Innovative nomogram for predicting 1-year clinically driven revascularization outcomes in endovascular femoropopliteal disease

Abstract

PurposeFemoropopliteal artery disease (FPAD) is a common vascular disease that usually requires surgical treatment. The aim of this study was to apply predictive modeling to develop predictive models for predicting clinically driven target revascularization (CD-TLR) events 1 year after intervention in patients with FPAD.Materials and methodsIn this study, clinical data were collected from a total of 484 patients who underwent FPAD endovascular intervention from 2014 to 2019. According to the inclusion and exclusion criteria, 400 patients will be finally included and assigned to the training cohort and test cohort in the ratio of 7:3. By analyzing these data through statistical methods, we will explore the effects of different factors on target revascularization events 1 year after intervention in FPAD patients, and build the corresponding prediction model of the column line graph.ResultsThe final nomogram model consisted of 5 independent predictors: history of cerebrovascular disease, lesion length >15 cm, no atherectomy device used, no medicated balloon used and procedure time. The C-index of the model was 0.766 and 0.726 for the training cohort and validation cohort, respectively. The calibration curves also showed that the model had satisfactory agreement in both cohorts.ConclusionsThe newly developed prediction model can accurately predict clinically driven target revascularization events at 1 year in patients with FPAD, providing valuable information for the development of individualized treatment plans.