Development and Validation of Overall Survival and Amputation-Free Survival Prediction Models for Patients with...

To identify the most valuable predictor associated with overall survival (OS) and amputation-free survival (AFS) and to develop and validate OS and AFS prediction models for patients with peripheral arterial disease (PAD) who underwent endovascular therapy (EVT).

This study included patient with PAD who EVT between January 2018 to June 2023 across 3 hospitals in southwest China. Outcomes were OS and AFS at 1, 2, and 3 years.

A total of 2792 consecutive patients with PAD who underwent EVT were included, with an average follow-up period was 14.0 ± 8.7 months. During the follow-up period, 412 (14.8%) patients died, and 737 (26.4%) patients experienced either amputation or death. The OS rates in 1-, 2-, and 3-years were 89.3%, 81.2%, and 74.2% in the whole cohort, respectively. In terms of AFS, the rates at 1-, 2-, and 3-years were 81.7%, 68.3%, and 54.1%, respectively. Predictors in the OS prediction model included age, diabetes mellitus (DM), body mass index (BMI) classification, dialysis-dependent renal failure (DRF), Rutherford classification, antiplatelet therapy, and cilostazol therapy (p < 0.05). Predictors in the AFS prediction model included DM, chronic obstructive pulmonary disease, DRF Rutherford classification, antiplatelet therapy, and cilostazol therapy (p < 0.05). The area under the curve and calibration curves for training, internal, and external validation of both OS and AFS prediction models indicated good performance.

This study demonstrates that both OS and AFS prediction models exhibit good discriminative ability to predict survival rate and AFS rate in patients with PAD. PAD patients with DM, DRF, Rutherford classification R5 and R6, absence of antiplatelet, and cilostazol therapy were associated with significantly lower OS and AFS rates. Controlling these risk predictors may be guide patient care in clinical settings.Clinical ImpactIn this multicenter longitudinal study, 2792 patients were included in the overall survival (OS) and amputation-free survival (AFS) prediction models development and validation. Patients with modifiable risk factors could potentially benefit from strategies aimed at preventing disease process (Rutherford classification), controlling comorbidities (diabetes mellitus, chronic obstructive pulmonary disease, and dialysis-dependent renal failure), and enhancing adherence to medical therapy (antiplatelet and cilostazol therapy). Our models have ability to predict survival and AFS rates at multiple time points, thereby facilitating early identification of high-risk patients and enabling timely interventions to optimize patient outcomes.