{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T22:18:48Z","timestamp":1791757128959,"version":"4.3.4"},"reference-count":66,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":366,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jvascsurg.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Vascular Surgery"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1016\/j.jvs.2023.07.006","type":"journal-article","created":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T21:16:23Z","timestamp":1689369383000},"page":"1449-1460.e7","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":14,"title":["Predicting outcomes following open revascularization for aortoiliac occlusive disease using machine learning"],"prefix":"10.1016","volume":"78","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7191-1034","authenticated-orcid":false,"given":"Ben","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raj","family":"Verma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derek","family":"Beaton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hani","family":"Tamim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamad A.","family":"Hussain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamal J.","family":"Hoballah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas S.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duminda N.","family":"Wijeysundera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles","family":"de Mestral","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Mamdani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Al-Omran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jvs.2023.07.006_bib1","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1016\/S0140-6736(13)61249-0","article-title":"Comparison of global estimates of prevalence and risk factors for peripheral artery disease in 2000 and 2010: a systematic review and analysis","volume":"382","author":"Fowkes","year":"2013","journal-title":"Lancet Lond Engl"},{"key":"10.1016\/j.jvs.2023.07.006_bib2","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.atherosclerosis.2019.09.012","article-title":"Morbidity and mortality associated with atherosclerotic peripheral artery disease: a systematic review","volume":"293","author":"Agnelli","year":"2020","journal-title":"Atherosclerosis"},{"key":"10.1016\/j.jvs.2023.07.006_bib3","article-title":"Functional status and health-related quality of life in patients with peripheral artery disease: a cross-sectional study","volume":"18","author":"Kim","year":"2021","journal-title":"Int J Environ Res Public Health"},{"key":"10.1016\/j.jvs.2023.07.006_bib4","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.atherosclerosis.2019.05.026","article-title":"Cost and inpatient burden of peripheral artery disease: findings from the National Inpatient Sample","volume":"286","author":"Kohn","year":"2019","journal-title":"Atherosclerosis"},{"key":"10.1016\/j.jvs.2023.07.006_bib5","series-title":"StatPearls [Internet]","article-title":"Aortoiliac occlusive disease","author":"Heaton","year":"2022"},{"key":"10.1016\/j.jvs.2023.07.006_bib6","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1053\/j.semvascsurg.2022.04.005","article-title":"Aortoiliac occlusive disease","volume":"35","author":"Paisley","year":"2022","journal-title":"Semin Vasc Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib7","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jvs.2015.02.025","article-title":"Mortality and complications after aortic bifurcated bypass procedures for chronic aortoiliac occlusive disease","volume":"62","author":"Bredahl","year":"2015","journal-title":"J\u00a0Vasc Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib8","doi-asserted-by":"crossref","first-page":"3S","DOI":"10.1016\/j.jvs.2019.02.016","article-title":"Global vascular guidelines on the management of chronic limb-threatening ischemia","volume":"69","author":"Conte","year":"2019","journal-title":"J\u00a0Vasc Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib9","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1016\/j.jvs.2016.04.045","article-title":"The vascular quality initiative cardiac risk index for prediction of myocardial infarction after vascular surgery","volume":"64","author":"Bertges","year":"2016","journal-title":"J\u00a0Vasc Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib10","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s00268-006-0242-y","article-title":"Risk-scoring method for prediction of 30-day postoperative outcome after infrainguinal surgical revascularization for critical lower-limb ischemia: a Finnvasc registry study","volume":"31","author":"Biancari","year":"2007","journal-title":"World J Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib11","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1097\/SLA.0000000000004132","article-title":"Predicting the outcome of limb revascularization in patients with lower-extremity arterial trauma: development and external validation of a supervised machine-learning algorithm to support surgical decisions","volume":"272","author":"Perkins","year":"2020","journal-title":"Ann Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib12","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.jamcollsurg.2013.07.385","article-title":"Development and evaluation of the Universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons","volume":"217","author":"Bilimoria","year":"2013","journal-title":"J\u00a0Am Coll Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib13","doi-asserted-by":"crossref","DOI":"10.1136\/bmjhci-2020-100253","article-title":"Adoption of clinical risk prediction tools is limited by a lack of integration with electronic health records","volume":"28","author":"Sharma","year":"2021","journal-title":"BMJ Health Care Inform"},{"key":"10.1016\/j.jvs.2023.07.006_bib14","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1016\/j.jacc.2018.03.008","article-title":"Major adverse limb events and mortality in patients with peripheral artery disease: the COMPASS trial","volume":"71","author":"Anand","year":"2018","journal-title":"J\u00a0Am Coll Cardiol"},{"key":"10.1016\/j.jvs.2023.07.006_bib15","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/978-1-62703-748-8_7","article-title":"Introduction to machine learning","volume":"1107","author":"Ba\u015ftanlar","year":"2014","journal-title":"Methods Mol Biol"},{"key":"10.1016\/j.jvs.2023.07.006_bib16","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1038\/s41746-019-0148-3","article-title":"Artificial intelligence and machine learning in clinical development: a translational perspective","volume":"2","author":"Shah","year":"2019","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.jvs.2023.07.006_bib17","doi-asserted-by":"crossref","first-page":"e471","DOI":"10.1016\/S2589-7500(21)00084-4","article-title":"Assessing the utility of deep neural networks in predicting postoperative surgical complications: a retrospective study","volume":"3","author":"Bonde","year":"2021","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.jvs.2023.07.006_bib18","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent reporting of a multivariable prediction model for individual Prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann Intern Med"},{"key":"10.1016\/j.jvs.2023.07.006_bib20","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.jamcollsurg.2009.09.031","article-title":"Toward robust information: data quality and inter-rater reliability in the American College of surgeons national surgical quality improvement Program","volume":"210","author":"Shiloach","year":"2010","journal-title":"J\u00a0Am Coll Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib21","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.jamcollsurg.2013.02.027","article-title":"Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus","volume":"217","author":"Cohen","year":"2013","journal-title":"J\u00a0Am Coll Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib22","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.jss.2021.02.045","article-title":"Machine learning and surgical outcomes prediction: a systematic review","volume":"264","author":"Elfanagely","year":"2021","journal-title":"J\u00a0Surg Res"},{"key":"10.1016\/j.jvs.2023.07.006_bib23","doi-asserted-by":"crossref","first-page":"3100","DOI":"10.1007\/s00268-022-06728-1","article-title":"Machine learning algorithms for predicting surgical outcomes after colorectal surgery: a systematic review","volume":"46","author":"Bekta\u015f","year":"2022","journal-title":"World J Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib24","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1016\/j.wneu.2017.09.149","article-title":"Machine learning and neurosurgical outcome prediction: a systematic review","volume":"109","author":"Senders","year":"2018","journal-title":"World Neurosurg"},{"issue":"Suppl 4","key":"10.1016\/j.jvs.2023.07.006_bib25","doi-asserted-by":"crossref","first-page":"S574","DOI":"10.21037\/jtd.2019.01.25","article-title":"Developing prediction models for clinical use using logistic regression: an overview","volume":"11","author":"Shipe","year":"2019","journal-title":"J\u00a0Thorac Dis"},{"key":"10.1016\/j.jvs.2023.07.006_bib26","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/1755-8794-4-31","article-title":"Optimally splitting cases for training and testing high dimensional classifiers","volume":"4","author":"Dobbin","year":"2011","journal-title":"BMC Med Genomics"},{"key":"10.1016\/j.jvs.2023.07.006_bib27","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1080\/10485252.2015.1010532","article-title":"A\u00a0K-fold averaging cross-validation procedure","volume":"27","author":"Jung","year":"2015","journal-title":"J\u00a0Nonparametric Stat"},{"key":"10.1016\/j.jvs.2023.07.006_bib28","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.803","article-title":"Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models","volume":"8","author":"Adnan","year":"2022","journal-title":"PeerJ Comput Sci"},{"key":"10.1016\/j.jvs.2023.07.006_bib29","doi-asserted-by":"crossref","first-page":"7830","DOI":"10.1016\/j.jksuci.2021.09.021","article-title":"Pruning-based oversampling technique with smoothed bootstrap resampling for imbalanced clinical dataset of Covid-19","volume":"34","author":"Wibowo","year":"2022","journal-title":"J\u00a0King Saud Univ Comput Inf Sci"},{"key":"10.1016\/j.jvs.2023.07.006_bib30","first-page":"627","article-title":"Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation","volume":"4","author":"Hajian-Tilaki","year":"2013","journal-title":"Casp J Intern Med"},{"key":"10.1016\/j.jvs.2023.07.006_bib31","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1016\/0895-4356(91)90146-Z","article-title":"Assessing predictive accuracy: how to compare Brier scores","volume":"44","author":"Redelmeier","year":"1991","journal-title":"J\u00a0Clin Epidemiol"},{"key":"10.1016\/j.jvs.2023.07.006_bib32","first-page":"569","article-title":"Variable importance scores","volume":"19","author":"Loh","year":"2021","journal-title":"J\u00a0Data Sci"},{"key":"10.1016\/j.jvs.2023.07.006_bib33","doi-asserted-by":"crossref","first-page":"m441","DOI":"10.1136\/bmj.m441","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","year":"2020","journal-title":"BMJ"},{"key":"10.1016\/j.jvs.2023.07.006_bib34","series-title":"Pmsampsize: calculates the minimum sample size required for developing a multivariable prediction model [Internet];","author":"Ensor","year":"2022"},{"key":"10.1016\/j.jvs.2023.07.006_bib35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/096228029900800102","article-title":"Multiple imputation: a primer","volume":"8","author":"Schafer","year":"1999","journal-title":"Stat Methods Med Res"},{"key":"10.1016\/j.jvs.2023.07.006_bib36","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1093\/aje\/kwaa124","article-title":"When is a complete-case approach to missing data valid? The importance of effect-measure Modification","volume":"189","author":"Ross","year":"2020","journal-title":"Am J Epidemiol"},{"key":"10.1016\/j.jvs.2023.07.006_bib37","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1093\/ije\/dyz032","article-title":"Accounting for missing data in statistical analyses: multiple imputation is not always the answer","volume":"48","author":"Hughes","year":"2019","journal-title":"Int J Epidemiol"},{"key":"10.1016\/j.jvs.2023.07.006_bib39","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1016\/j.jvs.2018.08.195","article-title":"Prediction of postoperative myocardial infarction after suprainguinal bypass using the vascular quality initiative cardiac risk index","volume":"69","author":"Diamond","year":"2019","journal-title":"J\u00a0Vasc Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib40","first-page":"5426","article-title":"Inaccurate risk assessment by the ACS NSQIP risk calculator in aortic surgery","volume":"10","author":"Hers","year":"2021","journal-title":"J\u00a0Clin Med"},{"key":"10.1016\/j.jvs.2023.07.006_bib41","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/s41746-021-00552-y","article-title":"Machine learning in vascular surgery: a systematic review and critical appraisal","volume":"5","author":"Li","year":"2022","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.jvs.2023.07.006_bib42","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.ejvs.2019.02.010","article-title":"Comparison of early and late post-operative outcomes after supra-inguinal bypass for aortoiliac occlusive disease","volume":"58","author":"Saadeddin","year":"2019","journal-title":"Eur J Vasc Endovasc Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib43","doi-asserted-by":"crossref","first-page":"2S","DOI":"10.1016\/j.jvs.2014.12.009","article-title":"Society for Vascular Surgery practice guidelines for atherosclerotic occlusive disease of the lower extremities: management of asymptomatic disease and claudication","volume":"61","author":"Conte","year":"2015","journal-title":"J\u00a0Vasc Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib44","first-page":"e686","article-title":"2016 AHA\/ACC guideline on the management of patients with lower extremity peripheral artery disease: executive summary: a Report of the American College of cardiology\/American heart association Task Force on clinical practice guidelines","volume":"135","author":"Gerhard-Herman","year":"2017","journal-title":"Circulation"},{"key":"10.1016\/j.jvs.2023.07.006_bib45","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.ejvs.2017.07.018","article-title":"Editor\u2019s choice - 2017 ESC guidelines on the diagnosis and treatment of peripheral arterial diseases, in collaboration with the European society for vascular surgery (ESVS)","volume":"55","author":"Aboyans","year":"2018","journal-title":"Eur J Vasc Endovasc Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib46","doi-asserted-by":"crossref","first-page":"2305","DOI":"10.1056\/NEJMoa2207899","article-title":"Surgery or endovascular therapy for chronic limb-threatening ischemia","volume":"387","author":"Farber","year":"2022","journal-title":"N\u00a0Engl J Med"},{"key":"10.1016\/j.jvs.2023.07.006_bib47","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1111\/j.1553-2712.2011.01185.x","article-title":"Logistic regression: a brief primer","volume":"18","author":"Stoltzfus","year":"2011","journal-title":"Acad Emerg Med"},{"key":"10.1016\/j.jvs.2023.07.006_bib48","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0228534","article-title":"Nonlinear dynamics based machine learning: Utilizing dynamics-based flexibility of nonlinear circuits to implement different functions","volume":"15","author":"Kia","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.jvs.2023.07.006_bib49","series-title":"Nonlinear systems in healthcare towards intelligent disease prediction [Internet]. Nonlinear Systems -Theoretical Aspects and Recent Applications","author":"Chatterjee","year":"2019"},{"key":"10.1016\/j.jvs.2023.07.006_bib50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-021-00394-8","article-title":"Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data","volume":"4","author":"Ravaut","year":"2021","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.jvs.2023.07.006_bib51","doi-asserted-by":"crossref","first-page":"659","DOI":"10.2147\/NDT.S349956","article-title":"XGBoost machine learning algorithm for prediction of outcome in aneurysmal subarachnoid Hemorrhage","volume":"18","author":"Wang","year":"2022","journal-title":"Neuropsychiatr Dis Treat"},{"key":"10.1016\/j.jvs.2023.07.006_bib52","doi-asserted-by":"crossref","DOI":"10.1136\/bmjopen-2021-056685","article-title":"Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study","volume":"12","author":"Fang","year":"2022","journal-title":"BMJ Open"},{"key":"10.1016\/j.jvs.2023.07.006_bib53","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s12942-022-00304-5","article-title":"A\u00a0machine learning approach to small area estimation: predicting the health, housing and well-being of the population of Netherlands","volume":"21","author":"Viljanen","year":"2022","journal-title":"Int J Health Geogr"},{"key":"10.1016\/j.jvs.2023.07.006_bib54","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1001\/jamainternmed.2018.3763","article-title":"Potential biases in machine learning algorithms using electronic health record data","volume":"178","author":"Gianfrancesco","year":"2018","journal-title":"JAMA Intern Med"},{"key":"10.1016\/j.jvs.2023.07.006_bib55","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1002\/jso.24488","article-title":"Gender as a risk factor for adverse intraoperative and postoperative outcomes of elective pancreatectomy","volume":"115","author":"Mazmudar","year":"2017","journal-title":"J\u00a0Surg Oncol"},{"key":"10.1016\/j.jvs.2023.07.006_bib56","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1177\/10556656221078154","article-title":"The impact of race and ethnicity on surgical risk and outcomes following Palatoplasty: an analysis of the NSQIP Pediatric database","volume":"60","author":"Halsey","year":"2023","journal-title":"Cleft Palate Craniofac J"},{"key":"10.1016\/j.jvs.2023.07.006_bib57","series-title":"StatPearls [Internet]","article-title":"Operative risk","author":"Shaydakov","year":"2022"},{"key":"10.1016\/j.jvs.2023.07.006_bib58","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1161\/ATVBAHA.119.312142","article-title":"Evidence-based medical management of peripheral artery disease","volume":"40","author":"Bevan","year":"2020","journal-title":"Arterioscler Thromb Vasc Biol"},{"key":"10.1016\/j.jvs.2023.07.006_bib59","first-page":"117","article-title":"Endovascular revascularization for aortoiliac atherosclerotic disease","volume":"12","author":"Aggarwal","year":"2016","journal-title":"Vasc Health Risk Manag"},{"key":"10.1016\/j.jvs.2023.07.006_bib60","first-page":"313","article-title":"Endovascular and hybrid revascularization for complicated aorto-iliac occlusive disease: short-term results in single institute experience","volume":"34","author":"Chen","year":"2018","journal-title":"Acta Cardiol Sin"},{"key":"10.1016\/j.jvs.2023.07.006_bib61","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1097\/SLA.0000000000002014","article-title":"The clinical impact of cardiology consultation prior to major vascular surgery","volume":"267","author":"Davis","year":"2018","journal-title":"Ann Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib62","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.amjsurg.2012.02.018","article-title":"Preoperative medical consultation: maximizing its benefits","volume":"204","author":"Rivera","year":"2012","journal-title":"Am J Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib63","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1093\/bja\/aew396","article-title":"Intensive care utilization and outcomes after high-risk surgery in Scotland: a population-based cohort study","volume":"118","author":"Gillies","year":"2017","journal-title":"Br J Anaesth"},{"key":"10.1016\/j.jvs.2023.07.006_bib64","series-title":"StatPearls [Internet]","article-title":"Discharge Planning","author":"Patel","year":"2022"},{"key":"10.1016\/j.jvs.2023.07.006_bib65","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1016\/j.jvs.2010.03.027","article-title":"Analysis of large databases in vascular surgery","volume":"52","author":"Nguyen","year":"2010","journal-title":"J\u00a0Vasc Surg"},{"key":"10.1016\/j.jvs.2023.07.006_bib66","first-page":"3","article-title":"The use of Big data analytics in healthcare","volume":"9","author":"Batko","year":"2022","journal-title":"J\u00a0Big Data"},{"key":"10.1016\/j.jvs.2023.07.006_bib67","doi-asserted-by":"crossref","first-page":"1994","DOI":"10.1056\/NEJMoa2000052","article-title":"Rivaroxaban in peripheral artery disease after revascularization","volume":"382","author":"Bonaca","year":"2020","journal-title":"N\u00a0Engl J Med"},{"key":"10.1016\/j.jvs.2023.07.006_bib68","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1056\/NEJMoa1709118","article-title":"Rivaroxaban with or without aspirin in stable cardiovascular disease","volume":"377","author":"Eikelboom","year":"2017","journal-title":"N\u00a0Engl J Med"}],"container-title":["Journal of Vascular Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0741521423016142?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0741521423016142?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T20:41:38Z","timestamp":1760042498000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0741521423016142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":66,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["S0741521423016142"],"URL":"https:\/\/doi.org\/10.1016\/j.jvs.2023.07.006","relation":{},"ISSN":["0741-5214"],"issn-type":[{"value":"0741-5214","type":"print"}],"subject":[],"published":{"date-parts":[[2023,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting outcomes following open revascularization for aortoiliac occlusive disease using machine learning","name":"articletitle","label":"Article Title"},{"value":"Journal of Vascular Surgery","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jvs.2023.07.006","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 by the Society for Vascular Surgery. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}