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
Published Date:Sep 09 2015
Source:Pharmacoepidemiol Drug Saf. 25(3):263-268.
Current Procedural Terminology
Insurance Claim Reporting
International Classification Of Diseases
Latent Class Models
No Reference Standard
Sensitivity And Specificity
Pubmed Central ID:PMC4775358
Funding:R01 HS019713/HS/AHRQ HHS/United States
U54 CK000162/CK/NCEZID CDC HHS/United States
FOA# CK11-001/CK/NCEZID CDC HHS/United States
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.
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.
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.
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.
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