{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T11:07:01Z","timestamp":1789556821525,"version":"build-2803163510"},"reference-count":24,"publisher":"BMJ","issue":"2","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"unspecified","delay-in-days":70,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Open Heart"],"accepted":{"date-parts":[[2026,8,28]]},"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Background and objectives<\/jats:title>\n                    <jats:p>The History, ECG, Age, Risk factors and Troponin (HEART) score is widely used for emergency department (ED) chest pain risk stratification, but its fixed-point structure may not optimally represent relationships between its components and major adverse cardiac events (MACE). We evaluated whether increasingly flexible modelling approaches improve prediction when restricted to the same five HEART components.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective multicentre cohort study of 47\u2009853 adult ED patients with chest pain and documented HEART scores across six EDs from 2021 to 2023. Data from five EDs (n=39\u2009587) were used for model development and one geographically held-out ED (n=8266) was used for validation. The conventional HEART score was compared with categorical cross-validated logistic regression (CVLR) and extreme gradient boosting (XGBoost) for predicting 30-day MACE. Performance was assessed using the area under the receiver operating characteristic (AUROC), the area under the precision\u2013recall curve (AUPRC), the Brier score, paired bootstrap comparisons and clinical performance at comparable-sensitivity operating points.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the held-out validation cohort, 267 patients (3.23%) experienced 30-day MACE. AUROCs were similar for HEART (0.855), CVLR (0.857) and XGBoost (0.856; pairwise p&gt;0.05). In contrast, both data-driven models demonstrated significantly higher AUPRC than HEART: CVLR (0.237 vs 0.187; \u0394=0.050, p&lt;0.001) and XGBoost (0.244 vs 0.187; \u0394=0.057, p&lt;0.001) with no significant difference between CVLR and XGBoost (p=0.305). At comparable-sensitivity operating points, HEART, CVLR and XGBoost classified 63.74%, 59.39% and 61.58% of patients as low risk, respectively, with corresponding low-risk MACE rates of 0.53%, 0.51% and 0.57%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Data-driven reweighting of HEART components improved precision\u2013recall performance and risk ranking compared with fixed HEART scoring. However, XGBoost provided no incremental benefit over categorical CVLR, and neither approach expanded low-risk classification at comparable sensitivity.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/openhrt-2026-004391","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T12:40:43Z","timestamp":1788957643000},"page":"e004391","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Does increasing model complexity improve HEART-based chest pain risk stratification? A multicentre development and held-out emergency department validation study"],"prefix":"10.1136","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5105-0951","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA"},{"name":"Department of Emergency Medicine, Baylor University Medical Center, Dallas, Texas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jenna R","family":"Williamson","sequence":"additional","affiliation":[{"name":"Anne Burnett Marion School of Medicine, Texas Christian University, Fort Worth, Texas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4808-6258","authenticated-orcid":false,"given":"Hunter","family":"Scarborough","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devin","family":"Sandlin","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"2026091603503273000_13.2.e004391.1","first-page":"e368","article-title":"2021 AHA\/ACC\/ASE\/CHEST\/SAEM\/SCCT\/SCMR guideline for the evaluation and diagnosis of chest pain: a report of the American College of Cardiology\/American Heart Association Joint Committee on Clinical Practice Guidelines","volume":"144","author":"Gulati","year":"2021","journal-title":"Circulation"},{"key":"2026091603503273000_13.2.e004391.2","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1001\/jamainternmed.2020.4325","article-title":"Early noninvasive cardiac testing after emergency department evaluation for suspected acute coronary syndrome","volume":"180","author":"Kawatkar","year":"2020","journal-title":"JAMA Intern Med"},{"key":"2026091603503273000_13.2.e004391.3","doi-asserted-by":"publisher","DOI":"10.1007\/BF03086144"},{"key":"2026091603503273000_13.2.e004391.4","doi-asserted-by":"crossref","unstructured":"Meyering SH , Schrader CD , Kumar D , et al . Role of HEART score in evaluating clinical outcomes among emergency department patients with different ethnicities. J Int Med Res 2021;49. doi:10.1177\/03000605211010638","DOI":"10.1177\/03000605211010638"},{"key":"2026091603503273000_13.2.e004391.5","doi-asserted-by":"publisher","DOI":"10.1016\/j.annemergmed.2018.12.010"},{"key":"2026091603503273000_13.2.e004391.6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2018.07.059"},{"key":"2026091603503273000_13.2.e004391.7","doi-asserted-by":"publisher","DOI":"10.1016\/j.annemergmed.2021.02.007"},{"key":"2026091603503273000_13.2.e004391.8","doi-asserted-by":"publisher","DOI":"10.1016\/j.annemergmed.2018.07.045"},{"key":"2026091603503273000_13.2.e004391.9","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCRESAHA.117.310782"},{"key":"2026091603503273000_13.2.e004391.10","doi-asserted-by":"crossref","DOI":"10.1111\/eci.70017","article-title":"Machine learning in cardiovascular risk assessment: towards a precision medicine approach","volume":"55 Suppl 1","author":"Wang","year":"2025","journal-title":"Eur J Clin Invest"},{"key":"2026091603503273000_13.2.e004391.11","doi-asserted-by":"publisher","DOI":"10.1161\/cir.0000000000001201"},{"key":"2026091603503273000_13.2.e004391.12","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.jacc.2024.05.003","article-title":"Transforming cardiovascular care with artificial intelligence: from discovery to practice","volume":"84","author":"Khera","year":"2024","journal-title":"J Am Coll Cardiol"},{"key":"2026091603503273000_13.2.e004391.13","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehae880"},{"key":"2026091603503273000_13.2.e004391.14","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.125.041915","article-title":"Using machine learning to risk stratify emergency department patients with chest pain but no acute myocardial infarction: a multicenter retrospective analysis","volume":"14","author":"Chou","year":"2025","journal-title":"J Am Heart Assoc"},{"key":"2026091603503273000_13.2.e004391.15","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-51604-8","article-title":"Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait","volume":"14","author":"Alkhamis","year":"2024","journal-title":"Sci Rep"},{"key":"2026091603503273000_13.2.e004391.16","doi-asserted-by":"crossref","DOI":"10.2196\/76215","article-title":"Comparing the performance of machine learning models and conventional risk scores for predicting major adverse cardiovascular cerebrovascular events after percutaneous coronary intervention in patients with acute myocardial infarction: systematic review and meta-analysis","volume":"27","author":"Yu","year":"2025","journal-title":"J Med Internet Res"},{"key":"2026091603503273000_13.2.e004391.17","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"2026091603503273000_13.2.e004391.18","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.h5527","article-title":"STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies","volume":"351","author":"Bossuyt","year":"2015","journal-title":"BMJ"},{"key":"2026091603503273000_13.2.e004391.19","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0202344","article-title":"Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease","volume":"13","author":"Steele","year":"2018","journal-title":"PLoS One"},{"key":"2026091603503273000_13.2.e004391.20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2019.02.004"},{"key":"2026091603503273000_13.2.e004391.21","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets","volume":"10","author":"Saito","year":"2015","journal-title":"PLoS One"},{"key":"2026091603503273000_13.2.e004391.22","doi-asserted-by":"publisher","DOI":"10.21037\/jtd.2019.01.25"},{"key":"2026091603503273000_13.2.e004391.23","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehz565"},{"key":"2026091603503273000_13.2.e004391.24","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.123.066917"}],"container-title":["Open Heart"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1136\/openhrt-2026-004391","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T10:50:51Z","timestamp":1789555851000},"score":1,"resource":{"primary":{"URL":"https:\/\/openheart.bmj.com\/lookup\/doi\/10.1136\/openhrt-2026-004391"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":24,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,9,9]]},"published-print":{"date-parts":[[2026,7]]}},"alternative-id":["10.1136\/openhrt-2026-004391"],"URL":"https:\/\/doi.org\/10.1136\/openhrt-2026-004391","relation":{},"ISSN":["2053-3624"],"issn-type":[{"value":"2053-3624","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]}}}