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.|
|Number of pages||4|
|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
|Name||Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018|
|Conference||2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018|
|Period||12/3/18 → 12/6/18|
Bibliographical noteFunding 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.
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.
© 2018 IEEE.
- Cardiac Resynchronization Therapy
- Electronic Health Records
- Heart Failure
- Machine Learning
- Medical Informatics