A PRCI-Based Individualized Risk Prediction Model for Potentially Inappropriate Medication in Older Outpatients
In brief
Nearly 3 in 10 older outpatient prescriptions contained an inappropriate medication
Across 131,894 prescriptions from 59 institutions in China, 29% contained at least one potentially inappropriate medication. A model using diagnoses and other risk factors showed moderate ability to distinguish higher-risk prescriptions in internal validation, with an area under the curve of 0.773. External validation is still needed before its risk estimates can guide care more broadly.
- Journal
- Journal of clinical medicine (Q1)
- Published
- 4 October 2026
- Study design
- Cross-sectional study
- Evidence level
- Level 3, Low (CEBM 3b)
- Authors
- Zhaoyan Chen, Congjun Zhou, Yongfei Dong, Cairong Zhu
- PMID
- 42882135
- DOI
- 10.3390/jcm15197675
Why clinicians should know about it
- Picked for Internal Medicine (paper of the day, 19 October 2026): PRCI-based model provides individualized risk prediction for potentially inappropriate medication
Abstract
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.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.