Nomogram for medication nonadherence risk prediction in post-valve surgery patients: a retrospective study

TL;DR

A new tool predicts medication nonadherence in patients after heart valve surgery using factors like warfarin use and education level.

Contribution

A validated nomogram for predicting medication nonadherence in post-valve surgery patients using five key predictors.

Findings

The nomogram achieved a C-index of 0.839 in the training cohort and 0.833 during internal validation.

Calibration plots showed good agreement between predicted and observed probabilities.

Decision curve analysis confirmed clinical usefulness of the nomogram at thresholds between 12% and 68%.

Abstract

To develop and internally validate a nomogram for individualized prediction of post-valvular surgery medication nonadherence risk.

We developed a prediction model in 244 post-valvular surgery patients enrolled between March 2025 and July 2025. Medication adherence was assessed using the Adherence to Refills and Medications Scale (ARMS). Among the 244 included patients, 112 were classified as nonadherent (ARMS score >16). Predictor selection was performed using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable logistic regression for nomogram construction. With 112 outcome events and five predictors in the final model, the events-per-variable ratio was 22.4, supporting sample size adequacy for exploratory prediction model development. Model performance was evaluated by the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA). Internal validation was performed using 1,000 bootstrap resamples in accordance with TRIPOD-oriented reporting principles.

The final nomogram incorporated five key predictors: use of warfarin, children accompany, dosing frequency daily, education level, and distance to hospital. The model demonstrated excellent discrimination, with a C-index of 0.839 (95% CI: 0.808–0.870) in the training cohort, and maintained strong predictive performance during internal validation (C-index = 0.833). Calibration plots indicated good agreement between predicted and observed probabilities. Decision curve analysis showed that the nonadherence nomogram was clinically useful when the threshold was between 12 and 68%. The AUC was found to be 0.817 [95% CI = 0.784–0.845] in the training set.

This validated nomogram incorporating warfarin use, children accompany, dosing frequency daily, education level, and distance to hospital provides a practical tool for individualized prediction of medication nonadherence risk in post-valvular surgery patients.

Figures