Development and evaluation of a prediction model for peripheral artery disease-related major adverse limb events using novel biomarker data

Background Prognostic tools for individuals with peripheral artery disease (PAD) remain limited. We developed prediction models for 3-year PAD related major adverse limb events (MALE) using demographic, clinical, and biomarker data previously validated by our group. Methods We performed a prognostic study using a prospectively recruited cohort of PAD patients (n = 569). Demographic/clinical data were recorded including sex, age, comorbidities, previous procedures, and medications. Plasma concentrations of 3 biomarkers (N-terminal pro-B-type natriuretic peptide [NT-proBNP], fatty acid binding protein 3 [FABP3], and FABP4) were measured at baseline. The cohort was followed for 3 years. MALE was the primary outcome (composite of open/endovascular vascular intervention or major amputation). We trained 3 machine learning models with 10-fold cross-validation using demographic, clinical, and biomarker data (random forest, decision trees, and Extreme Gradient Boosting [XGBoost]) to predict 3-year MALE in patients. Area under the receiver operating characteristic curve (AUROC) was the primary model evaluation metric. Results Three-year MALE was observed in 162 (29%) patients. XGBoost was the top-performing predictive model for 3-year MALE, achieving the following performance metrics: AUROC 0.88 (95% CI 0.84 – 0.94), sensitivity 88%, specificity 84%, positive predictive value 83%, and negative predictive value 91% on test set data. On an independent validation cohort of PAD patients, XGBoost attained an AUROC of 0.87 (95% CI 0.82 – 0.90). The 10 most important predictors of 3-year MALE consisted of: 1) FABP3, 2) FABP4, 3) age, 4) NT-proBNP, 5) active smoking, 6) diabetes, 7) hypertension, 8) dyslipidemia, 9) coronary artery disease, and 10) sex. Conclusions We built robust ML algorithms that accurately predict 3-year MALE in PAD patients using demographic, clinical, and novel biomarker data. Our algorithms can support risk-stratification of patients with PAD for additional vascular evaluation and early aggressive medical management, thereby improving outcomes. Further validation of our models for clinical implementation is warranted.