Use of administrative data to identify colorectal liver metastasis

Daniel A. Anaya, Natasha S. Becker, Peter Richardson, Neena Susan Abraham

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Background: The ability to identify patients with colorectal cancer (CRC) liver metastasis (LM) using administrative data is unknown. The goals of this study were to evaluate whether administrative data can accurately identify patients with CRCLM and to develop a diagnostic algorithm capable of identifying such patients. Materials and Methods: A retrospective cohort study was conducted to validate the diagnostic and procedural codes found in administrative databases of the Veterans Administration (VA) system. CRC patients evaluated at a major VA center were identified (1997-2008, n = 1671) and classified as having liver-specific ICD-9 and/or CPT codes. The presence of CRCLM was verified by primary chart abstraction in the study sample. Contingency tables were created and the positive predictive value (PPV) for CRCLM was calculated for each candidate administrative code. A multivariate logistic-regression model was used to identify independent predictors (codes) of CRCLM, which were used to develop a diagnostic algorithm. Validity of the algorithm was determined by discrimination (c-statistic) of the model and PPV of the algorithm. Results: Multivariate logistic regression identified ICD-9 diagnosis codes 155.2 (OR 9.7 [95% CI 2.5-38.4]) and 197.7 (84.6 [52.9-135.3]), and procedure code 50.22 (5.9 [1.3-25.5]) as independent predictors of CRCLM diagnosis. The model's discrimination was 0.89. The diagnostic algorithm, defined as the presence of any of these codes, had a PPV of 87%. Conclusions: VA administrative databases reliably identify patients with CRCLM. This diagnostic algorithm is highly predictive of CRCLM diagnosis and can be used for research studies evaluating population-level features of this disease within the VA system.

Original languageEnglish (US)
Pages (from-to)141-146
Number of pages6
JournalJournal of Surgical Research
Volume176
Issue number1
DOIs
StatePublished - Jul 2012
Externally publishedYes

Fingerprint

United States Department of Veterans Affairs
Neoplasm Metastasis
Liver
International Classification of Diseases
Logistic Models
Colorectal Neoplasms
Current Procedural Terminology
Databases
Liver Neoplasms
Cohort Studies
Retrospective Studies
Research
Population

Keywords

  • colorectal cancer
  • database studies
  • diagnostic algorithm
  • liver metastases
  • veterans

ASJC Scopus subject areas

  • Surgery

Cite this

Use of administrative data to identify colorectal liver metastasis. / Anaya, Daniel A.; Becker, Natasha S.; Richardson, Peter; Abraham, Neena Susan.

In: Journal of Surgical Research, Vol. 176, No. 1, 07.2012, p. 141-146.

Research output: Contribution to journalArticle

Anaya, Daniel A. ; Becker, Natasha S. ; Richardson, Peter ; Abraham, Neena Susan. / Use of administrative data to identify colorectal liver metastasis. In: Journal of Surgical Research. 2012 ; Vol. 176, No. 1. pp. 141-146.
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AB - Background: The ability to identify patients with colorectal cancer (CRC) liver metastasis (LM) using administrative data is unknown. The goals of this study were to evaluate whether administrative data can accurately identify patients with CRCLM and to develop a diagnostic algorithm capable of identifying such patients. Materials and Methods: A retrospective cohort study was conducted to validate the diagnostic and procedural codes found in administrative databases of the Veterans Administration (VA) system. CRC patients evaluated at a major VA center were identified (1997-2008, n = 1671) and classified as having liver-specific ICD-9 and/or CPT codes. The presence of CRCLM was verified by primary chart abstraction in the study sample. Contingency tables were created and the positive predictive value (PPV) for CRCLM was calculated for each candidate administrative code. A multivariate logistic-regression model was used to identify independent predictors (codes) of CRCLM, which were used to develop a diagnostic algorithm. Validity of the algorithm was determined by discrimination (c-statistic) of the model and PPV of the algorithm. Results: Multivariate logistic regression identified ICD-9 diagnosis codes 155.2 (OR 9.7 [95% CI 2.5-38.4]) and 197.7 (84.6 [52.9-135.3]), and procedure code 50.22 (5.9 [1.3-25.5]) as independent predictors of CRCLM diagnosis. The model's discrimination was 0.89. The diagnostic algorithm, defined as the presence of any of these codes, had a PPV of 87%. Conclusions: VA administrative databases reliably identify patients with CRCLM. This diagnostic algorithm is highly predictive of CRCLM diagnosis and can be used for research studies evaluating population-level features of this disease within the VA system.

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