Risk factors of carotid plaque vulnerability in patients with ischemic stroke and construction of predictive model
10.3760/cma.j.cn115682-20221205-05850
- VernacularTitle:缺血性脑卒中患者颈动脉斑块易损性危险因素分析及预测模型构建
- Author:
Yan HE
1
;
Can SHENG
;
Qiurong HAN
;
Zhiling ZHAO
;
Wenling CUI
;
Lingzhi WANG
;
Yan YANG
Author Information
1. 济宁医学院附属医院神经内科，济宁 272000
- Keywords:
Stroke;
Risk factors;
Vulnerable plaque;
Type D personality;
Risk prediction model
- From:
Chinese Journal of Modern Nursing
2023;29(21):2873-2879
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To screen risk factors for carotid plaque vulnerability in patients with ischemic stroke and construct a risk prediction model.Methods:From November 2019 to January 2021, a total of 164 patients with ischemic stroke hospitalized in the Neurology Department of the Affiliated Hospital of Jining Medical University were selected as the study subjects by convenience sampling method. Color doppler ultrasound was used to measure carotid plaques in patients with ischemic stroke to determine whether they were vulnerable plaques. The patients were surveyed using the General Information Questionnaire and the Type D Personality Scale. Binary Logistic regression analysis was used to explore the risk factors of carotid plaque vulnerability in ischemic stroke patients, and based on this, a risk prediction model for carotid plaque vulnerability in ischemic stroke patients was constructed.Results:A total of 87 patients with ischemic stroke had vulnerable carotid plaques. The results of binary Logistic regression analysis showed that age ( OR=1.136, 95% CI: 1.052-1.226), total score of Type D Personality Scale ( OR=1.170, 95% CI: 1.043-1.312), smoking history ( OR=3.058, 95% CI: 1.054-8.875), homocysteine ( OR=1.400, 95% CI: 1.179-1.664), triglycerides ( OR=2.356, 95% CI: 1.534-3.619) were risk factors for carotid plaque vulnerability in stroke patients ( P<0.05). The results of risk prediction model based on risk factors show that, the area under the receiver operating characteristic of the subjects was 0.935, indicating good clinical predictive ability. Conclusions:Age, total score of Type D Personality Scale, smoking history, homocysteine and triglyceride are risk factors of carotid plaque vulnerability in stroke patients. The risk prediction model can early identify and screen high-risk factors for carotid plaque vulnerability in ischemic stroke patients, and is worthy of clinical promotion and practice.
