Machine Learning-Based Prediction of Elevated N Terminal Pro Brain Natriuretic Peptide Among US General Population
Using nationally representative data from the United States, both logistic regression and machine learning models well predicted elevated NT-proBNP. The predictive performance remained consistent even when the models incorporated only commonly available variables in daily clinical practice. Prediction models using regularly measured information would serve as a potentially useful tools for clinicians to effectively identify targets of natriuretic-peptide screening.
Authors
- Shingo Fukuma (ORCID: https://orcid.org/0000-0002-8379-8761)
- Naoki Kondo (ORCID: https://orcid.org/0000-0002-6425-6844)
- Kosuke Inoue (ORCID: https://orcid.org/0000-0001-9614-8103)
- Kyohei Yamaji
- Yuichiro Mori (ORCID: https://orcid.org/0000-0001-7211-8970)
- Atsushi Mizuno
Institutions
- Kyoto University (JP)
- St. Luke's International Hospital (JP)
Publication Details
- Journal
- ESC Heart Failure
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1002/ehf2.15056
- Citations
- 6
- Primary Topic
- Heart Failure Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 2.41
Funders
- Japan Agency for Medical Research and Development
- Japan Science and Technology Corporation
- Ministry of Education, Culture, Sports, Science and Technology
- Japan Society for the Promotion of Science
- Precursory Research for Embryonic Science and Technology