Use of administrative claims data for identifying patients with cirrhosis
Open accessAbstract
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”
From Depression and anxiety are common among patients with cirrhosis (2022) · cites this paper as [16]
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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)
- PMC
- PMC3556340
- 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
- green
- www.ncbi.nlm.nih.gov
- 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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