• Naveed Afzal, Vishnu Priya Mallipeddi, Sunghwan Sohn, Hongfang Liu, Rajeev Chaudhry, Christopher G Scott, Iftikhar J Kullo, and Adelaide M Arruda-Olson.
• Department of Health Sciences Research, Mayo Clinic and Mayo Foundation, Rochester, MN, United States.
• Int J Med Inform. 2018 Mar 1; 111: 83-89.

BackgroundCritical limb ischemia (CLI) is a complication of advanced peripheral artery disease (PAD) with diagnosis based on the presence of clinical signs and symptoms. However, automated identification of cases from electronic health records (EHRs) is challenging due to absence of a single definitive International Classification of Diseases (ICD-9 or ICD-10) code for CLI.Methods And ResultsIn this study, we extend a previously validated natural language processing (NLP) algorithm for PAD identification to develop and validate a subphenotyping NLP algorithm (CLI-NLP) for identification of CLI cases from clinical notes. We compared performance of the CLI-NLP algorithm with CLI-related ICD-9 billing codes. The gold standard for validation was human abstraction of clinical notes from EHRs. Compared to billing codes the CLI-NLP algorithm had higher positive predictive value (PPV) (CLI-NLP 96%, billing codes 67%, p < 0.001), specificity (CLI-NLP 98%, billing codes 74%, p < 0.001) and F1-score (CLI-NLP 90%, billing codes 76%, p < 0.001). The sensitivity of these two methods was similar (CLI-NLP 84%; billing codes 88%; p < 0.12).ConclusionsThe CLI-NLP algorithm for identification of CLI from narrative clinical notes in an EHR had excellent PPV and has potential for translation to patient care as it will enable automated identification of CLI cases for quality projects, clinical decision support tools and support a learning healthcare system.Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

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