Development of an Algorithm to Identify Cases of Nonalcoholic Steatohepatitis Cirrhosis in the Electronic Health Record
Background and Aims Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis...
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| Published in | Digestive diseases and sciences Vol. 66; no. 5; pp. 1452 - 1460 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.05.2021
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0163-2116 1573-2568 1573-2568 |
| DOI | 10.1007/s10620-020-06388-y |
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| Abstract | Background and Aims
Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis in the EHR.
Methods
We used Clinical Query 2, a search tool at Beth Israel Deaconess Medical Center, to create a pool of potential NASH cirrhosis cases (
n
= 5415). We created a training set of 300 randomly selected patients for chart review to confirm cases of NASH cirrhosis. Test characteristics of different algorithms, consisting of diagnosis codes, laboratory values, anthropomorphic measurements, and medication records, were calculated. The algorithms with the highest positive predictive value (PPV) and the highest
F
score with a PPV ≥ 80% were selected for internal validation using a separate random set of 100 patients from the potential NASH cirrhosis pool. These were then externally validated in another random set of 100 individuals using the research patient data registry tool at Massachusetts General Hospital.
Results
The algorithm with the highest PPV of 100% on internal validation and 92% on external validation consisted of ≥ 3 counts of “cirrhosis, no mention of alcohol” (571.5, K74.6) and ≥ 3 counts of “nonalcoholic fatty liver” (571.8–571.9, K75.81, K76.0) codes in the absence of any diagnosis codes for other common causes of chronic liver disease.
Conclusions
We developed and validated an EHR algorithm using diagnosis codes that accurately identifies patients with NASH cirrhosis. |
|---|---|
| AbstractList | Background and Aims Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis in the EHR. Methods We used Clinical Query 2, a search tool at Beth Israel Deaconess Medical Center, to create a pool of potential NASH cirrhosis cases (n = 5415). We created a training set of 300 randomly selected patients for chart review to confirm cases of NASH cirrhosis. Test characteristics of different algorithms, consisting of diagnosis codes, laboratory values, anthropomorphic measurements, and medication records, were calculated. The algorithms with the highest positive predictive value (PPV) and the highest F score with a PPV [greater than or equal to] 80% were selected for internal validation using a separate random set of 100 patients from the potential NASH cirrhosis pool. These were then externally validated in another random set of 100 individuals using the research patient data registry tool at Massachusetts General Hospital. Results The algorithm with the highest PPV of 100% on internal validation and 92% on external validation consisted of [greater than or equal to] 3 counts of "cirrhosis, no mention of alcohol" (571.5, K74.6) and [greater than or equal to] 3 counts of "nonalcoholic fatty liver" (571.8-571.9, K75.81, K76.0) codes in the absence of any diagnosis codes for other common causes of chronic liver disease. Conclusions We developed and validated an EHR algorithm using diagnosis codes that accurately identifies patients with NASH cirrhosis. Background and AimsCurrent genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis in the EHR.MethodsWe used Clinical Query 2, a search tool at Beth Israel Deaconess Medical Center, to create a pool of potential NASH cirrhosis cases (n = 5415). We created a training set of 300 randomly selected patients for chart review to confirm cases of NASH cirrhosis. Test characteristics of different algorithms, consisting of diagnosis codes, laboratory values, anthropomorphic measurements, and medication records, were calculated. The algorithms with the highest positive predictive value (PPV) and the highest F score with a PPV ≥ 80% were selected for internal validation using a separate random set of 100 patients from the potential NASH cirrhosis pool. These were then externally validated in another random set of 100 individuals using the research patient data registry tool at Massachusetts General Hospital.ResultsThe algorithm with the highest PPV of 100% on internal validation and 92% on external validation consisted of ≥ 3 counts of “cirrhosis, no mention of alcohol” (571.5, K74.6) and ≥ 3 counts of “nonalcoholic fatty liver” (571.8–571.9, K75.81, K76.0) codes in the absence of any diagnosis codes for other common causes of chronic liver disease.ConclusionsWe developed and validated an EHR algorithm using diagnosis codes that accurately identifies patients with NASH cirrhosis. Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis in the EHR. We used Clinical Query 2, a search tool at Beth Israel Deaconess Medical Center, to create a pool of potential NASH cirrhosis cases (n = 5415). We created a training set of 300 randomly selected patients for chart review to confirm cases of NASH cirrhosis. Test characteristics of different algorithms, consisting of diagnosis codes, laboratory values, anthropomorphic measurements, and medication records, were calculated. The algorithms with the highest positive predictive value (PPV) and the highest F score with a PPV [greater than or equal to] 80% were selected for internal validation using a separate random set of 100 patients from the potential NASH cirrhosis pool. These were then externally validated in another random set of 100 individuals using the research patient data registry tool at Massachusetts General Hospital. The algorithm with the highest PPV of 100% on internal validation and 92% on external validation consisted of [greater than or equal to] 3 counts of "cirrhosis, no mention of alcohol" (571.5, K74.6) and [greater than or equal to] 3 counts of "nonalcoholic fatty liver" (571.8-571.9, K75.81, K76.0) codes in the absence of any diagnosis codes for other common causes of chronic liver disease. We developed and validated an EHR algorithm using diagnosis codes that accurately identifies patients with NASH cirrhosis. Background and Aims Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis in the EHR. Methods We used Clinical Query 2, a search tool at Beth Israel Deaconess Medical Center, to create a pool of potential NASH cirrhosis cases ( n = 5415). We created a training set of 300 randomly selected patients for chart review to confirm cases of NASH cirrhosis. Test characteristics of different algorithms, consisting of diagnosis codes, laboratory values, anthropomorphic measurements, and medication records, were calculated. The algorithms with the highest positive predictive value (PPV) and the highest F score with a PPV ≥ 80% were selected for internal validation using a separate random set of 100 patients from the potential NASH cirrhosis pool. These were then externally validated in another random set of 100 individuals using the research patient data registry tool at Massachusetts General Hospital. Results The algorithm with the highest PPV of 100% on internal validation and 92% on external validation consisted of ≥ 3 counts of “cirrhosis, no mention of alcohol” (571.5, K74.6) and ≥ 3 counts of “nonalcoholic fatty liver” (571.8–571.9, K75.81, K76.0) codes in the absence of any diagnosis codes for other common causes of chronic liver disease. Conclusions We developed and validated an EHR algorithm using diagnosis codes that accurately identifies patients with NASH cirrhosis. Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis in the EHR. We used Clinical Query 2, a search tool at Beth Israel Deaconess Medical Center, to create a pool of potential NASH cirrhosis cases (n = 5415). We created a training set of 300 randomly selected patients for chart review to confirm cases of NASH cirrhosis. Test characteristics of different algorithms, consisting of diagnosis codes, laboratory values, anthropomorphic measurements, and medication records, were calculated. The algorithms with the highest positive predictive value (PPV) and the highest F score with a PPV ≥ 80% were selected for internal validation using a separate random set of 100 patients from the potential NASH cirrhosis pool. These were then externally validated in another random set of 100 individuals using the research patient data registry tool at Massachusetts General Hospital. The algorithm with the highest PPV of 100% on internal validation and 92% on external validation consisted of ≥ 3 counts of "cirrhosis, no mention of alcohol" (571.5, K74.6) and ≥ 3 counts of "nonalcoholic fatty liver" (571.8-571.9, K75.81, K76.0) codes in the absence of any diagnosis codes for other common causes of chronic liver disease. We developed and validated an EHR algorithm using diagnosis codes that accurately identifies patients with NASH cirrhosis. Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis in the EHR.BACKGROUND AND AIMSCurrent genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis in the EHR.We used Clinical Query 2, a search tool at Beth Israel Deaconess Medical Center, to create a pool of potential NASH cirrhosis cases (n = 5415). We created a training set of 300 randomly selected patients for chart review to confirm cases of NASH cirrhosis. Test characteristics of different algorithms, consisting of diagnosis codes, laboratory values, anthropomorphic measurements, and medication records, were calculated. The algorithms with the highest positive predictive value (PPV) and the highest F score with a PPV ≥ 80% were selected for internal validation using a separate random set of 100 patients from the potential NASH cirrhosis pool. These were then externally validated in another random set of 100 individuals using the research patient data registry tool at Massachusetts General Hospital.METHODSWe used Clinical Query 2, a search tool at Beth Israel Deaconess Medical Center, to create a pool of potential NASH cirrhosis cases (n = 5415). We created a training set of 300 randomly selected patients for chart review to confirm cases of NASH cirrhosis. Test characteristics of different algorithms, consisting of diagnosis codes, laboratory values, anthropomorphic measurements, and medication records, were calculated. The algorithms with the highest positive predictive value (PPV) and the highest F score with a PPV ≥ 80% were selected for internal validation using a separate random set of 100 patients from the potential NASH cirrhosis pool. These were then externally validated in another random set of 100 individuals using the research patient data registry tool at Massachusetts General Hospital.The algorithm with the highest PPV of 100% on internal validation and 92% on external validation consisted of ≥ 3 counts of "cirrhosis, no mention of alcohol" (571.5, K74.6) and ≥ 3 counts of "nonalcoholic fatty liver" (571.8-571.9, K75.81, K76.0) codes in the absence of any diagnosis codes for other common causes of chronic liver disease.RESULTSThe algorithm with the highest PPV of 100% on internal validation and 92% on external validation consisted of ≥ 3 counts of "cirrhosis, no mention of alcohol" (571.5, K74.6) and ≥ 3 counts of "nonalcoholic fatty liver" (571.8-571.9, K75.81, K76.0) codes in the absence of any diagnosis codes for other common causes of chronic liver disease.We developed and validated an EHR algorithm using diagnosis codes that accurately identifies patients with NASH cirrhosis.CONCLUSIONSWe developed and validated an EHR algorithm using diagnosis codes that accurately identifies patients with NASH cirrhosis. |
| Audience | Professional Academic |
| Author | Lai, Michelle Corey, Kathleen E. Danford, Christopher J. Strohbehn, Ian A. Lee, Jennifer Y. |
| Author_xml | – sequence: 1 givenname: Christopher J. orcidid: 0000-0003-0322-7705 surname: Danford fullname: Danford, Christopher J. email: cdanford@bidmc.harvard.edu organization: Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center, Liver Center – sequence: 2 givenname: Jennifer Y. surname: Lee fullname: Lee, Jennifer Y. organization: Department of Medicine, Beth Israel Deaconess Medical Center – sequence: 3 givenname: Ian A. surname: Strohbehn fullname: Strohbehn, Ian A. organization: Gastrointestinal Unit, Massachusetts General Hospital – sequence: 4 givenname: Kathleen E. surname: Corey fullname: Corey, Kathleen E. organization: Gastrointestinal Unit, Massachusetts General Hospital – sequence: 5 givenname: Michelle surname: Lai fullname: Lai, Michelle organization: Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center, Liver Center |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32535780$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1186_s43066_022_00224_w crossref_primary_10_1080_00365521_2023_2185475 crossref_primary_10_1136_bmjhci_2021_100510 crossref_primary_10_1186_s12967_024_05726_2 crossref_primary_10_14309_ajg_0000000000002254 |
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Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large... Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our... Background and Aims Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large... Background and AimsCurrent genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large... |
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| SubjectTerms | Alcohol Algorithms Anthropomorphism Biochemistry Codes Electronic health records Electronic records Esophagus Fatty liver Gastroenterology Genetics Genomes Hepatitis C Hepatology Hospitals Laboratories Liver Liver cancer Liver cirrhosis Liver diseases Medical centers Medical records Medical research Medicine Medicine & Public Health Medicine, Experimental Oncology Original Article Patients Peritonitis Transplant Surgery Type 2 diabetes |
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| Title | Development of an Algorithm to Identify Cases of Nonalcoholic Steatohepatitis Cirrhosis in the Electronic Health Record |
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