Cited by 212Medical Coding and Health InformationLiver Disease and TransplantationElectronic Health Records Systems

Use of administrative claims data for identifying patients with cirrhosis

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Nehra, Mahendra S., Ma, Ying, Clark, Christopher, Amarasingham, Ruben, Rockey, Don C., Singal, Amit G.·2013·Journal of Clinical Gastroenterology

Abstract

BACKGROUND: Administrative data are used in clinical research, but the validity of ICD-9 codes to identify cirrhotic patients has not been well established. GOALS: To determine the diagnostic accuracy of ICD-9 codes for cirrhosis in clinical practice. STUDY: We conducted a retrospective cohort study of patients from a safety-net hospital between 2008 and 2011. Patients were initially identified using ICD-9 codes for cirrhosis or a resultant complication. The gold-standard for diagnosis of cirrhosis was histology and/or imaging based on medical record review. Sensitivity, specificity, positive predictive values, and negative predictive values for each ICD-9 code were calculated. Diagnostic accuracy was assessed by the c-statistic using receiver operator characteristic curve analysis. RESULTS: We identified 2893 patients with an ICD-9 code for cirrhosis, of whom 50.2% had 1 ICD-9 code, 20.3% had 2 different codes, and 29.5% had 3 or more codes. Cirrhosis was confirmed in 44.0% of patients with 1 ICD-9 code, 82.6% with 2 codes, and 95.7% of those with at least 3 codes. Ascites had a significantly lower positive predictive values for cirrhosis than other ICD-9 codes (P<0.001). The optimal combination of ICD-9 codes to identify cirrhotic patients included all codes except that of ascites, with a c-statistic of 0.71 in our derivation cohort. The sensitivity of this combination was confirmed to be 98% in a validation cohort of 285 patients with known cirrhosis. CONCLUSIONS: Administrative data can identify patients with cirrhosis with high accuracy, although ascites has a significantly lower positive predictive value than other ICD-9 codes.

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  • Patients were identified by a validated EMR case identification algorithm based on ICD-9 or ICD-10 codes for cirrhosis or cirrhosis complications.

    “Patients were identified by a validated EMR case identification algorithm based on International Classification of Diseases–Ninth Revision (ICD-9) or International Classification of Diseases–Tenth Revision (ICD-10) codes for cirrhosis (571.2 or 571.5; K70.30 or K74.6) or cirrhosis complications”

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Authors
Nehra, M. S., Ma, Y., Clark, C., Amarasingham, R., Rockey, D. C., & Singal, A. G.
Affiliations
Parkland Health & Hospital System (US)
Corresponding author
  • Nehra, Mahendra S.
  • Rockey, Don C.
  • Singal, Amit G.
Venue
Journal of Clinical Gastroenterology
Published
2013-05
Volume
47
Issue
5
Pages
e50-e54
ISSN
0192-0790 (ISSN-L)
OpenAlex
W2061784149
Cited by
211
References
15
Citations by year
2026: 11 · 2025: 17 · 2024: 17 · 2023: 21 · 2022: 13 · 2021: 22 · 2020: 31 · 2019: 20 · 2018: 17 · 2017: 8 · 2016: 16 · 2015: 9 · 2014: 5 · 2013: 3 · 2012: 1
FWCI
9.33
Type
journal article
Language
en
Open access
Locations
  • Journal of Clinical Gastroenterology (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (2kl2tr000453-06, UL1TR000451)
  • National Center for Research Resources (KL2RR024983)
  • National Center for Advancing Translational Sciences (KL2TR000453.)
Topics
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Health Sciences › Medicine › Hepatology › Liver Disease and Transplantation
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
liver cirrhosis, ICD-9 codes, administrative claims data, diagnostic accuracy, ascites, code combinations, safety-net hospitals, positive predictive value, sensitivity and specificity
MeSH terms
Female, Humans, Liver Cirrhosis, Male, Middle Aged, Predictive Value of Tests, Retrospective Studies, ROC Curve, Sensitivity and Specificity, Cohort Studies, International Classification of Diseases, Electronic Health Records, Clinical Coding

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