{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T09:28:44Z","timestamp":1769765324552,"version":"3.49.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Commun Med"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) remains challenging despite numerous 12-lead electrocardiogram (ECG) criteria and algorithms. Automated solutions leveraging computerized ECG interpretation (CEI) measurements and engineered features offer practical ways to improve diagnostic accuracy. We propose automated algorithms based on (i) WCT QRS polarity direction (WCT Polarity Code [WCT-PC]) and (ii) QRS polarity shifts between WCT and baseline ECGs (QRS Polarity Shift [QRS-PS]).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In a three-part study, we derive and validate machine learning (ML) models\u2014logistic regression (LR), artificial neural network (ANN), Random Forests (RF), support vector machine (SVM), and ensemble learning (EL)\u2014using engineered (WCT-PC and QRS-PS) and previously established WCT differentiation features. Part 1 uses WCT ECG measurements alone, Part 2 pairs WCT and baseline ECG features, and Part 3 combines all features used in Parts 1 and 2<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Among 235 WCT patients (158 SWCT, 77 VT), 103 had gold standard diagnoses. Part 1 models achieved AUCs of 0.86\u20130.88 using WCT ECG features alone. Part 2 improved accuracy with paired ECGs (AUCs 0.90\u20130.93). Part 3 showed variable results (AUC 0.72\u20130.93), with RF and SVM performing best.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Incorporating engineered parameters related to QRS polarity direction and shifts can yield effective WCT differentiation, presenting a promising approach for automated CEI algorithms.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1038\/s43856-024-00725-2","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T11:00:25Z","timestamp":1735642825000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated differentiation of wide QRS complex tachycardia using QRS complex polarity"],"prefix":"10.1038","volume":"4","author":[{"given":"Adam M.","family":"May","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhavesh B.","family":"Katbamna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Preet A.","family":"Shaikh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"LoCoco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Deych","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiwen","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krasimira M.","family":"Mikhova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rugheed","family":"Ghadban","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Phillip S.","family":"Cuculich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel H.","family":"Cooper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas M.","family":"Maddox","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter A.","family":"Noseworthy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthony","family":"Kashou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"725_CR1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.120.016598","volume":"9","author":"AH Kashou","year":"2020","unstructured":"Kashou, A. 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The technology in question relates directly to this manuscript\u2019s content. This technology was developed without industry funding or influence and was disclosed to the Office of Technology Management\/Tech Transfer (OTM) at Washington University in St. Louis, which possesses intellectual property rights. Adam May is an Editorial Board Member for <i>Communications Medicine<\/i> but was not involved in the editorial review or peer review, nor in the decision to publish this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"282"}}