作者
Huangguo Xiong,Li Wang,Pan Song,Xianglan Quan,Mingfeng Zhang,Siyuan Huang,Xiaoyu Liu,Qin Chen,Xinxin He,Xiuying Hu,Xi Yang,Meihong Shi
摘要
Objective To investigate the risk factors for major limb adverse events (MALE) in peripheral arterial disease (PAD) combined with frailty and to develop and validate a risk prediction model of MALE. Methods This prospective study was performed in the vascular surgery department of patients in six hospitals in southwest China. Prospective collection of patients with PAD combined with frailty from February 1 to December 20, 2021, with MALE as the primary outcome, and followed for 1 year. The cohort was divided into a development cohort and a validation cohort. In the development cohort, a multivariate risk prediction model was developed to predict MALE using random forests for variable selection and multivariable Cox regression analysis. The model is represented by a visualized nomogram and a web-based calculator. The model performance was tested with the validation cohort and assessed using the C-statistic and calibration plots. Results A total of 1179 patients were prospectively enrolled from February 1 to December 20, 2021. Among 816 patients with PAD who were included in the analysis, the median follow-up period for this study was 9 ± 4.07 months, the mean age was 74.64 ± 9.43 years, and 249 (30.5%) were women. Within 1 year, 222 patients (27.2%) developed MALE. Target lesion revascularizations were performed in 99 patients (12.1%), and amputations were performed in 131 patients (16.1%). The mortality rate within the whole cohort was 108 patients (13.2%). After controlling for competing risk events (death), the cumulative risk of developing MALE was not statistically different. Prealbumin (hazard ratio [HR], 0.6; 95% confidence interval [CI], 0.41-0.89; P = .010), percutaneous coronary intervention (HR, 2.31; 95% CI, 1.26-4.21; P = .006), Rutherford classification (HR, 1.77; 95% CI, 1.36-2.31; P < .001), white blood cell (HR, 1.85; 95% CI, 1.20-2.87; P = .005), high altitude area (HR, 3.1; 95% CI, 1.43-6.75; P = .004), endovascular treatment (HR, 10.2; 95% CI, 1.44-72.5; P = .020), and length of stay (HR, 1.01; 95% CI, 1.00-1.03; P = .012) were risk factors for MALE. The MALE prediction model had a C-statistic of 0.76 (95% CI, 0.70-0.79). The C-statistic was 0.68 for internal validation and 0.66 for external validation for the MALE prediction model. The MALE prediction model for PAD presented an interactive nomogram and a web-based network calculator. Conclusions In this study, the MALE prediction model has a discriminative ability to predict MALE among patients with PAD in frailty. The MALE model can optimize clinical decision-making for patients in PAD with frailty.