Use of clinical classifications software to address ICD coding transition in large healthcare databases analyzed via high‐dimensional propensity scores

Abstract

AbstractPurposeThe EUPAS26595 study characterized the rate of acute renal failure (ARF) in patients exposed to levetiracetam versus other antiepileptic drugs using healthcare claims data and a high‐dimensional propensity score (hd‐PS) for confounding adjustment. The data contained several coding systems by design and an update in International Classification of Diseases (ICD) coding dictionary. Such coding heterogeneity can affect the performance of hd‐PS, and manually coding harmonization is not feasible. Our objective was to explore the impact of code aggregation via Clinical Classifications Software (CCS) on the analysis of a large claims‐based database using hd‐PS.MethodsPatients with epilepsy, who were new‐users of an antiepileptic drug, were identified from the IBM® MarketScan® Research Databases. We used CCS categories to harmonize coding and compared the results with other alternatives. Incidence rate ratios (IRRs) were computed using modified Poisson regression model with a robust variance estimator.ResultsFor January 2008–October 2015 (before ICD update), 34 833 eligible patients initiated levetiracetam and 52 649 initiated a comparator drug; IRR (95% CI) for ARF for the hd‐PS analysis was 1.34 (0.72–2.50) without CCS categories and 1.30 (0.71–2.39) with CCS categories. For January 2008–December 2017 (including ICD coding change), 45 672 eligible patients initiated levetiracetam and 64 664 initiated a comparator drug; IRR (95% CI) for the hd‐PS analysis was 1.34 (0.78–2.29) without CCS categories and 1.37 (0.80–2.34) with CCS categories.ConclusionsUsing single‐level CCS categories to overcome differences in coding provides consistent results and can be used in studies that use large claims data and hd‐PS for adjustment.