Prognostic effect of polypharmacy in ambulatory patients with heart failure: a machine learning-based analysis

Managing heart failure often involves polypharmacy, reflecting the challenge of balancing multiple treatments to achieve the best outcomes for patients. We aimed to investigate the effect of polypharmacy on survival in ambulatory patients with heart failure and identify key predictors of survival.

We conducted a secondary analysis of data from 1375 ambulatory patients with heart failure managed at a heart failure clinic in Spain between 2006 and 2016. Patients were stratified into low polypharmacy (< 5 cardiovascular medications) and high polypharmacy groups (≥ 5 cardiovascular medications). Survival was evaluated using Kaplan-Meier curves, log-rank tests, and a multivariable random forest Cox regression model adjusting for demographic and clinical confounders using propensity scores.

There was a significant relationship between the number of medications used and survival (log-rank test, P = 0.017) with the most pronounced risk observed in those with seven or more medications. Additionally, patients in the high polypharmacy group showed significantly reduced survival probabilities than those in the low polypharmacy group (P = 0.016). The top important predictors of survival were age, N-terminal pro B-type natriuretic peptide, New York Heart Association functional class, heart failure duration, sacubitril-valsartan, β-blockers, and angiotensin-converting enzyme inhibitors or angiotensin receptor blockers (all P < 0.001). Multivariable analysis (C-index = 0.804) showed that high polypharmacy was independently associated with reduced survival (P < 0.001).

High polypharmacy is associated with reduced long-term survival in patients with heart failure. This highlights the need for personalized prescribing strategies to minimize the risks associated with polypharmacy while maximizing therapeutic benefits.