{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:12:18Z","timestamp":1771459938971,"version":"3.50.1"},"reference-count":23,"publisher":"Georg Thieme Verlag KG","funder":[{"name":"Japan Society for the Promotion of Science; Grant-in-Aid for Scientific Research C","award":["24K12133"],"award-info":[{"award-number":["24K12133"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Thromb Haemost"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In patients with sepsis, anticoagulant therapy is expected to have maximal efficacy when administered before the development of sepsis-induced overt disseminated intravascular coagulation (DIC). This therapeutic strategy requires a valid method for real-time, early prediction of sepsis-induced DIC, which is likely to progress to an overt stage. We aimed to develop machine learning prediction models for overt DIC, based on the International Society on Thrombosis and Haemostasis criteria, using a large-scale electronic medical record database.<\/jats:p>\n                  <jats:p>This multi-center, retrospective, observational study included adult patients with sepsis without overt DIC at day 1. The outcome was the development of overt DIC 24\u2009hours after a certain time point. Predictive features were baseline data and time series data within 7 days postadmission. We constructed three separate models (minimum, compact, and full models), each incorporating increasingly comprehensive sets of features. Prediction models were constructed with machine learning algorithms, including eXtreme Gradient Boosting (XGBoost) and gradient boosting machine (GBM), and evaluated on a randomly selected 20% test set.<\/jats:p>\n                  <jats:p>Among 7,532 patients with sepsis, 766 developed overt DIC within 7 days. XGBoost and GBM achieved the highest prediction accuracy. The full, compact, and minimum models on the test set exhibited area under the receiver operating characteristic curve values of 0.914, 0.884, and 0.851, respectively. The full XGBoost model achieved an area under the precision\u2013recall curve of 0.295; at 80% recall, its precision was 14.4%.<\/jats:p>\n                  <jats:p>The machine learning model exhibited high accuracy in predicting overt DIC at a clinically reliable level.<\/jats:p>","DOI":"10.1055\/a-2806-3756","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T00:28:16Z","timestamp":1770769696000},"source":"Crossref","is-referenced-by-count":0,"title":["Practical Machine Learning Model for Early and Accurate Prediction of Disseminated Intravascular Coagulation Before Its Progression to an OVERT Stage"],"prefix":"10.1055","author":[{"given":"Yutaka","family":"Umemura","sequence":"additional","affiliation":[{"name":"Department of Traumatology and Acute Critical Medicine, The University of Osaka Graduate School of Medicine, Osaka, Japan"},{"name":"Division of Trauma and Surgical Critical Care, Osaka General Medical Center, Osaka, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masataka","family":"Fujimoto","sequence":"additional","affiliation":[{"name":"School of Medicine and Health Sciences, College of Medical Sciences, University of Tsukuba, Tsukuba, Japan"},{"name":"MeDiCU, Inc., Osaka, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takahiro","family":"Kinoshita","sequence":"additional","affiliation":[{"name":"MeDiCU, Inc., Osaka, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Satoshi","family":"Fujimi","sequence":"additional","affiliation":[{"name":"Division of Trauma and Surgical Critical Care, Osaka General Medical Center, Osaka, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazuma","family":"Yamakawa","sequence":"additional","affiliation":[{"name":"Department of Emergency and Critical Care Medicine, Osaka Medical and Pharmaceutical University, Takatsuki, Osaka, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Oda","sequence":"additional","affiliation":[{"name":"Department of Traumatology and Acute Critical Medicine, The University of Osaka Graduate School of Medicine, Osaka, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"issue":"08","key":"ref1","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The Third International Consensus definitions for sepsis and septic shock (Sepsis-3)","volume":"315","author":"M Singer","year":"2016","journal-title":"JAMA"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/S0140-6736(19)32989-7","article-title":"Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study","volume":"395","author":"K E Rudd","year":"2020","journal-title":"Lancet"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.thromres.2019.04.025","article-title":"Role of disseminated intravascular coagulation in severe sepsis","volume":"178","author":"S Gando","year":"2019","journal-title":"Thromb Res"},{"issue":"07","key":"ref4","doi-asserted-by":"crossref","first-page":"2356","DOI":"10.1016\/j.jtha.2025.03.038","article-title":"Updated definition and scoring of disseminated intravascular coagulation in 2025: communication from the ISTH SSC subcommittee on disseminated intravascular coagulation","volume":"23","author":"T Iba","year":"2025","journal-title":"J Thromb Haemost"},{"issue":"03","key":"ref5","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1007\/s00134-017-4683-6","article-title":"Surviving Sepsis Campaign: international guidelines for management of sepsis and septic shock: 2016","volume":"43","author":"A Rhodes","year":"2017","journal-title":"Intensive Care 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Forrest","year":"2023","journal-title":"Lancet"},{"issue":"07","key":"ref12","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1038\/s41569-020-00503-2","article-title":"Artificial intelligence-enhanced electrocardiography in cardiovascular disease management","volume":"18","author":"K C Siontis","year":"2021","journal-title":"Nat Rev Cardiol"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"106838","DOI":"10.1016\/j.ijsu.2022.106838","article-title":"Artificial intelligence for predicting survival following deceased donor liver transplantation: retrospective multi-center study","volume":"105","author":"Y D Yu","year":"2022","journal-title":"Int J Surg"},{"issue":"05","key":"ref14","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1007\/s00415-019-09518-3","article-title":"Artificial intelligence as an emerging technology in the current care of neurological disorders","volume":"268","author":"U K Patel","year":"2021","journal-title":"J 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severe sepsis","volume":"17","author":"S Gando","year":"2013","journal-title":"Crit Care"},{"key":"ref18","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"S M Lundberg","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"issue":"07","key":"ref19","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1177\/0885066618773679","article-title":"Newly proposed sepsis-induced coagulopathy precedes International Society on Thrombosis and Haemostasis overt-disseminated intravascular coagulation and predicts high mortality","volume":"35","author":"T Iba","year":"2020","journal-title":"J Intensive Care Med"},{"issue":"03","key":"ref20","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1177\/107602960300900308","article-title":"Increased soluble fibrin in plasma of patients with disseminated intravascular coagulation","volume":"9","author":"H Wada","year":"2003","journal-title":"Clin Appl Thromb 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Haemostasis"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-2806-3756.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T23:58:12Z","timestamp":1771459092000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-2806-3756"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,10]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1055\/a-2806-3756","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["0340-6245","2567-689X"],"issn-type":[{"value":"0340-6245","type":"print"},{"value":"2567-689X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,2,10]]},"published":{"date-parts":[[2026,2,10]]}}}