{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T15:14:41Z","timestamp":1781190881694,"version":"3.54.1"},"reference-count":47,"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"}],"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>Objectives<\/jats:title>\n                    <jats:p>To develop a machine learning (ML)-based prognostic model for predicting the risk of lower extremity deep vein thrombosis (DVT) after acute stroke, with an emphasis on limb functional assessments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective analysis of 225 acute stroke patients admitted within 15 days of onset between December 1, 2015, and April 30, 2025. Predictor variables were selected using collinearity diagnostics and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Three machine learning survival models\u2014Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and Generalized Linear Model (GLM)\u2014were employed to identify the most effective model. The performance of the optimal ML model was compared with that of the traditional Cox proportional hazards model using the concordance index (C-index), cumulative\/dynamic area under the curve (C\/D AUC), and integrated Brier score (IBS). The optimal model was further interpreted using SurvSHAP(t).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Six variables were selected for the model: age, stroke type, gender, tension of the muscle, Brunnstrom stage (lower limb), and sitting balance. The RSF model, implemented using the Ranger algorithm, demonstrated superior performance, with an integrated Brier score (IBS) of 0.081 and a C-index of 0.841. Age and Brunnstrom stage (lower limb) were identified as the most influential predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed an ML-based prognostic model for predicting the risk of lower limb DVT after acute stroke. Age and Brunnstrom stage (lower limb) were the most significant predictors. This model shows promise for risk stratification in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10760296261458525","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T14:47:33Z","timestamp":1781016453000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Machine Learning-Based Prognostic Model for Lower Extremity Deep Vein Thrombosis Following Acute Stroke"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4392-3676","authenticated-orcid":false,"given":"Lingling","family":"Liu","sequence":"first","affiliation":[{"name":"The First Affiliated Hospital of Nanjing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Zhou","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Nanjing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaodan","family":"Gan","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Nanjing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Lu","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Nanjing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangxu","family":"Xu","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Nanjing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7823-5899","authenticated-orcid":false,"given":"Qian","family":"Ye","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Nanjing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(19)30034-1"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12893-022-01714-9"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/10760296221121282"},{"key":"e_1_3_5_5_2","doi-asserted-by":"publisher","DOI":"10.1161\/hs0901.094253"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1136\/svn-2019-000321"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1161\/STR.0000000000000211"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12877-021-02088-y"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000001741"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clineuro.2021.106638"},{"key":"e_1_3_5_11_2","first-page":"107602961986853","article-title":"Clinical Risk Factors of Asymptomatic Deep Venous Thrombosis in Patients With Acute Stroke","volume":"25","author":"Wang Y","year":"2019","unstructured":"Wang Y, Shi Y, Dong Y, Dong Q, Fang K. 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