A PRCI-Based Individualized Risk Prediction Model for Potentially Inappropriate Medication in Older Outpatients.

Objectives

Potentially inappropriate medication (PIM) use remains highly prevalent among older outpatients, yet individualized risk prediction tools are limited. This study aimed to develop and validate a PIM risk prediction model incorporating a novel disease-weighted index.

Methods

A multicenter cross-sectional study was conducted using 131,894 outpatient prescriptions from 59 medical institutions in China. Diagnoses were mapped to ICD-10 codes. A PIM Risk-Related Comorbidity Index (PRCI) was developed using multivariable logistic regression and coefficient-weighted scoring. A prediction model was constructed and internally validated using a 6:4 split-sample approach. Discrimination, calibration, and clinical utility were assessed using ROC curves, calibration plots, and decision curve analysis.

Results

The proportion of prescriptions containing at least one PIM was 29.0%. Polypharmacy showed a strong dose-response relationship with PIM risk. The PRCI was independently associated with PIM (OR = 1.07 per unit increase). The final model demonstrated good discrimination (AUC = 0.775 in training and 0.773 in validation), satisfactory calibration, and favorable clinical utility. A cutoff probability of 0.304 stratified prescriptions into high- and low-risk groups.

Conclusions

The PRCI-based model provides an individualized and clinically applicable tool for identifying older outpatient prescriptions at high risk of containing PIMs, supporting proactive medication safety management.