TY - JOUR
T1 - Validity of an automated algorithm to identify cirrhosis using electronic health records in patients with primary biliary cholangitis
AU - FOLD Investigators
AU - Lu, Mei
AU - Bowlus, Christopher L.
AU - Lindor, Keith
AU - Rodriguez-Watson, Carla V.
AU - Romanelli, Robert J.
AU - Haller, Irina V.
AU - Anderson, Heather
AU - Vanwormer, Jeffrey J.
AU - Boscarino, Joseph A.
AU - Schmidt, Mark A.
AU - Daida, Yihe G.
AU - Sahota, Amandeep
AU - Vincent, Jennifer
AU - Li, Jia
AU - Trudeau, Sheri
AU - Rupp, Loralee B.
AU - Gordon, Stuart C.
N1 - Funding Information:
The FOLD Consortium has previously received funding from Intercept Pharmaceuticals Inc.
Funding Information:
Stuart C. Gordon receives grant/research support from AbbV ie Pharmaceuticals, Conatus, CymaBay , Eiger Pharmaceuticals, Eli Lilly , Genfit, Gilead Sciences, GlaxoSmithKline, Intercept Pharmaceuticals, Merck, and V iking Therapeutics. Mei Lu, Joseph A. Boscarino, Mark A. Schmidt, Y ihe G. Daida, Jia Li, Loralee B. Rupp, and Sheri T rudeau receive research grant support from Gilead Sciences and Intercept Pharmaceuticals. Carla V . Rodriguez-W atson owns stock in Gilead (<$5000). Heather Anderson receives grant/research support from Intercept Pharmaceuticals. Jeffrey J. V anW ormer receives grant/research support from Retrophin. Christopher L. Bowlus receives grant/research support from AbbV ie Pharmaceuticals, Bristol-Myers-Squibb, CymaBay , Gilead Biosciences, GlaxoSmithKline, Intercept Pharmaceuticals, Merck, Mirum, Shire Pharmaceuticals, T akeda Pharmaceuticals, T ARGET Pharmasolutions, and has served as an advisor for Bristol-Myers-Squibb, Gilead Biosciences, Intercept Pharmaceuticals, and T akeda. Keith Lindor is a consultant/advisor for Biopharma and has served as an ad hoc advisor for HighT ide, T akeda, Shire, and Intercept Pharmaceuticals. He sits on a Data Safety Monitoring Board for T akeda. Robert J. Romanelli receives received grant/ research support from Pfizer Inc. and Janssen Scientific Affairs. The authors report no other conflicts of interest in this work.
PY - 2020
Y1 - 2020
N2 - Background: Biopsy remains the gold standard for determining fibrosis stage in patients with primary biliary cholangitis (PBC), but it is unavailable for most patients. We used data from the 11 US health systems in the FibrOtic Liver Disease Consortium to explore a combination of biochemical markers and electronic health record (EHR)-based diagnosis/ procedure codes (DPCs) to identify the presence of cirrhosis in PBC patients. Methods: Histological fibrosis staging data were obtained from liver biopsies. Variables considered for the model included demographics (age, gender, race, ethnicity), total bilirubin, alkaline phosphatase, albumin, aspartate aminotransferase (AST) to platelet ratio index (APRI), Fibrosis 4 (FIB4) index, AST to alanine aminotransferase (ALT) ratio, and >100 DPCs associated with cirrhosis/decompensated cirrhosis, categorized into ten clusters. Using least absolute shrinkage and selection operator regression (LASSO), we derived and validated cutoffs for identifying cirrhosis. Results: Among 4328 PBC patients, 1350 (32%) had biopsy data; 121 (9%) were staged F4 (cirrhosis). DPC clusters (including codes related to cirrhosis and hepatocellular carcinoma diagnoses/procedures), Hispanic ethnicity, ALP, AST/ALT ratio, and total bilirubin were retained in the final model (AUROC=0.86 and 0.83 on learning and testing data, respectively); this model with two cutoffs divided patients into three categories (no cirrhosis, indeterminate, and cirrhosis) with specificities of 81.8% (for no cirrhosis) and 80.3% (for cirrhosis). A model excluding DPCs retained ALP, AST/ALT ratio, total bilirubin, Hispanic ethnicity, and gender (AUROC=0.81 and 0.78 on learning and testing data, respectively). Conclusion: An algorithm using laboratory results and DPCs can categorize a majority of PBC patients as cirrhotic or noncirrhotic with high accuracy (with a small remaining group of patients’ cirrhosis status indeterminate). In the absence of biopsy data, this EHR-based model can be used to identify cirrhosis in cohorts of PBC patients for research and/or clinical follow-up.
AB - Background: Biopsy remains the gold standard for determining fibrosis stage in patients with primary biliary cholangitis (PBC), but it is unavailable for most patients. We used data from the 11 US health systems in the FibrOtic Liver Disease Consortium to explore a combination of biochemical markers and electronic health record (EHR)-based diagnosis/ procedure codes (DPCs) to identify the presence of cirrhosis in PBC patients. Methods: Histological fibrosis staging data were obtained from liver biopsies. Variables considered for the model included demographics (age, gender, race, ethnicity), total bilirubin, alkaline phosphatase, albumin, aspartate aminotransferase (AST) to platelet ratio index (APRI), Fibrosis 4 (FIB4) index, AST to alanine aminotransferase (ALT) ratio, and >100 DPCs associated with cirrhosis/decompensated cirrhosis, categorized into ten clusters. Using least absolute shrinkage and selection operator regression (LASSO), we derived and validated cutoffs for identifying cirrhosis. Results: Among 4328 PBC patients, 1350 (32%) had biopsy data; 121 (9%) were staged F4 (cirrhosis). DPC clusters (including codes related to cirrhosis and hepatocellular carcinoma diagnoses/procedures), Hispanic ethnicity, ALP, AST/ALT ratio, and total bilirubin were retained in the final model (AUROC=0.86 and 0.83 on learning and testing data, respectively); this model with two cutoffs divided patients into three categories (no cirrhosis, indeterminate, and cirrhosis) with specificities of 81.8% (for no cirrhosis) and 80.3% (for cirrhosis). A model excluding DPCs retained ALP, AST/ALT ratio, total bilirubin, Hispanic ethnicity, and gender (AUROC=0.81 and 0.78 on learning and testing data, respectively). Conclusion: An algorithm using laboratory results and DPCs can categorize a majority of PBC patients as cirrhotic or noncirrhotic with high accuracy (with a small remaining group of patients’ cirrhosis status indeterminate). In the absence of biopsy data, this EHR-based model can be used to identify cirrhosis in cohorts of PBC patients for research and/or clinical follow-up.
KW - Cholangitis
KW - Decompensated cirrhosis
KW - Ethnicity
KW - Gender
KW - Primary biliary cirrhosis
KW - Race/gender/ethnicity
KW - UCDA
KW - Ursodeoxycholic acid
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UR - http://www.scopus.com/inward/citedby.url?scp=85096065458&partnerID=8YFLogxK
U2 - 10.2147/CLEP.S262558
DO - 10.2147/CLEP.S262558
M3 - Article
AN - SCOPUS:85096065458
VL - 12
SP - 1261
EP - 1267
JO - Clinical Epidemiology
JF - Clinical Epidemiology
SN - 1179-1349
ER -