Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record

Sisi Ma; Rui Zhang; Jessica Munroe; Lindsey Shanahan; Sarah Horn; Stuart Speedie

doi:10.1109/BIBM.2018.8621518

Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record

Sisi Ma, Rui Zhang, Jessica Munroe, Lindsey Shanahan, Sarah Horn, Stuart Speedie

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

New York Heart Association (NYHA) Class is an important measure of functional status for heart failure (HF) patients used for clinical documentation, treatment decisions, as well as for eligibility criteria and outcome measures in clinical studies. Electronic health records (EHRs) possess the potential to more efficiently access NYHA class information to measure effectiveness of treatments such as cardiac resynchronization therapy (CRT) and to monitor disease progression. However, our previous study has shown that the percentage of encounters of HF patients with a CRT implant that have explicit NYHA class documentation is low. In the present study, we examine if NYHA class can be estimated from EHR data for HF patients when it is not explicitly documented. Using machine learning methods, we constructed a model that estimated NYHA class for HF patient encounters with high quality, demonstrated by an AUC of 0.87.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1504-1507
Number of pages	4
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621518
State	Published - Jan 21 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: Dec 3 2018 → Dec 6 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	12/3/18 → 12/6/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Cardiac Resynchronization Therapy
Electronic Health Records
Heart Failure
Machine Learning
Medical Informatics

Publisher link

10.1109/BIBM.2018.8621518

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Ma, S., Zhang, R., Munroe, J., Shanahan, L., Horn, S., & Speedie, S. (2019). Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 1504-1507). Article 8621518 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621518

Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record. / Ma, Sisi ; Zhang, Rui; Munroe, Jessica et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1504-1507 8621518 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ma, S , Zhang, R, Munroe, J, Shanahan, L, Horn, S & Speedie, S 2019, Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621518, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1504-1507, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 12/3/18. https://doi.org/10.1109/BIBM.2018.8621518

Ma S , Zhang R, Munroe J, Shanahan L, Horn S, Speedie S. Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1504-1507. 8621518. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621518

Ma, Sisi ; Zhang, Rui ; Munroe, Jessica et al. / Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1504-1507 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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title = "Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record",

abstract = "New York Heart Association (NYHA) Class is an important measure of functional status for heart failure (HF) patients used for clinical documentation, treatment decisions, as well as for eligibility criteria and outcome measures in clinical studies. Electronic health records (EHRs) possess the potential to more efficiently access NYHA class information to measure effectiveness of treatments such as cardiac resynchronization therapy (CRT) and to monitor disease progression. However, our previous study has shown that the percentage of encounters of HF patients with a CRT implant that have explicit NYHA class documentation is low. In the present study, we examine if NYHA class can be estimated from EHR data for HF patients when it is not explicitly documented. Using machine learning methods, we constructed a model that estimated NYHA class for HF patient encounters with high quality, demonstrated by an AUC of 0.87.",

keywords = "Cardiac Resynchronization Therapy, Electronic Health Records, Heart Failure, Machine Learning, Medical Informatics",

author = "Sisi Ma and Rui Zhang and Jessica Munroe and Lindsey Shanahan and Sarah Horn and Stuart Speedie",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

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Estimating New York Heart Association Classification for Heart Failure Patients from Information in the Electronic Health Record

Abstract

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