Drug‐related readmissions in older hospitalized adults: External validation and updating of OPERAM DRA prediction tool

Abstract

BackgroundDrug‐related readmissions (DRAs) are defined as rehospitalizations with an adverse drug event as their main or significant contributory cause. DRAs represent a major adverse health burden for older patients. A prediction model which identified older hospitalized patients at high risk of a DRA <1 year was previously developed using the OPERAM trial cohort, a European cluster randomized controlled trial including older hospitalized patients with multimorbidity and polypharmacy. This study has performed external validation and updated the prediction model consequently.MethodsThe MedBridge trial cohort (a multicenter cluster randomized crossover trial performed in Sweden) was used as a validation cohort. It consisted of 2516 hospitalized patients aged ≥65 years. Model performance was assessed by: (1) discriminative power, assessed by the C‐statistic with a 95% confidence interval (CI); (2) calibration, assessed by visual examination of the calibration plot and use of the Hosmer–Lemeshow goodness‐of‐fit test; and (3) overall accuracy, assessed by the scaled Brier score. Several updating methods were carried out to improve model performance.ResultsIn total, 2516 older patients were included in the validation cohort, of whom 582 (23.1%) experienced a DRA <1 year. In the validation cohort, the original model showed a good overall accuracy (scaled Brier score 0.03), but discrimination was moderate (C‐statistic 0.62 [95% CI 0.59–0.64]), and calibration showed underestimation of risks. In the final updated model, the predictor “cirrhosis with portal hypertension” was removed and “polypharmacy” was added. This improved the model's discriminative capability to a C‐statistic of 0.64 (95% CI 0.59–0.70) and enhanced calibration plots. Overall accuracy remained good.ConclusionsThe updated OPERAM DRA prediction model may be a useful tool in clinical practice to estimate the risk of DRAs in older hospitalized patients subsequent to discharge. Our efforts lay the groundwork for the future development of models with even better performance.