Adaptability of High Dimensional Propensity Score Procedure in the Transition from ICD-9 to ICD-10 in the US Healthcare System

Abstract

Background

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

Data Sharing Statement

Access to data is contingent on establishing the required data agreement with the vendor. The programming code may be requested from the corresponding author.

Disclosure

Almut Winterstein has received funding from the FDA, NIH, PCORI, AHRQ, the Bill and Melinda Gates Foundation, Merck & Co. and the state of Florida. She has received consulting honoraria from Arbor Pharmaceuticals and Genentech. She is a special government employee of the FDA and has served as the Chair of the Drug Safety and Risk Management (DSaRM) Advisory Committee from 2012 to 2018. None is related to this project or poses a conflict of interest. Christian Hampp is employed by Regeneron Pharmaceuticals, Inc., and owns company stock. Regeneron did not provide study funding, does not hold a marketing license for any of the study drugs, and had no role in manuscript development and decision to publish. Amir Sarayani is currently affiliated with Janssen Research & Development LLC. The study conceptualization, conduct, and manuscript writing were part of his doctoral dissertation and completed before his new role. Dr Joshua D Brown reports employment from Pfizer, outside the submitted work. The authors report no other conflicts of interest in this work.

Additional information

Funding

This project did not have any external funding source and was conducted as part of first author’s doctoral dissertation.