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 proceedingConference contribution

1 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 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

Bibliographical note

Funding Information:
The research reported in this article was performed in collaboration with the named Medtronic, Inc. employees by faculty members at the University of Minnesota under a contract with the University of Minnesota funded by Medtronic, Inc.

Funding Information:
The Clinical Data Repository used in this work was partially funded by the CTSI grant from National Center for Advancing Translational Sciences (UL1TR000114, Blazar). Medtronic, Inc. funded the research reported in this work through a contract with the University of Minnesota.

Keywords

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

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