Development and Validation of a Risk Score Predicting Medication Non-adherence in Patients with Heart Failure

Abstract

In this study, we aimed to develop a clinically applicable risk score using demographic and clinical data available at hospital admission for decompensated heart failure (HF) to identify patients at high risk of medication non-adherence after discharge. In total, 795 patients with HF were enrolled from a prospective, multicenter cohort study. A logistic regression model identified independent predictors of medication non-adherence leading to hospitalization, and we derived a risk score from the partial regression coefficients of these factors. Hospitalization due to medication non-adherence occurred in 6.4% of patients. Independent predictors included dementia (odds ratio [OR]: 2.79; 95% confidence interval [CI]: 1.38–5.65), male sex (OR: 2.50; 95% CI: 1.30–4.79), current smoking (OR: 2.17; 95% CI: 1.07–4.41), non-assisted living (OR: 2.14; 95% CI: 1.17–3.94), and prior HF hospitalization (OR: 1.91; 95% CI: 1.05–3.46). The predictive model showed good discrimination (area under the receiver operating characteristic curve = 0.704). Patients with higher risk scores (4–6 points) had significantly higher rates of all-cause and cardiovascular mortality over 2 years. In conclusion, this pragmatic, admission-based risk score enables early identification of patients at risk for medication non-adherence and may support targeted interventions to improve long-term HF outcomes. Incorporating this score into routine admission processes may help prioritize adherence support for high-risk patients, guide multidisciplinary care planning, and reduce post-discharge complications in HF management.