Development and Validation of an Algorithm to Identify Nonalcoholic Fatty Liver Disease in the Electronic Medical Record.
Background and Aims: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD disease progression and liver-related outcomes remain incompletely understood due to the lack of computational identification methods. The present study s...
| Publicado en: | Digestive Diseases & Sciences Vol. 61; no. 3; pp. 913 - 920 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Mar2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=113205114&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113205114 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01632116 1VP jtl: Digestive Diseases & Sciences issn: 01632116 maglogo: N pubinfo: dt: Mar2016 vid: 61 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 113205114 113205114 NLM26537487 113205114 10.1007/s10620-015-3952-x NLM26537487 PMC4761309 [Available on 03/01/17] 113205114 ppf: 913 ppct: 7 formats: tig: atl: Development and Validation of an Algorithm to Identify Nonalcoholic Fatty Liver Disease in the Electronic Medical Record. aug: au: Corey, Kathleen Kartoun, Uri Zheng, Hui Shaw, Stanley Corey, Kathleen E Shaw, Stanley Y affil: Biostatistics Center, Massachusetts General Hospital, Boston USA sug: subj: Natural Language Processing Nonalcoholic Fatty Liver Disease Blood Electronic Health Records Algorithms Nonalcoholic Fatty Liver Disease Epidemiology Triglycerides Blood Alanine Aminotransferase Blood Male Data Collection Adult Diabetes Mellitus Epidemiology International Classification of Diseases Prevalence Female Aged Prospective Studies Biopsy Aspartate Aminotransferase Blood United States Human Middle Age Logistic Regression Validation Studies Comparative Studies Evaluation Research Multicenter Studies Arthritis Impact Measurement Scales Scales Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Male Female ab: Background and Aims: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Risk factors for NAFLD disease progression and liver-related outcomes remain incompletely understood due to the lack of computational identification methods. The present study sought to design a classification algorithm for NAFLD within the electronic medical record (EMR) for the development of large-scale longitudinal cohorts.Methods: We implemented feature selection using logistic regression with adaptive LASSO. A training set of 620 patients was randomly selected from the Research Patient Data Registry at Partners Healthcare. To assess a true diagnosis for NAFLD we performed chart reviews and considered either a documentation of a biopsy or a clinical diagnosis of NAFLD. We included in our model variables laboratory measurements, diagnosis codes, and concepts extracted from medical notes. Variables with P < 0.05 were included in the multivariable analysis.Results: The NAFLD classification algorithm included number of natural language mentions of NAFLD in the EMR, lifetime number of ICD-9 codes for NAFLD, and triglyceride level. This classification algorithm was superior to an algorithm using ICD-9 data alone with AUC of 0.85 versus 0.75 (P < 0.0001) and leads to the creation of a new independent cohort of 8458 individuals with a high probability for NAFLD.Conclusions: The NAFLD classification algorithm is superior to ICD-9 billing data alone. This approach is simple to develop, deploy, and can be applied across different institutions to create EMR-based cohorts of individuals with NAFLD. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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