Bayesian estimation of the accuracy of ICD-9-CM- and CPT-4-based algorithms to identify cholecystectomy procedures in administrative data without a reference standard

Abstract

Purpose: To estimate the accuracy of two algorithms to identify cholecystectomy procedures using International Classification of Diseases, 9th Edition, Clinical Modification (ICD-9-CM) and Current Procedural Terminology (CPT-4) codes in administrative data.

Methods: Private insurer medical claims for 30 853 patients 18-64 years with an inpatient hospitalization between 2006 and 2010, as indicated by providers/facilities place of service in addition to room and board charges, were cross-classified according to the presence of codes for cholecystectomy. The accuracy of ICD-9-CM- and CPT-4-based algorithms was estimated using a Bayesian latent class model.

Results: The sensitivity and specificity were 0.92 [probability interval (PI): 0.92, 0.92] and 0.99 (PI: 0.97, 0.99) for ICD-9-CM-, and 0.93 (PI: 0.92, 0.93) and 0.99 (PI: 0.97, 0.99) for CPT-4-based algorithms, respectively. The parallel-joint scheme, where positivity of either algorithm was considered a positive outcome, yielded a sensitivity and specificity of 0.99 (PI: 0.99, 0.99) and 0.97 (PI: 0.95, 0.99), respectively.

Conclusions: Both ICD-9-CM- and CPT-4-based algorithms had high sensitivity to identify cholecystectomy procedures in administrative data when used individually and especially in a parallel-joint approach.

Keywords: Bayesian; cholecystectomy; latent class models; no reference standard; pharmacoepidemiology; sensitivity; specificity.