{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T18:42:20Z","timestamp":1785436940781,"version":"3.56.0"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Province Cadres Health Project","award":["2024-118"],"award-info":[{"award-number":["2024-118"]}]},{"DOI":"10.13039\/501100004912","name":"Sichuan University","doi-asserted-by":"crossref","award":["GSSCU2023089"],"award-info":[{"award-number":["GSSCU2023089"]}],"id":[{"id":"10.13039\/501100004912","id-type":"DOI","asserted-by":"crossref"}]},{"award":["GSSCU2023089"],"award-info":[{"award-number":["GSSCU2023089"]}],"id":[{"id":"https:\/\/ror.org\/011ashp19","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: The accurate prediction of postprocedural mortality is critical for clinical decision-making; however, research on mortality risk models for patients undergoing mechanical thrombectomy remains limited. This study aimed to develop and validate machine learning models for predicting 90-day post-mechanical thrombectomy mortality. Methods: A retrospective-prospective cohort study involving 699 retrospective patients (January 2019\u2013December 2022) and 274 prospective patients (January 2023\u2013June 2024) from a single institution in Sichuan was conducted. The primary outcome was all-cause mortality within 90 days, ascertained via telephone follow-up. Predictors were identified using univariate analysis and LASSO regression. Eight predictive models were developed and evaluated using existing machine learning methods via 10-fold cross-validation. Model performance was assessed through discrimination, calibration, decision curve analysis, and interpretability via Shapley additive explanations. Results: The final dataset included 593 patients in the modeling set and 247 in the validation set. The 90-day mortality rates were 25.6% and 32.0%, respectively. Key predictors included age, hyperlipidemia, atrial fibrillation, pre-stroke statin use, antiplatelet\/anticoagulant therapy within 48 h of onset, dysphagia, D-dimer levels, and activities of daily living scores. Logistic regression demonstrated superior performance in the modeling cohort (AUC = 0.87), whereas the multilayer perceptron model exhibited the greatest efficacy in the validation cohort (AUC = 0.77). Conclusions: Machine learning algorithms can accurately predict 90-day mortality among patients undergoing mechanical thrombectomy. The multilayer perceptron model demonstrated robust validation performance and offers a potential tool for personalized risk assessment and optimization of clinical decision-making.<\/jats:p>","DOI":"10.3390\/jcm15124702","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T10:19:53Z","timestamp":1781691593000},"page":"4702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction and Validation of a 90-Day Mortality Risk Prediction Model Based on Interpretable Machine Learning for Acute Ischemic Stroke Patients Undergoing Mechanical Thrombectomy"],"prefix":"10.3390","volume":"15","author":[{"given":"Qian","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Neurology, West China Hospital, Sichuan University\/West China School of Nursing, Sichuan University, Chengdu 610041, China"},{"name":"Department of Emergency, West China Tianfu Hospital, Sichuan University, Chengdu 610213, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Neurology, West China Hospital, Sichuan University\/West China School of Nursing, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6756-5685","authenticated-orcid":false,"given":"Yueyue","family":"He","sequence":"additional","affiliation":[{"name":"Department of Neurology, West China Hospital, Sichuan University\/West China School of Nursing, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingxiao","family":"He","sequence":"additional","affiliation":[{"name":"Trauma Centre of West China Hospital, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Neurology, West China Hospital, Sichuan University\/West China School of Nursing, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyu","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Neurology, West China Hospital, Sichuan University\/West China School of Nursing, Sichuan University, Chengdu 610041, China"},{"name":"Department of Neurology, West China Tianfu Hospital, Sichuan University, Chengdu 610213, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junli","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, West China Hospital, Sichuan University\/West China School of Nursing, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1792-3501","authenticated-orcid":false,"given":"Ling","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Neurology, West China Hospital, Sichuan University\/West China School of Nursing, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"ref_1","unstructured":"(2024). 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