{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T22:18:50Z","timestamp":1791757130830,"version":"4.3.4"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Patients with peripheral artery disease (PAD) face high postoperative mortality risks, necessitating precise risk stratification. While machine learning offers superior performance, its black-box nature limits clinical utility, and the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) remains controversial.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 610 surgically managed PAD patients were enrolled (median follow-up: 4 years) and randomly split into training (70%) and test (30%) sets. Six machine learning algorithms were constructed and optimized. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The sHapley additive exPlanations (SHAP) were employed for model interpretation and visualizing nonlinear relationships.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The random forest model achieved optimal performance (test set AUC\u2009=\u20090.814) with significant clinical net benefit. SHAP analysis identified age, prothrombin activity, and Rutherford classification as top predictors. Notably, while multivariate Cox regression failed to identify NLR as a linear predictor, SHAP dependence plots revealed a distinct nonlinear pattern: risk contribution increased sharply at low standardized NLR values before plateauing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We established an interpretable random forest model for predicting postoperative mortality in PAD. By integrating SHAP analysis, this study validates the nonlinear prognostic significance of NLR and demonstrates how explainable ML can complement traditional statistics for individualized risk assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1879240","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T04:21:55Z","timestamp":1786335715000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of mortality risk in patients with peripheral artery disease using interpretable machine learning"],"prefix":"10.3389","volume":"13","author":[{"given":"Yifei","family":"Li","sequence":"first","affiliation":[{"name":"Department of Vascular Surgery, The Second Hospital of Shanxi Medical University","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, The Ninth Clinical Medical College of Shanxi Medical University (Taiyuan Central Hospital)","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College","place":["Beijing, China"]},{"name":"Graduate School of Chinese Academy of Medical Sciences & Peking Union Medical College","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyuan","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, The Second Hospital of Shanxi Medical University","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, The Second Hospital of Shanxi Medical University","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, The Second Hospital of Shanxi Medical University","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, The Second Hospital of Shanxi Medical University","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"WenQi","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, The Second Hospital of Shanxi Medical University","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuoguan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lishuang","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, The Second Hospital of Shanxi Medical University","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Vascular Surgery, The Second Hospital of Shanxi Medical University","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e1313","DOI":"10.1161\/CIR.0000000000001251","article-title":"2024 ACC\/AHA\/AACVPR\/APMA\/ABC\/SCAI\/SVM\/SVN\/SVS\/SIR\/VESS guideline for the management of lower extremity peripheral artery disease: a report of the American College of Cardiology\/American Heart Association joint committee on clinical practice guidelines","volume":"149","author":"Gornik","year":"2024","journal-title":"Circulation"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1818","DOI":"10.1161\/CIRCRESAHA.121.318535","article-title":"Epidemiology of peripheral artery disease and polyvascular disease","volume":"128","author":"Aday","year":"2021","journal-title":"Circ Res"},{"key":"B3","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1177\/1358863X241281699","article-title":"Neutrophil-to-lymphocyte ratio as a prognostic biomarker in patients with peripheral artery disease: a systematic review and meta-analysis","volume":"29","author":"Kurniawan","year":"2024","journal-title":"Vasc Med"},{"key":"B4","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.cca.2015.04.020","article-title":"Association between cardiometabolic index and atherosclerotic progression in patients with peripheral arterial disease","volume":"446","author":"Wakabayashi","year":"2015","journal-title":"Clin Chim Acta"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1868","DOI":"10.1161\/CIRCRESAHA.121.318258","article-title":"Contemporary medical management of peripheral artery disease","volume":"128","author":"Bonaca","year":"2021","journal-title":"Circ Res"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1016\/j.cell.2022.04.004","article-title":"Atherosclerosis: recent developments","volume":"185","author":"Bj\u00f6rkegren","year":"2022","journal-title":"Cell"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.3390\/medicina59091557","article-title":"The role played by novel inflammatory markers in assessment of peripheral artery disease","volume":"59","author":"Onofrei","year":"2023","journal-title":"Medicina (Mex)"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1234142","DOI":"10.3389\/fimmu.2023.1234142","article-title":"Neutrophil to lymphocyte ratio as a predictor for immune-related adverse events in cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis","volume":"14","author":"Zhang","year":"2023","journal-title":"Front Immunol"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.3892\/ijmm.2020.4644","article-title":"Platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio and monocyte-to-HDL cholesterol ratio as markers of peripheral artery disease in elderly patients","volume":"46","author":"Selvaggio","year":"2020","journal-title":"Int J Mol Med"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.3390\/life13091861","article-title":"Inflammatory markers used as predictors of subclinical atherosclerosis in patients with diabetic polyneuropathy","volume":"13","author":"Mure\u0219an","year":"2023","journal-title":"Life"},{"key":"B11","doi-asserted-by":"publisher","first-page":"4050","DOI":"10.1111\/iwj.14296","article-title":"Risk factor analysis for diabetic foot ulcer-related amputation including controlling nutritional Status score and neutrophil-to-lymphocyte ratio","volume":"20","author":"Zhu","year":"2023","journal-title":"Int Wound J"},{"key":"B12","doi-asserted-by":"publisher","first-page":"211","DOI":"10.3390\/jcm14010211","article-title":"Preoperative neutrophil-to-lymphocyte ratio as a predictor of clinical outcomes in patients undergoing femoral endarterectomy","volume":"14","author":"Yamamoto","year":"2025","journal-title":"J Clin Med"},{"key":"B13","doi-asserted-by":"publisher","first-page":"110744","DOI":"10.1016\/j.compbiomed.2025.110744","article-title":"Applications of machine learning for peripheral artery disease diagnosis and management: a systematic review","volume":"196","author":"Aant","year":"2025","journal-title":"Comput Biol Med"},{"key":"B14","doi-asserted-by":"publisher","first-page":"588","DOI":"10.1016\/j.cjca.2022.01.026","article-title":"Inflammation as a mechanism and therapeutic target in peripheral artery disease","volume":"38","author":"Chan","year":"2022","journal-title":"Can J Cardiol"},{"key":"B15","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1161\/HYPERTENSIONAHA.124.19468","article-title":"AI, machine learning, and ChatGPT in hypertension","volume":"81","author":"Layton","year":"2024","journal-title":"Hypertension"},{"key":"B16","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1139\/gen-2020-0131","article-title":"Machine learning for precision medicine","volume":"64","author":"MacEachern","year":"2021","journal-title":"Genome"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1320335","DOI":"10.3389\/fendo.2024.1320335","article-title":"Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation","volume":"15","author":"Liu","year":"2024","journal-title":"Front Endocrinol"},{"key":"B18","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.jvs.2022.07.160","article-title":"Applications of artificial intelligence for patients with peripheral artery disease","volume":"77","author":"Lareyre","year":"2023","journal-title":"J Vasc Surg"},{"key":"B19","doi-asserted-by":"publisher","first-page":"465","DOI":"10.3390\/biomimetics9080465","article-title":"Current applications and future perspectives of artificial and biomimetic intelligence in vascular surgery and peripheral artery disease","volume":"9","author":"Martelli","year":"2024","journal-title":"Biomimetics"},{"key":"B20","doi-asserted-by":"publisher","first-page":"813524","DOI":"10.3389\/fvets.2022.813524","article-title":"Prognostic value of neutrophil-to-lymphocyte ratio in cats with hypertrophic cardiomyopathy","volume":"9","author":"Fries","year":"2022","journal-title":"Front Vet Sci"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1016\/j.jvs.2022.12.015","article-title":"The global burden of peripheral artery disease","volume":"77","author":"Eid","year":"2023","journal-title":"J Vasc Surg"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1186\/s12885-024-13354-8","article-title":"Establishment and clinical application of a prognostic index for inflammatory status in triple-negative breast cancer patients undergoing neoadjuvant therapy using machine learning","volume":"24","author":"Sun","year":"2024","journal-title":"BMC Cancer"},{"key":"B23","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1016\/j.jvs.2025.05.022","article-title":"Interpretable machine learning prediction model for major adverse cardiovascular events in patients with peripheral artery disease","volume":"82","author":"Song","year":"2025","journal-title":"J Vasc Surg"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1161\/ATVBAHA.124.320195","article-title":"Peripheral artery disease: molecular mechanisms and novel therapies","volume":"44","author":"Athavale","year":"2024","journal-title":"Arterioscler Thromb Vasc Biol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1056\/NEJMcp2501200","article-title":"Peripheral artery disease in the legs","volume":"394","author":"McDermott","year":"2026","journal-title":"N Engl J Med"},{"key":"B26","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.ejim.2022.05.004","article-title":"Low serum albumin: a neglected predictor in patients with cardiovascular disease","volume":"102","author":"Manolis","year":"2022","journal-title":"Eur J Intern Med"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1434580","DOI":"10.3389\/fendo.2024.1434580","article-title":"Neutrophil-to-lymphocyte ratio in type 2 diabetes patients combined with lower extremity peripheral artery disease","volume":"15","author":"Li","year":"2024","journal-title":"Front Endocrinol"},{"key":"B28","doi-asserted-by":"publisher","first-page":"5940","DOI":"10.3390\/jcm13195940","article-title":"Evaluation of inflammatory markers in predicting coronary complexity: insights from the SYNTAX II score in NSTEMI patients","volume":"13","author":"Bilgin","year":"2024","journal-title":"J Clin Med"}],"container-title":["Frontiers in Cardiovascular Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2026.1879240\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T04:21:56Z","timestamp":1786335716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2026.1879240\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,10]]},"references-count":28,"alternative-id":["10.3389\/fcvm.2026.1879240"],"URL":"https:\/\/doi.org\/10.3389\/fcvm.2026.1879240","relation":{},"ISSN":["2297-055X"],"issn-type":[{"value":"2297-055X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,10]]},"article-number":"1879240"}}