{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T11:58:45Z","timestamp":1777550325210,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Unfractionated heparin (UFH) is a widely used therapy in intensive care units (ICUs) and is associated with an increased risk of serious adverse events or death if the therapeutic target is not reached quickly. Adjusting UFH dosage is challenging, and no reliable algorithms exist for predicting anti-Xa levels in ICUs. This study aimed to develop and evaluate machine learning algorithms to predict anti-Xa levels during UFH therapy, helping clinicians optimize dosing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This single-center retrospective cohort study was conducted using Rennes University Hospital's clinical data warehouse from December 21, 2019 to November 22, 2021. Critically ill patients\u2009\u2265\u200918 years on UFH, without other anticoagulants and complete data, were included. Anti-Xa levels were classified as infra-therapeutic (&lt;0.3), therapeutic (0.3\u20130.7), or supra-therapeutic (&gt;0.7). Models incorporated UFH rate, bolus, prior anti-Xa, kidney function, inflammation, volemic state, extracorporeal membrane oxygenation, and bilirubinemia. Performance was assessed using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, and specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 3790 anti-Xa intervals, corresponding to 211 patients, were included in the study. Out of several machine learning algorithms, random forest achieved the best results with an AUROC score of 0.80 [0.77;0.83], an AUPRC score of 0.61 [0.58;0.65], sensitivity of 0.56 [0.53;0.59] and specificity of 0.82 [0.82;0.82].<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this cohort study, machine learning-based prediction models achieved good performance for predicting anti-Xa results during UFH therapy in an ICU setting. Further validation with prospective multicenter data is needed in order to confirm the model's generalizability and support its integration into clinical practice to assist clinicians in selecting the optimal heparin dose.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076241305957","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T01:13:42Z","timestamp":1745802822000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Anti-factor Xa during unfractionated heparin therapy in critically ill patients: Development of prediction models using machine learning"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6055-6935","authenticated-orcid":false,"given":"Boris","family":"Delange","sequence":"first","affiliation":[{"name":"Service de Maladies Infectieuses et R\u00e9animation M\u00e9dicale, \rH\u00f4pital Pontchaillou, Universit\u00e9 de Rennes, Rennes, \rFrance"},{"name":"*Current affiliation: INSERM, LTSI-UMR 1099, Universit\u00e9 de Rennes, Rennes, France."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guillaume","family":"Bouzill\u00e9","sequence":"additional","affiliation":[{"name":"CHU Rennes, INSERM, LTSI-UMR 1099, Universit\u00e9 de Rennes, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pauline","family":"Guillot","sequence":"additional","affiliation":[{"name":"Service de Maladies Infectieuses et R\u00e9animation M\u00e9dicale, \rH\u00f4pital Pontchaillou, Universit\u00e9 de Rennes, Rennes, \rFrance"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana\u00eblle","family":"Bichon","sequence":"additional","affiliation":[{"name":"Service de Maladies Infectieuses et R\u00e9animation M\u00e9dicale, \rH\u00f4pital Pontchaillou, Universit\u00e9 de Rennes, Rennes, 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d\u2019H\u00e9matologie-H\u00e9mostase, Centre Hospitalo-Universitaire de Rennes, Rennes, France"},{"name":"D\u00e9partement Anesth\u00e9sie R\u00e9animation et M\u00e9decine P\u00e9riop\u00e9ratoire, Centre Hospitalier Universitaire Rennes, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mathieu","family":"Lesouhaitier","sequence":"additional","affiliation":[{"name":"Service de Maladies Infectieuses et R\u00e9animation M\u00e9dicale, \rH\u00f4pital Pontchaillou, Universit\u00e9 de Rennes, Rennes, \rFrance"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Marc","family":"Tadi\u00e9","sequence":"additional","affiliation":[{"name":"Service de Maladies Infectieuses et R\u00e9animation M\u00e9dicale, \rH\u00f4pital Pontchaillou, Universit\u00e9 de Rennes, Rennes, \rFrance"},{"name":"Facult\u00e9 de M\u00e9decine, Universit\u00e9 de Rennes 1, Unit\u00e9 INSERM CIC 1414, IFR 140, Rennes, 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