{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T17:21:09Z","timestamp":1790961669644,"version":"4.1.0"},"reference-count":52,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2024,5,21]]},"abstract":"<jats:sec sec-type=\"background\" xml:lang=\"en\">\n            <jats:title>Background<\/jats:title>\n            <jats:p xml:lang=\"en\">Patients with chronic limb\u2010threatening ischemia (CLTI) face a high long\u2010term mortality risk. Identifying novel mortality predictors and risk profiles would enable individual health care plan design and improved survival. We aimed to leverage a random survival forest machine\u2010learning algorithm to identify long\u2010term all\u2010cause mortality predictors in patients with CLTI undergoing peripheral vascular intervention.<\/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\">Patients with CLTI undergoing peripheral vascular intervention from 2017 to 2018 were derived from the Medicare\u2010linked VQI (Vascular Quality Initiative) registry. We constructed a random survival forest to rank 66 preprocedural variables according to their relative importance and mean minimal depth for 3\u2010year all\u2010cause mortality. A random survival forest of 2000 trees was built using a training sample (80% of the cohort). Accuracy was assessed in a testing sample (20%) using continuous ranked probability score, Harrell C\u2010index, and out\u2010of\u2010bag error rate. A total of 10\u2009114 patients were included (mean\u00b1SD age, 72.0\u00b111.0\u2009years; 59% men). The 3\u2010year mortality rate was 39.1%, with a median survival of 1.4\u2009years (interquartile range, 0.7\u20132.0\u2009years). The most predictive variables were chronic kidney disease, age, congestive heart failure, dementia, arrhythmias, requiring assisted care, living at home, and body mass index. A total of 41 variables spanning all domains of the biopsychosocial model were ranked as mortality predictors. The accuracy of the model was excellent (continuous ranked probability score, 0.172; Harrell C\u2010index, 0.70; out\u2010of\u2010bag error rate, 29.7%).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p xml:lang=\"en\">Our random survival forest accurately predicts long\u2010term CLTI mortality, which is driven by demographic, functional, behavioral, and medical comorbidities. Broadening frameworks of risk and refining health care plans to include multidimensional risk factors could improve individualized care for CLTI.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/jaha.124.034477","type":"journal-article","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T10:27:44Z","timestamp":1716028064000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":16,"title":["Long\u2010Term Mortality Predictors Using a Machine\u2010Learning Approach in Patients With Chronic Limb\u2010Threatening Ischemia After Peripheral Vascular Intervention"],"prefix":"10.1161","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3317-1845","authenticated-orcid":false,"given":"Santiago","family":"Callegari","sequence":"first","affiliation":[{"name":"Vascular Medicine Outcomes Program Yale University New Haven CT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4294-5507","authenticated-orcid":false,"given":"Ga\u00eblle","family":"Romain","sequence":"additional","affiliation":[{"name":"Vascular Medicine Outcomes Program Yale University New Haven CT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6143-0237","authenticated-orcid":false,"given":"Jacob","family":"Cleman","sequence":"additional","affiliation":[{"name":"Vascular Medicine Outcomes Program Yale University New Haven CT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1338-0589","authenticated-orcid":false,"given":"Lindsey","family":"Scierka","sequence":"additional","affiliation":[{"name":"Vascular Medicine Outcomes Program Yale University New Haven CT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0692-3198","authenticated-orcid":false,"given":"Francky","family":"Jacque","sequence":"additional","affiliation":[{"name":"Vascular Medicine Outcomes Program Yale University New Haven CT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6104-6254","authenticated-orcid":false,"given":"Kim G.","family":"Smolderen","sequence":"additional","affiliation":[{"name":"Vascular Medicine Outcomes Program Yale University New Haven CT"},{"name":"Department of Psychiatry Yale School of Medicine New Haven CT"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4954-5672","authenticated-orcid":false,"given":"Carlos","family":"Mena\u2010Hurtado","sequence":"additional","affiliation":[{"name":"Vascular Medicine Outcomes Program Yale University New Haven CT"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(13)61249-0"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2009.05.061"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2023.01.200"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2015.07.005"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(05)67704-5"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2022.06.026"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.120.006550"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2021.09.025"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2022.09.008"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2022.09.098"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.8741"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(23)00462-2"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.avsg.2014.06.004"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2019.02.016"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.120.018183"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2023.04.040"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychores.2023.111207"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2023.01.025"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1037\/hea0001152"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvs.2012.03.016"},{"key":"e_1_3_1_22_2","unstructured":"Surgery SoV . 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