Constructing predictive modelling for the risk of serious adverse cardiovascular events in postoperative patients of symptomatic arteriosclerosis obliterans
10.3760/cma.j.cn113855-20230704-00355
- VernacularTitle:下肢动脉硬化闭塞症患者术后严重不良心血管事件的风险预测
- Author:
Ye JI
1
;
Baoyan WANG
;
Qinshu WEN
;
Dan HAN
;
Guangyan WU
;
Yepeng ZHANG
;
Min ZHOU
Author Information
1. 南京中医药大学鼓楼临床医学院血管外科，南京 210008
- Keywords:
Arteriosclerosis obliterans;
Risk factors;
Cardiovascular system;
Forecasting
- From:
Chinese Journal of General Surgery
2024;39(3):197-202
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a predictive model for the risk of major adverse cardiovascular events(MACE) after surgery in patients with symptomatic arteriosclerosis obliterans(ASO) .Methods:From Jan 2018 to Dec 2021, 957 patients with symptomatic ASO admitted to Nanjing Drum Tower Hospital were selected and divided into MACE and non-MACE groups according to whether they had a post-op MACE. A risk prediction model was constructed based on a stepwise regression method with multi-factor COX regression analysis. The model was evaluated using the receiver operating characteristic curve (ROC), the calibration curve to assess the model fit, and the Bootstrap method for internal validation.Results:MACE occurred in 143 patients (14.94%). After COX regression analysis, BMI, creatinine clearance, fibrinogen, rivaroxaban and previous history of surgery were enrolled into model constructing. The ROC curve assessed the model with a C-statistic of 0.690 (95% CI: 0.644-0.736), sensitivity and specificity of 49.2% and 80.7% respectively, a Jorden index of 0.299 and an optimal cut-off value of 0.086. Calibration curves showing agreement between predicted and actual observed values. Internally validated C-statistic of 0.689 (95% CI: 0.672-0.700). The population was divided into high and low risk groups based on the best cut-off value and analysed for survival. The difference between the two groups was statistically different. Conclusion:The risk prediction model for the occurrence of MACE based on clinical parameters is simple and convenient, with good predictability and good discriminatory ability, and can provide reference for the assessment and treatment of MACE in ASO patients.
