{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T22:22:23Z","timestamp":1791498143554,"version":"4.3.4"},"reference-count":25,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2018,6,14]],"date-time":"2018-06-14T00:00:00Z","timestamp":1528934400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"Prevencio, Inc."},{"name":"Dennis and Marilyn Barry fellowship in cardiology research"},{"name":"Hutter Family Professorship in Cardiology"},{"name":"Ruth and James Clark Fund for Cardiac Research Innovation."}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Cardiology"],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Peripheral arterial disease (PAD) is a global health problem that is frequently underdiagnosed and undertreated. Noninvasive tools to predict the presence and severity of PAD have limitations including inaccuracy, cost, or need for intravenous contrast and ionizing radiation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Hypothesis<\/jats:title><jats:p>A clinical\/biomarker score may offer an attractive alternative diagnostic method for PAD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In a prospective cohort of 354 patients referred for diagnostic peripheral and\/or coronary angiography, predictors of \u226550% stenosis in \u22651 peripheral vessel (carotid\/subclavian, renal, or lower extremity arteries) were identified from &gt;50 clinical variables and 109 biomarkers. Machine learning identified variables predictive of obstructive PAD; a score derived from the final model was developed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The score consisted of 1 clinical variable (history of hypertension) and 6 biomarkers (midkine, kidney injury molecule\u20101, interleukin\u201023, follicle\u2010stimulating hormone, angiopoietin\u20101, and eotaxin\u20101). The model had an in\u2010sample area under the receiver operating characteristic curve of 0.85 for obstructive PAD and a cross\u2010validated area under the curve of 0.84; higher scores were associated with greater severity of angiographic stenosis. At optimal cutoff, the score had 65% sensitivity, 88% specificity, 76% positive predictive value (PPV), and 81% negative predictive value (NPV) for obstructive PAD and performed consistently across vascular territories. Partitioning the score into 5 levels resulted in a PPV of 86% and NPV of 98% in the highest and lowest levels, respectively. Elevated score was associated with shorter time to revascularization during 4.3\u2009years of follow\u2010up.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A clinical\/biomarker score demonstrates high accuracy for predicting the presence of PAD.<\/jats:p><\/jats:sec>","DOI":"10.1002\/clc.22939","type":"journal-article","created":{"date-parts":[[2018,6,7]],"date-time":"2018-06-07T06:02:04Z","timestamp":1528351324000},"page":"903-909","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A clinical and proteomics approach to predict the presence of obstructive peripheral arterial disease: From the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA) Study"],"prefix":"10.1002","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4944-0034","authenticated-orcid":false,"given":"Cian P.","family":"McCarthy","sequence":"first","affiliation":[{"name":"Department of Medicine Massachusetts General Hospital  Boston"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasrien E.","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Division of Cardiology Massachusetts General Hospital  Boston"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roland R.J.","family":"van Kimmenade","sequence":"additional","affiliation":[{"name":"Division of Cardiology Maastricht University Medical Centre  Maastricht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna K.","family":"Gaggin","sequence":"additional","affiliation":[{"name":"Division of Cardiology Massachusetts General Hospital  Boston"},{"name":"Baim Institute for Clinical Research Cardiometabolic Trials  Boston Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mandy L.","family":"Simon","sequence":"additional","affiliation":[{"name":"Division of Cardiology Massachusetts General Hospital  Boston"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parul","family":"Gandhi","sequence":"additional","affiliation":[{"name":"Division of Cardiology VA Connecticut Healthcare System and Yale University  New Haven Connecticut"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noreen","family":"Kelly","sequence":"additional","affiliation":[{"name":"Division of Cardiology Brigham and Women's Hospital  Boston Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shweta R.","family":"Motiwala","sequence":"additional","affiliation":[{"name":"Division of Cardiology Beth Israel Deaconess Medical Center  Boston Massachusetts"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renata","family":"Mukai","sequence":"additional","affiliation":[{"name":"Division of Cardiology Massachusetts General Hospital  Boston"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Craig A.","family":"Magaret","sequence":"additional","affiliation":[{"name":"Prevencio, Inc.  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