Development and validation of a nomogram to predict the risk of potentially inappropriate medication use in older lung cancer outpatients with multimorbidity.

BACKGROUND: At present, there is no predictive model that can predict the prevalence of potentially inappropriate medication (PIM) use in older lung cancer outpatients.

RESEARCH DESIGN AND METHODS: We measured PIM by the 2019 Beers criteria. Significant factors were identified to develop the nomogram using logistic regression. We validated the nomogram internally and externally in two cohorts. The discrimination, calibration, and clinical practicability of the nomogram were verified using receiver operating characteristic (ROC) curve analysis, Hosmer-Lemeshow test, and decision curve analysis (DCA), respectively.

RESULTS: A total of 3300 older lung cancer outpatients were divided into a training cohort (n=1718) and two validation cohorts, including an internal validation cohort (n=739) and an external validation cohort (n=843). A nomogram for predicting PIM use patients was developed using six significant factors. ROC curve analysis showed that the area under the curve was 0.835 in the training cohort and 0.810 and 0.826 in the internal validation and external validation cohorts, respectively. The Hosmer‒Lemeshow test yielded P=0.180, 0.779 and 0.069, respectively. The nomogram demonstrated a high net benefit in DCA.

CONCLUSIONS: The nomogram could be a convenient, intuitive, and personalized clinical tool for assessing the risk of PIM in older lung cancer outpatients.