{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:55:36Z","timestamp":1792000536664,"version":"4.4.0"},"reference-count":89,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"9","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2024,5,7]]},"abstract":"<jats:sec sec-type=\"background\" xml:lang=\"en\">\n            <jats:title>Background<\/jats:title>\n            <jats:p xml:lang=\"en\">Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30\u2010day outcomes following lower extremity endovascular revascularization.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Methods and Results<\/jats:title>\n            <jats:p xml:lang=\"en\">The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic\/clinical variables. The primary outcome was 30\u2010day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10\u2010fold cross\u2010validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21\u2009886 patients were included, and 30\u2010day major adverse limb event\/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30\u2010day major adverse limb event\/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92\u20130.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70\u20130.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb\u2010threatening ischemia, (2) tibial intervention, and (3) congestive heart failure.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p xml:lang=\"en\">Our machine learning models accurately predict 30\u2010day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/jaha.123.033194","type":"journal-article","created":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T11:10:36Z","timestamp":1713525036000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":19,"title":["Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning"],"prefix":"10.1161","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7191-1034","authenticated-orcid":false,"given":"Ben","family":"Li","sequence":"first","affiliation":[{"name":"Department of Surgery University of Toronto Canada"},{"name":"Division of Vascular Surgery St. Michael\u2019s Hospital, Unity Health Toronto, University of Toronto Toronto Canada"},{"name":"Institute of Medical Science, University of Toronto Toronto Canada"},{"name":"Temerty Centre for Artificial Intelligence Research and Education in Medicine (T\u2010CAIREM) University of Toronto Toronto Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6543-522X","authenticated-orcid":false,"given":"Badr","family":"Aljabri","sequence":"additional","affiliation":[{"name":"Department of Surgery King Saud University Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raj","family":"Verma","sequence":"additional","affiliation":[{"name":"School of Medicine, Royal College of Surgeons in Ireland University of Medicine and Health Sciences Dublin Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6118-4366","authenticated-orcid":false,"given":"Derek","family":"Beaton","sequence":"additional","affiliation":[{"name":"Data Science &amp; 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