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 M Speedie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1504-1507
Number of pages4
ISBN (Electronic)9781538654880
DOIs
StatePublished - Jan 21 2019
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: Dec 3 2018Dec 6 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
CountrySpain
CityMadrid
Period12/3/1812/6/18

Fingerprint

Electronic Health Records
Heart Failure
Health
Cardiac Resynchronization Therapy
Documentation
Cardiac resynchronization therapy
Area Under Curve
Disease Progression
Outcome Assessment (Health Care)
Learning systems

Keywords

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

Cite this

Ma, S., Zhang, R., Munroe, J., Shanahan, L., Horn, S., & Speedie, S. M. (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). [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; Shanahan, Lindsey; Horn, Sarah; Speedie, Stuart M.

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 proceedingConference contribution

Ma, S, Zhang, R, Munroe, J, Shanahan, L, Horn, S & Speedie, SM 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 SM. 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). https://doi.org/10.1109/BIBM.2018.8621518
Ma, Sisi ; Zhang, Rui ; Munroe, Jessica ; Shanahan, Lindsey ; Horn, Sarah ; Speedie, Stuart M. / 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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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.",
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