目的: 分析Gensini评分联合CHADS2评分对急性缺血性卒中(AIS)患者发生急性冠状动脉综合征(ACS)的预测价值。方法: 对2019年1月至2022年1月期间治疗的185例AIS患者的一般资料及临床资料进行回顾性分析,依据患者是否发生ACS分为ACS组(n=39)和对照组(n=146),比较两组患者的各项一般资料和临床资料,对差异有统计学意义的因素进一步行Logistic多因素分析,明确AIS患者发生ACS的危险因素,并分别对Gensini评分、CHADS2评分与各危险因素的关系行Pearson相关性分析,以ROC曲线分析Gensini评分联合CHADS2评分对AIS患者发生ACS的预测价值。结果: Logistic多因素分析结果显示,年龄≥65岁、合并糖尿病、左室射血分数＜50 %、Gensini评分≥47分、CHADS2评分≥2分为AIS患者发生ACS的危险因素(P＜0.05)。Gensini评分与年龄、糖尿病、CHADS2评分呈正相关(P＜0.05),与左室射血分数呈负相关(P＜0.05)。CHADS2评分与年龄、糖尿病、Gensini评分呈正相关(P＜0.05),与左室射血分数呈负相关(P＜0.05)。ROC曲线分析结果显示,Gensini评分预测AIS患者发生ACS的敏感度和特异度分别为65.38 %和71.19 %,最佳截断值为50分,曲线下面积(AUC)=0.766,95 %CI为0.694-0.838;CHADS2评分预测AIS患者发生ACS的敏感度和特异度分别为77.56 %和75.42 %,最佳截断值为2分,AUC=0.864,95 %CI为0.742-0.897;Gensini评分联合CHADS2评分预测AIS患者发生ACS的敏感度和特异度分别为87.65 %和84.57 %,AUC=0.924,95 %CI为0.812-0.934。结论: Gensini评分、CHADS2评分均为AIS患者发生ACS的危险因素,且二者联合应用对AIS患者发生ACS有较佳的预测价值。
Objective: To analyze the predictive value of Gensini score combined with CHADS2 score for acute coronary syndrome (ACS) in patients with acute ischemic stroke (AIS). Methods: The general data and clinical data of 185 AIS patients who were treated in our hospital from January 2019 to January 2022 were retrospectively analyzed. According to the occurrence of ACS, the patients were divided into ACS group (n=39) and control group (n=146). The general data and clinical data of the two groups were compared. Logistic multifactor analysis was further performed for the factors with statistically significant differences to identify the risk factors for ACS in AIS patients, and Pearson correlation analysis was performed for the relationship between Gensini score, CHADS2 score and each risk factor. ROC curve was used to analyze the predictive value of Gensini score combined with CHADS2 score for ACS in AIS patients. Results: Logistic multivariate analysis showed that age ≥65 years, diabetes mellitus, left ventricular ejection fraction < 50 %, Gensini score ≥47, CHADS2 score ≥2 were divided into risk factors for ACS in AIS patients (P＜0.05). Gensini score was positively correlated with age, diabetes mellitus and CHADS2 score (P＜0.05), but negatively correlated with left ventricular ejection fraction (P＜0.05). CHADS2 score was positively correlated with age, diabetes mellitus and Gensini score (P＜0.05), but negatively correlated with left ventricular ejection fraction (P＜0.05). ROC curve analysis showed that Gensini score predicted the sensitivity and specificity of ACS in AIS patients were 65.38 % and 71.19 %, respectively. The optimal cut-off value was 50 points, area under curve (AUC) =0.766, 95 %CI = 0.694-0.838. The sensitivity and specificity of CHADS2 score for predicting ACS in AIS patients were 77.56 % and 75.42 %, respectively. The optimal cut-off value was 2 points, AUC=0.864, 95 %CI = 0.742-0.897. The sensitivity and specificity of Gensini score combined with CHADS2 score in predicting ACS in AIS patients were 87.65 % and 84.57 %, respectively, with AUC=0.924 and 95 %CI = 0.812-0.934. Conclusion: Both Gensini score and CHADS2 score are risk factors for ACS in AIS patients, and their combined application has better predictive value for ACS in AIS patients.
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