Cian P McCarthy1, Nasrien E Ibrahim2, Roland R J van Kimmenade3, Hanna K Gaggin2,4, Mandy L Simon2, Parul Gandhi5, Noreen Kelly6, Shweta R Motiwala7, Renata Mukai2, Craig A Magaret8, Grady Barnes8, Rhonda F Rhyne8, Joseph M Garasic2, James L Januzzi2,4. 1. Department of Medicine, Massachusetts General Hospital, Boston. 2. Division of Cardiology, Massachusetts General Hospital, Boston. 3. Division of Cardiology, Maastricht University Medical Centre, Maastricht, The Netherlands. 4. Baim Institute for Clinical Research, Cardiometabolic Trials, Boston, Massachusetts. 5. Division of Cardiology, VA Connecticut Healthcare System and Yale University, New Haven, Connecticut. 6. Division of Cardiology, Brigham and Women's Hospital, Boston, Massachusetts. 7. Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts. 8. Prevencio, Inc., Washington.
Abstract
BACKGROUND: 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. HYPOTHESIS: A clinical/biomarker score may offer an attractive alternative diagnostic method for PAD. METHODS: In a prospective cohort of 354 patients referred for diagnostic peripheral and/or coronary angiography, predictors of ≥50% stenosis in ≥1 peripheral vessel (carotid/subclavian, renal, or lower extremity arteries) were identified from >50 clinical variables and 109 biomarkers. Machine learning identified variables predictive of obstructive PAD; a score derived from the final model was developed. RESULTS: The score consisted of 1 clinical variable (history of hypertension) and 6 biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1, and eotaxin-1). The model had an in-sample area under the receiver operating characteristic curve of 0.85 for obstructive PAD and a cross-validated 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 years of follow-up. CONCLUSIONS: A clinical/biomarker score demonstrates high accuracy for predicting the presence of PAD.
BACKGROUND: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. HYPOTHESIS: A clinical/biomarker score may offer an attractive alternative diagnostic method for PAD. METHODS: In a prospective cohort of 354 patients referred for diagnostic peripheral and/or coronary angiography, predictors of ≥50% stenosis in ≥1 peripheral vessel (carotid/subclavian, renal, or lower extremity arteries) were identified from >50 clinical variables and 109 biomarkers. Machine learning identified variables predictive of obstructive PAD; a score derived from the final model was developed. RESULTS: The score consisted of 1 clinical variable (history of hypertension) and 6 biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1, and eotaxin-1). The model had an in-sample area under the receiver operating characteristic curve of 0.85 for obstructive PAD and a cross-validated 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 years of follow-up. CONCLUSIONS: A clinical/biomarker score demonstrates high accuracy for predicting the presence of PAD.
Authors: Sue Duval; Joseph M Massaro; Michael R Jaff; William E Boden; Mark J Alberts; Robert M Califf; Kim A Eagle; Ralph B D'Agostino; Alison Pedley; Gregg C Fonarow; Joanne M Murabito; P Gabriel Steg; Deepak L Bhatt; Alan T Hirsch Journal: Vasc Med Date: 2012-06-17 Impact factor: 3.239
Authors: K J Haley; C M Lilly; J H Yang; Y Feng; S P Kennedy; T G Turi; J F Thompson; G H Sukhova; P Libby; R T Lee Journal: Circulation Date: 2000-10-31 Impact factor: 29.690
Authors: A T Hirsch; M H Criqui; D Treat-Jacobson; J G Regensteiner; M A Creager; J W Olin; S H Krook; D B Hunninghake; A J Comerota; M E Walsh; M M McDermott; W R Hiatt Journal: JAMA Date: 2001-09-19 Impact factor: 56.272
Authors: Christian Erbel; Thomas J Dengler; Susanne Wangler; Felix Lasitschka; Florian Bea; Nadine Wambsganss; Maani Hakimi; Dittmar Böckler; Hugo A Katus; Christian A Gleissner Journal: Basic Res Cardiol Date: 2010-12-01 Impact factor: 17.165
Authors: Victor Aboyans; Elena Ho; Julie O Denenberg; Lindsey A Ho; Loki Natarajan; Michael H Criqui Journal: J Vasc Surg Date: 2008-08-09 Impact factor: 4.268
Authors: F Gerald R Fowkes; Diana Rudan; Igor Rudan; Victor Aboyans; Julie O Denenberg; Mary M McDermott; Paul E Norman; Uchechukwe K A Sampson; Linda J Williams; George A Mensah; Michael H Criqui Journal: Lancet Date: 2013-08-01 Impact factor: 79.321
Authors: Cian P McCarthy; Shreya Shrestha; Nasrien Ibrahim; Roland R J van Kimmenade; Hanna K Gaggin; Renata Mukai; Craig Magaret; Grady Barnes; Rhonda Rhyne; Joseph M Garasic; James L Januzzi Journal: Open Heart Date: 2019-05-13