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.
OBJECTIVE
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.
Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
S Reza
http://orcid.org/0000-0002-1099-9175
SR
Jafarzadeh
Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
David K
DK
Warren
Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
Katelin B
KB
Nickel
HealthCore Inc., Wilmington, DE, USA.
Anna E
AE
Wallace
Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
Victoria J
VJ
Fraser
Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.|Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA.
Margaret A
MA
Olsen
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D002763:Cholecystectomy / Q000145:classification* / Q000706:statistics & numerical data; D039702:Current Procedural Terminology; D016208:Databases, Factual; D006801:Humans; D007344:Insurance Claim Reporting / Q000706:statistics & numerical data*; D038801:International Classification of Diseases / Q000592:standards* / Q000706:statistics & numerical data; D008875:Middle Aged; D015233:Models, Statistical; D012680:Sensitivity and Specificity; D055815:Young Adult
Bayesian; cholecystectomy; latent class models; no reference standard; pharmacoepidemiology; sensitivity; specificity