{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T22:42:41Z","timestamp":1793659361581,"version":"4.5.1"},"reference-count":40,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"12","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2024,6,18]]},"abstract":"\n Background<\/jats:title>\n \n Oxygen saturation (Sp\n o<\/jats:sc>\n 2<\/jats:sub>\n ) screening has not led to earlier detection of critical congenital heart disease (CCHD). Adding pulse oximetry features (ie, perfusion data and radiofemoral pulse delay) may improve CCHD detection, especially coarctation of the aorta (CoA). We developed and tested a machine learning (ML) pulse oximetry algorithm to enhance CCHD detection.\n <\/jats:p>\n <\/jats:sec>\n \n Methods and Results<\/jats:title>\n \n Six sites prospectively enrolled newborns with and without CCHD and recorded simultaneous pre\u2010 and postductal pulse oximetry. We focused on models at 1 versus 2 time points and with\/without pulse delay for our ML algorithms. The sensitivity, specificity, and area under the receiver operating characteristic curve were compared between the Sp\n o<\/jats:sc>\n 2<\/jats:sub>\n \u2010alone and ML algorithms. A total of 523 newborns were enrolled (no CHD, 317; CHD, 74; CCHD, 132, of whom 21 had isolated CoA). When applying the Sp\n o<\/jats:sc>\n 2<\/jats:sub>\n \u2010alone algorithm to all patients, 26.2% of CCHD would be missed. We narrowed the sample to patients with both 2 time point measurements and pulse\u2010delay data (no CHD, 65; CCHD, 14) to compare ML performance. Among these patients, sensitivity for CCHD detection increased with both the addition of pulse delay and a second time point. All ML models had 100% specificity. With a 2\u2010time\u2010points+pulse\u2010delay model, CCHD sensitivity increased to 92.86% (\n P<\/jats:italic>\n =0.25) compared with Sp\n o<\/jats:sc>\n 2<\/jats:sub>\n alone (71.43%), and CoA increased to 66.67% (\n P<\/jats:italic>\n =0.5) from 0. The area under the receiver operating characteristic curve for CCHD and CoA detection significantly improved (0.96 versus 0.83 for CCHD, 0.83 versus 0.48 for CoA; both\n P<\/jats:italic>\n =0.03) using the 2\u2010time\u2010points+pulse\u2010delay model compared with Sp\n o<\/jats:sc>\n 2<\/jats:sub>\n alone.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n ML pulse oximetry that combines oxygenation, perfusion data, and pulse delay at 2 time points may improve detection of CCHD and CoA within 48\u2009hours after birth.<\/jats:p>\n <\/jats:sec>\n \n Registration<\/jats:title>\n \n URL:\n https:\/\/www.clinicaltrials.gov\/study\/NCT04056104?term=NCT04056104&rank=1<\/jats:ext-link>\n ; Unique identifier: NCT04056104.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1161\/jaha.123.033786","type":"journal-article","created":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T22:53:56Z","timestamp":1718492036000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":14,"title":["Machine Learning\u2013Based Critical Congenital Heart Disease Screening Using Dual\u2010Site Pulse Oximetry Measurements"],"prefix":"10.1161","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3325-2821","authenticated-orcid":false,"given":"Heather","family":"Siefkes","sequence":"first","affiliation":[{"name":"University of California","place":["Davis"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2787-1685","authenticated-orcid":false,"given":"Luca Cerny","family":"Oliveira","sequence":"additional","affiliation":[{"name":"University of 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FDA in Brief: FDA warns about limitations and accuracy of pulse oximetry. 2021. Accessed June 16 2021. https:\/\/www.fda.gov\/news\u2010events\/fda\u2010brief\/fda\u2010brief\u2010fda\u2010warns\u2010about\u2010limitations\u2010and\u2010accuracy\u2010pulse\u2010oximeters."},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMc2029240"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.5812\/ijp-134709"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2017-017580"}],"container-title":["Journal of the American Heart Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ahajournals.org\/doi\/full\/10.1161\/JAHA.123.033786","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T04:56:24Z","timestamp":1790312184000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ahajournals.org\/doi\/10.1161\/JAHA.123.033786"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,18]]},"references-count":40,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,6,18]]}},"alternative-id":["10.1161\/JAHA.123.033786"],"URL":"https:\/\/doi.org\/10.1161\/jaha.123.033786","relation":{},"ISSN":["2047-9980"],"issn-type":[{"value":"2047-9980","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,18]]},"assertion":[{"value":"2023-12-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e033786"}}