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.