories, including comorbidities (e.g., liver disease, diabetes), patient demographics (e.g., age, BMI), clinical data (e.g., lipid and blood levels), diagnostic parameters (e.g., ejection fraction, HF type), discharge details (e.g., discharge destination and location), and medication history. The AI model identifies high-risk patients, who are then prescribed a more focused wellness and rehabilitation plan, including in-person visits and regular monitoring. Lower-risk patients are given less rigorous plans, involving virtual check-ins and self-management tools. In contrast, high-risk patients can undergo intensive wellness plans, involving hospital/clinical visits and tracking by wellness staff. The study's approach of customizing post-discharge care based on re-admission risk aims to reduce re-admissions and improve HF patient outcomes. This research employs key ML models on the PhysioNet dataset, a collection of hospital admissions and mortality records for HF patients from Zigong Fourth People's Hospital in Sichuan, China (2016-2019). To evaluate the broader applicability of the developed models, their results on the PhysioNet dataset are benchmarked against the HCUP Maryland SID (2014), a secondary dataset of HF patients. The ML models exhibited strong performance, achieving efficiencies exceeding 76% and specificities reaching 97% in identifying HF patients with a higher risk of readmission and ranking them according to their risk level. The study's approach to customizing post-discharge care based on readmission risk aims to reduce readmissions and improve HF patient outcomes. Thus, this presents a comprehensive and reliable strategy for HF management and wellness.