{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T15:02:21Z","timestamp":1784732541698,"version":"3.55.0"},"reference-count":22,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Wuhan Science and Technology Bureau Basic Research Project","award":["2023020201010191"],"award-info":[{"award-number":["2023020201010191"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Clin Appl Thromb Hemost"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate machine learning models for early prediction of sepsis-induced coagulation dysfunction (SIC) risk at intensive care unit (ICU) admission, before diagnostic criteria are fully met.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective cohort study enrolled 197 septic ICU patients. The cohort was randomly split into training (n=159, 80.7%) and internal validation sets (n=38, 19.3%). An independent external cohort of 91patients served for validation. Feature screening used least absolute shrinkage and selection operator (LASSO) followed by multivariate logistic regression (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.05). Four models were built and optimized via 10-fold cross validation. Models were evaluated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA); The optimal model was interpreted by shapley additive explanations (SHAP).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      57.9% of patients developed SIC. Prothrombin time (PT), diastolic blood pressure (DBP), activated partial thromboplastin time (APTT), D-dimer (DD), white blood cell count (WBC), renal insufficiency (RI), and partial pressure of carbon dioxide (pCO\n                      <jats:sub>2<\/jats:sub>\n                      ) were identified as influencing factors for SIC. Prolonged PT and APTT, elevated DD, RI, and higher pCO\n                      <jats:sub>2<\/jats:sub>\n                      increased SIC risk, while higher DBP and WBC were protective. The random forest (RF) model achieved the best predictive efficiency, with internal and external validation AUCs of 0.76 and 0.79.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The RF model presents moderate predictive performance for SIC risk, suggesting its potential utility for early identification of high-risk patients upon ICU admission. Further prospective validation is needed before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10760296261468959","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T14:19:34Z","timestamp":1784557174000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and Validation of an Early Risk Prediction Model for Sepsis-Induced Coagulopathy Based on Machine Learning"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6134-0653","authenticated-orcid":false,"given":"Qiuxiang","family":"Yang","sequence":"first","affiliation":[{"name":"Wuhan Third Hospital (Tongren Hospital of Wuhan University)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Fang","sequence":"additional","affiliation":[{"name":"Wuhan Third Hospital (Tongren Hospital of Wuhan University)"},{"name":"Wuhan University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caiyi","family":"Ren","sequence":"additional","affiliation":[{"name":"Wuhan Third Hospital (Tongren Hospital of Wuhan University)"},{"name":"Hubei University of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanjuan","family":"Xiong","sequence":"additional","affiliation":[{"name":"Wuhan Third Hospital (Tongren Hospital of Wuhan University)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jili","family":"Zou","sequence":"additional","affiliation":[{"name":"Wuhan Third Hospital (Tongren Hospital of Wuhan University)"},{"name":"Wuhan University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luyu","family":"Yang","sequence":"additional","affiliation":[{"name":"Wuhan Third Hospital (Tongren Hospital of Wuhan University)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyuan","family":"Zeng","sequence":"additional","affiliation":[{"name":"Wuhan Third Hospital (Tongren Hospital of Wuhan University)"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtha.2023.05.028"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13613-025-01434-2"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.2147\/JIR.S504184"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/10760296241257517"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13613-025-01594-1"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13613-025-01441-3"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.2196\/66612"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtha.2025.03.038"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1213\/ANE.0000000000006888"},{"issue":"11","key":"e_1_3_5_11_2","first-page":"1221","article-title":"Chinese expert consensus on diagnosis and management of sepsis-induced coagulopathy (2024 edition)","volume":"49","author":"Song JC","year":"2024","unstructured":"Song JC, Ding RY, Lyu B, et al. Chinese expert consensus on diagnosis and management of sepsis-induced coagulopathy (2024 edition). Med J Chin People\u2019s Liberation Army. 2024;49(11):1221-1236.","journal-title":"Med J Chin People\u2019s Liberation Army"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1097\/MBC.0000000000000755"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.2196\/62932"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.109251"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2020.637434"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1552265"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2025.1681621"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-023-02779-w"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.1055\/s-0042-1756196"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.202102-104OC"},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.1111\/liv.15132"},{"issue":"1","key":"e_1_3_5_22_2","first-page":"1","article-title":"Elevated plasma D-dimer level and the presence of septic shock in patients with sepsis: a cross-sectional study","volume":"53","author":"Hidayat M","year":"2025","unstructured":"Hidayat M, Irawan B, Soenarto RF, et al. Elevated plasma D-dimer level and the presence of septic shock in patients with sepsis: a cross-sectional study. J Int Med Res. 2025;53(1):1-9.","journal-title":"J Int Med Res"},{"key":"e_1_3_5_23_2","doi-asserted-by":"publisher","DOI":"10.2147\/IJGM.S459583"}],"container-title":["Clinical and Applied Thrombosis\/Hemostasis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/10760296261468959","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/10760296261468959","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/10760296261468959","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T14:17:09Z","timestamp":1784729829000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/10760296261468959"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":22,"alternative-id":["10.1177\/10760296261468959"],"URL":"https:\/\/doi.org\/10.1177\/10760296261468959","relation":{},"ISSN":["1076-0296","1938-2723"],"issn-type":[{"value":"1076-0296","type":"print"},{"value":"1938-2723","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"article-number":"10760296261468959"}}