Back to Journals » Clinical Epidemiology » Volume 9

Validity of an automated algorithm using diagnosis and procedure codes to identify decompensated cirrhosis using electronic health records

Authors Lu M, Chacra W, Rabin D, Rupp LB, Trudeau S, Li J, Gordon SC

Received 4 March 2017

Accepted for publication 22 May 2017

Published 12 July 2017 Volume 2017:9 Pages 369—376


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 4

Editor who approved publication: Professor Henrik Toft Sørensen

Mei Lu,1 Wadih Chacra,2 David Rabin,3 Loralee B Rupp,4 Sheri Trudeau,1 Jia Li,1 Stuart C Gordon5

On behalf of the Chronic Hepatitis Cohort Study (CHeCS) Investigators

1Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, USA; 2Division of Gastroenterology and Hepatology, University of Illinois College of Medicine, Chicago, IL, USA; 3Atlanta Gastroenterology Associates, Atlanta, GA, USA; 4Center for Health Policy & Health Services Research, Henry Ford Health System, Detroit MI, USA; 5Division of Gastroenterology and Hepatology, Henry Ford Health System, Detroit, MI, USA

Abstract: Viral hepatitis-induced cirrhosis can progress to decompensated cirrhosis. Clinical decompensation represents a milestone event for the patient with cirrhosis, yet there remains uncertainty regarding precisely how to define this important phenomenon. With the development of broader treatment options for cirrhotic hepatitis patients, efficient identification of liver status before evolving to decompensated cirrhosis could be life-saving, but research on the topic has been limited by inconsistencies across studies, populations, and case-confirmation methods. We sought to determine whether diagnosis/procedure codes drawn from electronic health records (EHRs) could be used to identify patients with decompensated cirrhosis. In our first step, chart review was used to determine liver status (compensated cirrhosis, decompensated cirrhosis, noncirrhotic) in patients from the Chronic Hepatitis Cohort Study. Next, a hybrid approach between Least Absolute Shrinkage and Selection Operator regression and Classification Regression Trees models was used to optimize EHR-based identification of decompensated cirrhosis, based on 41 diagnosis and procedure codes. These models were validated using tenfold cross-validation; method accuracy was evaluated by positive predictive values (PPVs) and area under receiver operating characteristic (AUROC) curves. Among 296 patients (23 with hepatitis B, 268 with hepatitis C, and 5 co-infected) with a 2:1 ratio of biopsy-confirmed cirrhosis to noncirrhosis, chart review identified 127 cases of decompensated cirrhosis (Kappa=0.88). The algorithm of five liver-related conditions—liver transplant, hepatocellular carcinoma, esophageal varices complications/procedures, ascites, and ­cirrhosis—yielded a PPV of 85% and an AUROC of 92%. A hierarchical subset of three conditions (hepatocellular carcinoma, ascites, and esophageal varices) demonstrated a PPV of 81% and an AUROC of 86%. Given the excellent predictive ability of our model, this EHR-based automated algorithm may be used to successfully identify patients with decompensated cirrhosis. This algorithm may contribute to timely identification and treatment of viral hepatitis patients who have progressed to decompensated cirrhosis.

Keywords: chronic viral hepatitis, hepatitis B, HBV, hepatitis C, HCV, classification and regression tree modeling, CART modeling

Creative Commons License This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

Download Article [PDF]  View Full Text [HTML][Machine readable]