Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2014 Apr;52(4):e21-9.
doi: 10.1097/MLR.0b013e31824ebdf5.

Determination of Colonoscopy Indication From Administrative Claims Data

Affiliations
Free PMC article

Determination of Colonoscopy Indication From Administrative Claims Data

Cynthia W Ko et al. Med Care. .
Free PMC article

Abstract

Background: Colonoscopy outcomes, such as polyp detection or complication rates, may differ by procedure indication.

Objectives: To develop methods to classify colonoscopy indications from administrative data, facilitating study of colonoscopy quality and outcomes.

Research design: We linked 14,844 colonoscopy reports from the Clinical Outcomes Research Initiative, a national repository of endoscopic reports, to the corresponding Medicare Carrier and Outpatient File claims. Colonoscopy indication was determined from the procedure reports. We developed algorithms using classification and regression trees and linear discriminant analysis (LDA) to classify colonoscopy indication. Predictor variables included ICD-9CM and CPT/HCPCS codes present on the colonoscopy claim or in the 12 months prior, patient demographics, and site of colonoscopy service. Algorithms were developed on a training set of 7515 procedures, then validated using a test set of 7329 procedures.

Results: Sensitivity was lowest for identifying average-risk screening colonoscopies, varying between 55% and 86% for the different algorithms, but specificity for this indication was consistently over 95%. Sensitivity for diagnostic colonoscopy varied between 77% and 89%, with specificity between 55% and 87%. Algorithms with classification and regression trees with 7 variables or LDA with 10 variables had similar overall accuracy, and generally lower accuracy than the algorithm using LDA with 30 variables.

Conclusions: Algorithms using Medicare claims data have moderate sensitivity and specificity for colonoscopy indication, and will be useful for studying colonoscopy quality in this population. Further validation may be needed before use in alternative populations.

Figures

Figure 1
Figure 1
Selection and matching of CORI records and Medicare claims
Figure 2
Figure 2
Classification and regression tree algorithm for 4-level grouping* of colonoscopy indication *Four-level classification = diagnostic, average risk screening, surveillance, and high risk. The number of prior ICD codes is defined as the total count of prior ICD-9CM codes present from the list of codes in the second column of Table 2. Algorithm developed in training set of 7,515 colonoscopies, and validated in test set of 7,329 colonoscopies.

Similar articles

See all similar articles

Cited by 19 articles

See all "Cited by" articles

Publication types

MeSH terms

Feedback