Clinical Epidemiology (May 2023)
Adaptability of High Dimensional Propensity Score Procedure in the Transition from ICD-9 to ICD-10 in the US Healthcare System
Abstract
Amir Sarayani,1,2 Joshua D Brown,1,2 Christian Hampp,1,3 William T Donahoo,4,5 Almut G Winterstein1,2 1Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA; 2Center for Drug Safety and Evaluation, University of Florida, Gainesville, FL, USA; 3Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA; 4Division of Endocrinology, Diabetes, & Metabolism, College of Medicine, University of Florida, Gainesville, FL, USA; 5Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USACorrespondence: Amir Sarayani, University of Florida College of Pharmacy, 1225 Center Drive, HPNP Bldg, Room 3334, Gainesville, FL, 32610, USA, Email [email protected]; [email protected]: High-Dimensional Propensity Score procedure (HDPS) is a data-driven approach to assist control for confounding in pharmacoepidemiologic research. The transition to the International Classification of Disease (ICD-9/10) in the US health system may pose uncertainty in applying the HDPS procedure.Methods: We assembled a base cohort of patients in MarketScan® Commercial Claims Database who had newly initiated celecoxib or traditional NSAIDs to compare gastrointestinal bleeding risk. We then created bootstrapped hypothetical cohorts from the base cohort with predefined patient selection patterns from the ICD eras. Three strategies for HDPS deployment were tested: 1) split the cohort by ICD era, deploy HDPS twice, and pool the relative risks (pooled RR), 2) consider codes from each ICD era as a separate data dimension and deploy HDPS in the entire cohort (data dimensions) and 3) map ICD codes from both eras to Clinical Classifications Software (CCS) concepts before deploying HDPS in the entire cohort (CCS mapping). We calculated percent bias and root-mean-squared error to compare the strategies.Results: A similar bias reduction was observed in cohorts where patient selection pattern from each ICD era was comparable between the exposure groups. In the presence of considerable disparity in patient selection, we observed a bimodal distribution of propensity scores in the data dimensions strategy, indicating instrument-like covariates. Moreover, the CCS mapping strategy resulted in at least 30% less bias than pooled RR and data dimensions strategies (RMSE: 0.14, 0.19, 0.21, respectively) in this scenario.Conclusion: Mapping ICD codes to a stable terminology like CCS serves as a helpful strategy to reduce residual bias when deploying HDPS in pharmacoepidemiologic studies spanning both ICD eras.Keywords: propensity score, confounding, real-world evidence, comparative effectiveness research, ICD-10, ICD-9, HDPS algorithm
