Automated extraction of sudden cardiac death risk factors in hypertrophic cardiomyopathy patients by natural language processing

Sungrim Moon; Sijia Liu; Christopher G Scott; Sujith Samudrala; Mohamed M Abidian; Jeffrey B Geske; Peter A Noseworthy; Jane L Shellum; Rajeev Chaudhry; Steve R Ommen; Rick A Nishimura; Hongfang Liu; Adelaide M Arruda-Olson

doi:10.1016/j.ijmedinf.2019.05.008

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Automated extraction of sudden cardiac death risk factors in hypertrophic cardiomyopathy patients by natural language processing

Journal article

Open access

Peer reviewed

Automated extraction of sudden cardiac death risk factors in hypertrophic cardiomyopathy patients by natural language processing

Sungrim Moon, Sijia Liu, Christopher G Scott, Sujith Samudrala, Mohamed M Abidian, Jeffrey B Geske, Peter A Noseworthy, Jane L Shellum, Rajeev Chaudhry, Steve R Ommen, …

International journal of medical informatics (Shannon, Ireland), Vol.128, pp.32-38

08/2019

DOI: 10.1016/j.ijmedinf.2019.05.008

PMID: 31160009

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Abstract

Background: The management of hypertrophic cardiomyopathy (HCM) patients requires the knowledge of risk factors associated with sudden cardiac death (SCD). SCD risk factors such as syncope and family history of SCD (FH-SCD) as well as family history of HCM (FH-HCM) are documented in electronic health records (EHRs) as clinical narratives. Automated extraction of risk factors from clinical narratives by natural language processing (NLP) may expedite management workflow of HCM patients. The aim of this study was to develop and deploy NLP algorithms for automated extraction of syncope, FH-SCD, and FH-HCM from clinical narratives. Methods and Results: We randomly selected 200 patients from the Mayo HCM registry for development (n = 100) and testing (n = 100) of NLP algorithms for extraction of syncope, FH-SCD as well as FH-HCM from clinical narratives of EHRs. The clinical reference standard was manually abstracted by 2 independent annotators. Performance of NLP algorithms was compared to aggregation and summarization of data entries in the HCM registry for syncope, FH-SCD, and FH-HCM. We also compared the NLP algorithms with billing codes for syncope as well as responses to patient survey questions for FH-SCD and FH-HCM. These analyses demonstrated NLP had superior sensitivity (0.96 vs 0.39, p < 0.001) and comparable specificity (0.90 vs 0.92, p = 0.74) and PPV (0.90 vs 0.83, p = 0.37) compared to billing codes for syncope. For FH-SCD, NLP outperformed survey responses for all parameters (sensitivity: 0.91 vs 0.59, p = 0.002; specificity: 0.98 vs 0.50, p < 0.001; PPV: 0.97 vs 0.38, p < 0.001). NLP also achieved superior sensitivity (0.95 vs 0.24, p < 0.001) with comparable specificity (0.95 vs 1.0, p-value not calculable) and positive predictive value (PPV) (0.92 vs 1.0, p = 0.09) compared to survey responses for FH-HCM. Conclusions: Automated extraction of syncope, FH-SCD and FH-HCM using NLP is feasible and has promise to increase efficiency of workflow for providers managing HCM patients.

Electronic Health Records

Hypertrophic cardiomyopathy

Natural language processing

Sudden cardiac death

Details

Title: Subtitle: Automated extraction of sudden cardiac death risk factors in hypertrophic cardiomyopathy patients by natural language processing
Creators: Sungrim Moon - Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
Sijia Liu - Emory University
Christopher G Scott - Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
Sujith Samudrala - Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
Mohamed M Abidian - University of Iowa, Internal Medicine
Jeffrey B Geske - Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
Peter A Noseworthy - Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
Jane L Shellum - Robert and Patricia Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
Rajeev Chaudhry - Robert and Patricia Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
Steve R Ommen - Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
Rick A Nishimura - Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
Hongfang Liu - Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
Adelaide M Arruda-Olson - Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
Resource Type: Journal article
Publication Details: International journal of medical informatics (Shannon, Ireland), Vol.128, pp.32-38
DOI: 10.1016/j.ijmedinf.2019.05.008
PMID: 31160009
NLM abbreviation: Int J Med Inform
ISSN: 1386-5056
eISSN: 1872-8243
Publisher: Elsevier B.V
Grant note: name: National Heart, Lung, and Blood Institute of the National Institutes of Health, award: K01HL124045; name: National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health, award: R01EB19403; name: National Center for Advancing Translational Sciences of the National Institutes of Health, award: U01TR02062; name: Mayo Clinic K2R award
Language: English
Date published: 08/2019
Academic Unit: General Internal Medicine; Internal Medicine
Record Identifier: 9984129300402771

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