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However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre-heart failure, has not been well established. This study evaluated the performance of machine learning prediction models for predicting elevated N terminal pro brain natriuretic peptide (NT-proBNP) levels in the US general population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>Individuals aged 20\u201379 years without cardiovascular disease from the nationally representative National Health and Nutrition Examination Survey 1999\u20132004 were included. Six prediction models (two conventional regression models and four machine learning models) were trained with the 1999\u20132002 cohort to predict elevated NT-proBNP levels (&amp;gt;125 pg\/mL) using demographic, lifestyle, and commonly measured biochemical data. The model performance was tested using the 2003\u20132004 cohort. Of the 10 237 individuals, 1510 (14.8%) had NT-proBNP levels &amp;gt;125 pg\/mL. The highest area under the receiver operating characteristic curve (AUC) was observed in SuperLearner (AUC [95% CI] = 0.862 [0.847\u20130.878], P &amp;lt; 0.001 compared with the logistic regression model). The logistic regression model with splines showed a comparable performance (AUC [95% CI] = 0.857 [0.841\u20130.874], P = 0.08). Age, albumin level, haemoglobin level, sex, estimated glomerular filtration rate, and systolic blood pressure were the most important predictors. We found a similar prediction performance even after excluding socio-economic information (marital status, family income, and education status) from the prediction models. When we used different thresholds for elevated NT-proBNP, the AUC (95% CI) in the SuperLearner models 0.846 (0.830\u20130.861) for NT-proBNP &amp;gt; 100 pg\/mL and 0.866 (0.849\u20130.884) for NT-proBNP &amp;gt; 150 pg\/mL.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ehf2.15056","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T03:57:51Z","timestamp":1731988671000},"page":"859-868","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine Learning-Based Prediction of Elevated N Terminal Pro Brain Natriuretic Peptide Among US General Population"],"prefix":"10.1093","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7211-8970","authenticated-orcid":false,"given":"Yuichiro","family":"Mori","sequence":"first","affiliation":[{"name":"Department of Human Health Sciences, Graduate School of Medicine, Kyoto University , Kyoto ,","place":["Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shingo","family":"Fukuma","sequence":"additional","affiliation":[{"name":"Department of Human Health Sciences, Graduate School of Medicine, Kyoto University , Kyoto ,","place":["Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyohei","family":"Yamaji","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University , Kyoto ,","place":["Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atsushi","family":"Mizuno","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, St. Luke's International Hospital , Tokyo ,","place":["Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoki","family":"Kondo","sequence":"additional","affiliation":[{"name":"Department of Social Epidemiology, Graduate School of Medicine, Kyoto University , Kyoto ,","place":["Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9614-8103","authenticated-orcid":false,"given":"Kosuke","family":"Inoue","sequence":"additional","affiliation":[{"name":"Department of Social Epidemiology, Graduate School of Medicine, Kyoto University , Kyoto ,","place":["Japan"]},{"name":"Hakubi Center for Advanced Research, Kyoto University , Kyoto ,","place":["Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"2026021803543709600_ehf215056-bib-0001","doi-asserted-by":"publisher","first-page":"e895","DOI":"10.1161\/CIR.0000000000001063","article-title":"2022 AHA\/ACC\/HFSA guideline for the Management of Heart Failure: a report of the American College of Cardiology\/American Heart Association joint committee on clinical practice guidelines","volume":"145","author":"Heidenreich","year":"2022","journal-title":"Circulation American Heart Association"},{"key":"2026021803543709600_ehf215056-bib-0002","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.122.029110","article-title":"NT-proBNP and all-cause and cardiovascular mortality in US adults: a prospective cohort study","volume":"12","author":"Echouffo-Tcheugui","year":"2023","journal-title":"J Am Heart Assoc Ovid Technologies (Wolters Kluwer Health)"},{"key":"2026021803543709600_ehf215056-bib-0003","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1016\/j.jchf.2016.05.001","article-title":"Prevalence, Neurohormonal correlates, and prognosis of heart failure stages in the community","volume":"4","author":"Xanthakis","year":"2016","journal-title":"JACC Heart Fail"},{"key":"2026021803543709600_ehf215056-bib-0004","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1056\/NEJMp1702071","article-title":"Machine learning and prediction in medicine - 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