Prediction of Peripheral Artery Disease Prognosis Using Clinical and Inflammatory Biomarker Data

Abstract

Ben Li,1– 4,* Farah Shaikh,2,* Abdelrahman Zamzam,2 Ravel Raphael,2 Muzammil H Syed,2 Houssam K Younes,5 Rawand Abdin,6 Mohammad Qadura1– 3,7 1Department of Surgery, University of Toronto, Toronto, Ontario, Canada; 2Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada; 3Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; 4Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM), University of Toronto, Toronto, Ontario, Canada; 5Heart, Vascular, & Thoracic Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates; 6Department of Medicine, McMaster University, Hamilton, Ontario, Canada; 7Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada*These authors contributed equally to this workCorrespondence: Mohammad Qadura, University of Toronto, Division of Vascular Surgery, St. Michael’s Hospital, Unity Health Toronto, 30 Bond Street, Suite 7-076, Toronto, Ontario, M5B 1W8, Canada, Tel [phone], Email [email protected]: Inflammatory biomarkers associated with peripheral artery disease (PAD) have been examined separately; however, an algorithm that includes a panel of inflammatory proteins to inform prognosis of PAD could improve predictive accuracy. We developed predictive models for 2-year PAD-related major adverse limb events (MALE) using clinical/inflammatory biomarker data.Methods: We conducted a prognostic study using 2 phases (discovery/validation models). The discovery cohort included 100 PAD patients that were propensity-score matched to 100 non-PAD patients. The validation cohort included 365 patients with PAD and 144 patients without PAD (non-matched). Plasma concentrations of 29 inflammatory proteins were determined at recruitment and the cohorts were followed for 2 years. The outcome of interest was 2-year MALE (composite of major amputation, vascular intervention, or acute limb ischemia). A random forest model was trained with 10-fold cross-validation to predict 2-year MALE using the following input features: 1) clinical characteristics, 2) inflammatory biomarkers that were expressed differentially in PAD vs non-PAD patients, and 3) clinical characteristics and inflammatory biomarkers.Results: The model discovery cohort was well-matched on age, sex, and comorbidities. Of the 29 proteins tested, 5 were elevated in PAD vs non-PAD patients (MMP-7, MMP-10, IL-6, CCL2/MCP-1, and TFPI). For prognosis of 2-year MALE on the validation cohort, our model achieved AUROC 0.63 using clinical features alone and adding inflammatory biomarker levels improved performance to AUROC 0.84.Conclusion: Using clinical characteristics and inflammatory biomarker data, we developed an accurate predictive model for PAD prognosis.Plain Language Summary: Inflammatory biomarkers associated with peripheral artery disease (PAD) have been examined separately; however, an algorithm that includes an inflammatory protein panel to inform prognosis of PAD may improve predictive accuracy. We developed predictive models for 2-year major adverse limb events (MALE) using clinical characteristics (demographics, comorbidities, and medications) and a panel of 5 PAD-specific inflammatory biomarkers (MMP-7, MMP-10, IL-6, CCL2/MCP-1, and TFPI) that achieved excellent performance on an independent validation cohort (AUROC 0.84). The models developed through this study may support PAD risk-stratification and targeted management strategies.Keywords: inflammatory biomarkers, predictive model, prognosis, peripheral artery disease

Keywords

• inflammatory biomarkers
• predictive model
• prognosis
• peripheral artery disease