{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T01:01:56Z","timestamp":1790211716010,"version":"4.1.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"vor","delay-in-days":7,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"None"},{"DOI":"10.13039\/501100004702","name":"Universit\u00e0 degli Studi di Genova","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004702","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. Cardiovasc. Transl. Res."],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The aim of this study was to develop and validate a machine learning tool for predicting survival in PAD patients who received surgical treatment. We used the data from 1,615 patients who underwent PAD surgery from 2005 to 2020. Gradient boosted decision trees (GBDTs) were used to predict mortality at one, three and five years after the first surgery, while predictor importance was assessed using the SHAP values method. The area under the curve (AUC) of the receiver operating characteristic curve of the one-, three and five-year prediction models were 0.86, 0.84 and 0.80, respectively. Disease stage was the most important predictor, along with age, chronic kidney disease status, hospital length-of-stay and total number of comorbidities. Presence of dyslipidemia was slightly predictive of one- and three-year mortality. Simple clinical and demographic parameters can be used to train a GBDT model capable of predicting PAD follow-up mortality.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1007\/s12265-025-10692-1","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T13:54:16Z","timestamp":1759931656000},"page":"1459-1469","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Machine Learning Tool to Predict Survival After First Surgery in Peripheral Artery Disease Patients"],"prefix":"10.1007","volume":"18","author":[{"given":"Martina","family":"Doneda","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ettore","family":"Lanzarone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio Riccardo","family":"Pisa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bianca","family":"Pane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Pratesi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Spinella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,8]]},"reference":[{"issue":"9289","key":"10692_CR1","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1016\/S0140-6736(01)06351-6","volume":"358","author":"K Ouriel","year":"2001","unstructured":"Ouriel K. Peripheral arterial disease. Lancet. 2001;358(9289):1257\u201364.","journal-title":"Lancet"},{"issue":"20","key":"10692_CR2","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1016\/j.jacc.2018.03.008","volume":"71","author":"SS Anand","year":"2018","unstructured":"Anand SS, Caron F, Eikelboom JW, Bosch J, Dyal L, Aboyans V, et al. Major adverse limb events and mortality in patients with peripheral artery disease. J Am Coll Cardiol. 2018;71(20):2306\u201315.","journal-title":"J Am Coll Cardiol"},{"issue":"1","key":"10692_CR3","doi-asserted-by":"publisher","first-page":"S5","DOI":"10.1016\/j.jvs.2006.12.037","volume":"45","author":"L Norgren","year":"2007","unstructured":"Norgren L, Hiatt WR, Dormandy JA, Nehler MR, Harris KA, Fowkes FGR, et al. Inter-society consensus for the management of peripheral arterial disease (TASC II). J Vasc Surg. 2007;45(1):S5\u201367.","journal-title":"J Vasc Surg"},{"key":"10692_CR4","doi-asserted-by":"crossref","unstructured":"Conte, M. S., Bradbury, A. W., Kolh, P., White, J. V., Dick, F., Fitridge, R., et al. Global vascular guidelines on the management of chronic limb-threatening ischemia.\u00a0J Vasc Surg. 2019;69(6):3S\u2013 125S.e40.","DOI":"10.4103\/ijves.ijves_50_19"},{"issue":"25","key":"10692_CR5","doi-asserted-by":"publisher","first-page":"3274","DOI":"10.1016\/j.jacc.2018.09.078","volume":"72","author":"CC Low Wang","year":"2018","unstructured":"Low Wang CC, Blomster JI, Heizer G, Berger JS, Baumgartner I, Fowkes FGR, et al. Cardiovascular and limb outcomes in patients with diabetes and peripheral artery disease. J Am Coll Cardiol. 2018;72(25):3274\u201384.","journal-title":"J Am Coll Cardiol"},{"key":"10692_CR6","doi-asserted-by":"crossref","unstructured":"Nordanstig J, Behrendt C-A, Baumgartner I, Belch J, B\u00e4ck M, Fitridge R, et al. Editor\u2019s choice \u2013 European Society for Vascular Surgery (ESVS) 2024 clinical practice guidelines on the management of asymptomatic lower limb peripheral arterial disease and intermittent claudication. Eur J Vasc Endovasc Surg. 2024;67(1):9\u201396.","DOI":"10.1016\/j.ejvs.2023.08.067"},{"issue":"5","key":"10692_CR7","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1016\/j.jvs.2013.11.063","volume":"59","author":"T Mueller","year":"2014","unstructured":"Mueller T, Hinterreiter F, Luft C, Poelz W, Haltmayer M, Dieplinger B. Mortality rates and mortality predictors in patients with symptomatic peripheral artery disease stratified according to age and diabetes. J Vasc Surg. 2014;59(5):1291\u20139.","journal-title":"J Vasc Surg"},{"issue":"1","key":"10692_CR8","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.atherosclerosis.2015.07.002","volume":"242","author":"TR Wyss","year":"2015","unstructured":"Wyss TR, Adam L, Haynes AG, Kucher N, Silbernagel G, Do D-D, et al. Impact of cardiovascular risk factors on severity of peripheral artery disease. Atherosclerosis. 2015;242(1):97\u2013101.","journal-title":"Atherosclerosis"},{"issue":"1","key":"10692_CR9","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s00268-006-0242-y","volume":"31","author":"F Biancari","year":"2006","unstructured":"Biancari F, Salenius J, Heikkinen M, Luther M, Yl\u00f6nen K, Lep\u00e4ntalo M. Risk-scoring method for prediction of 30-day postoperative outcome after infrainguinal surgical revascularization for critical lower-limb ischemia: a Finnvasc registry study. World J Surg. 2006;31(1):217\u201325.","journal-title":"World J Surg"},{"issue":"5","key":"10692_CR10","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1016\/j.jvs.2010.06.101","volume":"52","author":"E Arvela","year":"2010","unstructured":"Arvela E, S\u00f6derstr\u00f6m M, Korhonen M, Halmesm\u00e4ki K, Alb\u00e4ck A, Lep\u00e4ntalo M, et al. Finnvasc score and modified prevent iii score predict long-term outcome after infrainguinal surgical and endovascular revascularization for critical limb ischemia. J Vasc Surg. 2010;52(5):1218\u201325.","journal-title":"J Vasc Surg"},{"issue":"12","key":"10692_CR11","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.1016\/j.jcin.2014.06.018","volume":"7","author":"Y Soga","year":"2014","unstructured":"Soga Y, Iida O, Takahaera M, Hirano K, Suzuki K, Kawasaki D, et al. Two-year life expectancy in patients with critical limb ischemia. JACC Cardiovasc Interv. 2014;7(12):1444\u20139.","journal-title":"JACC Cardiovasc Interv"},{"key":"10692_CR12","doi-asserted-by":"crossref","unstructured":"Simons, J. P., Schanzer, A., Flahive, J. M., Osborne, N. H., Mills, J. L., Bradbury, A. W., et al. Survival prediction in patients with chronic limb-threatening ischemia who undergo infrainguinal revascularization.\u00a0J Vasc Surg. 2019;69(6):137S\u2013151S.e3.","DOI":"10.1016\/j.jvs.2018.08.169"},{"issue":"12","key":"10692_CR13","doi-asserted-by":"publisher","first-page":"1833","DOI":"10.1161\/CIRCRESAHA.121.318224","volume":"128","author":"AM Flores","year":"2021","unstructured":"Flores AM, Demsas F, Leeper NJ, Ross EG. Leveraging machine learning and artificial intelligence to improve peripheral artery disease detection, treatment, and outcomes. Circ Res. 2021;128(12):1833\u201350.","journal-title":"Circ Res"},{"issue":"10","key":"10692_CR14","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.124.034477","volume":"13","author":"S Callegari","year":"2024","unstructured":"Callegari S, Romain G, Cleman J, Scierka L, Jacque F, Smolderen KG, et al. Long-term mortality predictors using a machine-learning approach in patients with chronic limb-threatening ischemia after peripheral vascular intervention. J Am Heart Assoc. 2024;13(10):e034477.","journal-title":"J Am Heart Assoc"},{"issue":"4","key":"10692_CR15","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1161\/CIRCULATIONAHA.110.015701","volume":"124","author":"PK Gupta","year":"2011","unstructured":"Gupta PK, Gupta H, Sundaram A, Kaushik M, Fang X, Miller WJ, et al. Development and validation of a risk calculator for prediction of cardiac risk after surgery. Circulation. 2011;124(4):381\u20137.","journal-title":"Circulation"},{"issue":"5","key":"10692_CR16","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1379","volume":"10","author":"G Stiglic","year":"2020","unstructured":"Stiglic G, Kocbek P, Fijacko N, Zitnik M, Verbert K, Cilar L. Interpretability of machine learning-based prediction models in healthcare. WIREs Data Min Knowl Discov. 2020;10(5):e1379.","journal-title":"WIREs Data Min Knowl Discov"},{"key":"10692_CR17","unstructured":"Feeny AK, Rickard J, Patel D, Toro S, Trulock KM, Park CJ, et al. Machine learning prediction of response to cardiac resynchronization therapy.\u00a0Circ Arrhythm Electrophysiol.\u00a02019;12(7):e007316."},{"key":"10692_CR18","unstructured":"European Union data (2004). Eurostat. accessed on 23\u201312\u20132024."},{"key":"10692_CR19","unstructured":"WHO (1978).\u00a0International classification of diseases: 9th revision, basic tabulation list with alphabetic index. World Health Organization."},{"key":"10692_CR20","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.avsg.2023.12.068","volume":"103","author":"G Spinella","year":"2024","unstructured":"Spinella G, Pisa FR, Boschetti GA, Finotello A, Pane B, Pratesi G, et al. Reconsidering the impact of endovascular repair on short-term and mid-term outcomes in peripheral arterial disease: a retrospective analysis. Ann Vasc Surg. 2024;103:141\u201350.","journal-title":"Ann Vasc Surg"},{"key":"10692_CR21","unstructured":"WHO (1978). International classification of procedures in medicine, volume 1. World Health Organization."},{"key":"10692_CR22","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A. (2018). Catboost: unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31."},{"key":"10692_CR23","unstructured":"Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30."},{"key":"10692_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2021.100395","author":"SD Mohanty","year":"2022","unstructured":"Mohanty SD, Lekan D, McCoy TP, Jenkins M, Manda P. Machine learning for predicting readmission risk among the frail: explainable AI for healthcare. Patterns. 2022. https:\/\/doi.org\/10.1016\/j.patter.2021.100395.","journal-title":"Patterns"},{"issue":"4","key":"10692_CR25","doi-asserted-by":"publisher","first-page":"291","DOI":"10.2174\/1389202922666210705124359","volume":"22","author":"H Habehh","year":"2021","unstructured":"Habehh H, Gohel S. Machine learning in healthcare. Curr Genomics. 2021;22(4):291.","journal-title":"Curr Genomics"},{"key":"10692_CR26","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.ijcard.2023.04.040","volume":"383","author":"Y Liu","year":"2023","unstructured":"Liu Y, Xue J, Jiang J. Application of machine learning algorithms in electronic medical records to predict amputation- free survival after first revascularization in patients with peripheral artery disease. Int J Cardiol. 2023;383:175\u201384.","journal-title":"Int J Cardiol"},{"issue":"1","key":"10692_CR27","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1177\/1526602814564370","volume":"22","author":"N Kobayashi","year":"2015","unstructured":"Kobayashi N, Hirano K, Nakano M, Muramatsu T, Tsukahara R, Ito Y, et al. Development and validation of a new scoring system to predict wound healing after endovascular therapy in critical limb ischemia with tissue loss. J Endovasc Ther. 2015;22(1):48\u201356.","journal-title":"J Endovasc Ther"},{"issue":"12","key":"10692_CR28","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCINTERVENTIONS.119.008015","volume":"12","author":"N Azuma","year":"2019","unstructured":"Azuma N, Takahara M, Kodama A, Soga Y, Terashi H, Tazaki J, et al. Predictive model for mortality risk including the wound, ischemia, foot infection classification in patients undergoing revascularization for critical limb ischemia. Circ Cardiovasc Interv. 2019;12(12):e008015.","journal-title":"Circ Cardiovasc Interv"},{"issue":"6","key":"10692_CR29","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1016\/j.jvs.2019.08.245","volume":"71","author":"JP Roijers","year":"2020","unstructured":"Roijers JP, Rakk\u00e9 YS, Hopmans CJ, Buimer MG, Ho GH, de Groot HG, et al. A mortality prediction model for elderly patients with critical limb ischemia. J Vasc Surg. 2020;71(6):2065\u201372.","journal-title":"J Vasc Surg"},{"key":"10692_CR30","doi-asserted-by":"crossref","unstructured":"de Oliveira Cerqueira L, Garcia Duarte E, de Souza Barros AL, Cerqueira JR, Boim de Ara\u00fajo WJ. WIfI classification: the society for vascular surgery lower extremity threatened limb classification system, a literature review. J Vasc Bras. 2020;19:e20190070.","DOI":"10.1590\/1677-5449.190070"},{"issue":"5","key":"10692_CR31","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1016\/j.jvs.2016.04.026","volume":"64","author":"EG Ross","year":"2016","unstructured":"Ross EG, Shah NH, Dalman RL, Nead KT, Cooke JP, Leeper NJ. The use of machine learning for the identification of peripheral artery disease and future mortality risk. J Vasc Surg. 2016;64(5):1515\u201322.","journal-title":"J Vasc Surg"},{"issue":"4","key":"10692_CR32","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1016\/j.ejvs.2023.07.055","volume":"66","author":"SR Levin","year":"2023","unstructured":"Levin SR, Farber A, Goodney PP, King EG, Eslami MH, Malas MB, et al. Five year survival in Medicare patients undergoing interventions for peripheral arterial disease: a retrospective cohort analysis of linked registry claims data. Eur J Vasc Endovasc Surg. 2023;66(4):541\u20139.","journal-title":"Eur J Vasc Endovasc Surg"},{"issue":"1","key":"10692_CR33","doi-asserted-by":"publisher","first-page":"45833","DOI":"10.1038\/srep45833","volume":"7","author":"B Rantner","year":"2017","unstructured":"Rantner B, Kollerits B, Pohlhammer J, Stadler M, Lamina C, Peric S, et al. The fate of patients with intermittent claudication in the 21st century revisited\u2013results from the CAVASIC study. Sci Rep. 2017;7(1):45833.","journal-title":"Sci Rep"},{"issue":"3","key":"10692_CR34","first-page":"133","volume":"47","author":"B Villemur","year":"2022","unstructured":"Villemur B, Roux C, Poggi J-N, Elias A, Le Hello C. Is it justified to search for cancer in patients with peripheral arterial disease? JMV-J Mal Vasc. 2022;47(3):133\u201340.","journal-title":"JMV-J Mal Vasc"},{"issue":"7187","key":"10692_CR35","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1038\/nature06846","volume":"452","author":"TE Thorgeirsson","year":"2008","unstructured":"Thorgeirsson TE, Geller F, Sulem P, Rafnar T, Wiste A, Magnusson KP, et al. A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature. 2008;452(7187):638\u201342.","journal-title":"Nature"},{"issue":"5","key":"10692_CR36","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.ejvs.2022.07.009","volume":"64","author":"N Kontopodis","year":"2022","unstructured":"Kontopodis N, Galanakis N, Charalambous S, Matsagkas M, Giannoukas AD, Tsetis D, et al. Editor\u2019s choice \u2013 endovascular aneurysm repair in high risk patients: a systematic review and meta- analysis. Eur J Vasc Endovasc Surg. 2022;64(5):461\u201374.","journal-title":"Eur J Vasc Endovasc Surg"}],"container-title":["Journal of Cardiovascular Translational Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12265-025-10692-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12265-025-10692-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12265-025-10692-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T15:12:17Z","timestamp":1763565137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12265-025-10692-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":36,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["10692"],"URL":"https:\/\/doi.org\/10.1007\/s12265-025-10692-1","relation":{},"ISSN":["1937-5387","1937-5395"],"issn-type":[{"value":"1937-5387","type":"print"},{"value":"1937-5395","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"26 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Data were processed in compliance with the Declaration of Helsinki. Ethical board approval was not required for this retrospective study. All data are collected and processed anonymously.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human Subjects\/Informed Consent"}},{"value":"The authors declare no competing conflicts of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical Trial Number"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human Ethics and Consent to Participate"}}]}}