Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals. (7th August 2016)

• Record Type: Journal Article
• Authors: Teixeira, Pedro L; Wei, Wei-Qi; Cronin, Robert M; Mo, Huan; VanHouten, Jacob P; Carroll, Robert J; LaRose, Eric; Bastarache, Lisa A; Rosenbloom, S. Trent; Edwards, Todd L; Roden, Dan M; Lasko, Thomas A; Dart, Richard A; Nikolai, Anne M; Peissig, Peggy L; Denny, Joshua C
• Is Part Of: Journal of the American Medical Informatics Association. Volume 24:Number 1(2017:Jan.)
• Page Start: 162
• Page End: 171
• Publication Date: 2016-08-07
• Subjects: phenotyping algorithms -- machine learning -- random forests -- hypertension -- natural language processing -- electronic health records; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocw071
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract : Objective: Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, and is time- and labor-intensive. We developed and evaluated 4 types of phenotyping algorithms and categories of EHR information to identify hypertensive individuals and controls and provide a portable module for implementation at other sites. Materials and Methods: We reviewed the EHRs of 631 individuals followed at Vanderbilt for hypertension status. We developed features and phenotyping algorithms of increasing complexity. Input categories included International Classification of Diseases, Ninth Revision (ICD9) codes, medications, vital signs, narrative-text search results, and Unified Medical Language System (UMLS) concepts extracted using natural language processing (NLP). We developed a module and tested portability by replicating 10 of the best-performing algorithms at the Marshfield Clinic. Results: Random forests using billing codes, medications, vitals, and concepts had the best performance with a median area under the receiver operator characteristic curve (AUC) of 0.976. Normalized sums of all 4 categories also performed well (0.959 AUC). The best non-NLP algorithm combined normalized ICD9 codes, medications, and blood pressure readings with a median AUC of 0.948. Blood pressure cutoffs or ICD9 code counts alone had AUCs of 0.854 and 0.908, respectively. Marshfield Clinic results were similar. Conclusion: This work shows that billing codes or blood pressure readings alone yield good hypertension classification performance. However, even simple combinations of input categories improve performance. The most complex algorithms classified hypertension with excellent recall and precision.