国际医药卫生导报 ›› 2025, Vol. 31 ›› Issue (22): 3731-3737.DOI: 10.3760/cma.j.cn441417-20250819-22008

• 论著 • 上一篇    下一篇

冠心病患者并发心源性脑卒中的影响因素分析及风险预测

狄海莉1  廖永彬1  郭显2   

  1. 1西北大学第一医院神经内科,西安 710043;2解放军空军军医大学航空航天医学系,西安 710032
  • 收稿日期:2025-08-19 出版日期:2025-11-01 发布日期:2025-11-19
  • 通讯作者: 廖永彬,Email:314902107@qq.com
  • 基金资助:
    陕西省重点研发计划(2022SF-276)

Analysis of influencing factors and risk prediction of coronary heart disease complicated with cardiogenic stroke

Di Haili1, Liao Yongbin1, Guo Xian2   

  1. 1Department of Neurology, Northwest University First Hospital, Xi'an 710043, China; 2Department of Aerospace Medicine, Air Force Medical University, Xi'an 710032, China
  • Received:2025-08-19 Online:2025-11-01 Published:2025-11-19
  • Contact: Liao Yongbin, Email: 314902107@qq.com
  • Supported by:
    Key Plan of Research and Development in Shaanxi (2022SF-276)

摘要:

目的 分析冠心病患者并发心源性脑卒中的影响因素,并依此构建风险预测模型。方法 回顾性分析,收集2022年1月至2025年1月西北大学第一医院收治的249例冠心病患者临床资料,统计患者并发心源性脑卒中情况,将并发患者归为病例组(105例),同时将性别、年龄、体质量指数、吸烟史、饮酒史作为匹配变量,按照1∶1比例匹配无并发症患者作为对照组(105例)。病例组男58例、女47例,年龄(66.23±6.53)岁;对照组男54例、女51例,年龄(64.85±6.91)岁。对比两组患者临床资料,采用多因素logistic回归分析冠心病并发心源性脑卒中的影响因素;绘制冠心病并发心源性脑卒中的列线图模型;采用校准曲线、受试者操作特征(ROC)曲线、决策曲线分析(DCA)评估列线图模型的预测效能。统计学方法采用t检验、χ2检验。结果 病例组高血压、心律失常、心脏附壁血栓、颈动脉斑块、重度冠状动脉病变占比及左心房内径(LAD)、左心房内径指数(LADi)、同型半胱氨酸(Hcy)均高于对照组[65.71%(69/105)比40.95%(43/105)、71.43%(75/105)比29.52%(31/105)、21.90%(23/105)比6.67%(7/105)、48.57%(51/105)比26.67%(28/105)、22.86%(24/105)比5.71%(6/105)、(38.16±5.81)mm比(35.92±4.73)mm、(22.69±3.24)mm/m2比(19.41±2.78)mm/m2、(16.92±4.20)μmol/L比(13.67±3.74)μmol/L],左室射血分数(LVEF)低于对照组[(52.58±11.83)%比(57.40±9.89)%],差异均有统计学意义(均P<0.05)。多因素logistic回归分析结果显示,高血压、心律失常、心脏附壁血栓、颈动脉斑块、重度冠状动脉病变、LAD扩大、LADi及Hcy高是冠心病并发心源性脑卒中的危险因素(均P<0.05),LVEF高是其保护因素(P<0.05)。基于多因素分析结果构建的列线图模型经内部验证显示,预测曲线与实际曲线相近,一致性指数为0.847。ROC曲线结果显示,列线图模型预测冠心病并发心源性脑卒中的曲线下面积(AUC)、灵敏度、特异度分别为0.836(95%CI 0.779~0.884)、82.86%、80.95%。DCA结果显示,阈值范围为0.08~0.93时,列线图模型具有较好的临床收益。结论 高血压、心律失常、心脏附壁血栓、颈动脉斑块、冠状动脉病变程度、LAD、LADi、LVEF、Hcy是冠心病并发心源性脑卒中的影响因素,基于上述因素构建的列线图预测模型具有良好的临床价值。

关键词: 冠心病, 心源性脑卒中, 影响因素, 列线图

Abstract: Objective To analyze the influencing factors of in patients with cardiogenic stroke in patients with, and to construct a risk prediction model. Methods The clinical data of 249 patients with coronary heart disease admitted to the Northwest University First Hospital from January 2022 to January 2025 were retrospectively analyzed. The patients with cardiogenic stroke were counted, and these patients were classified as a case group (105 cases). Meanwhile, gender, age, body mass index, smoking history, and drinking history were used as matching variables. The patients without concurrent cardiogenic stroke were matched at a 1:1 ratio as a control group (105 cases). There were 58 males and 47 females in the case group, aged (66.23±6.53) years. There were 54 males and 51 females in the control group, aged (64.85±6.91) years old. The clinical data of the two groups were compared, and the influencing factors of coronary heart disease complicated with cardiogenic stroke were analyzed by multivariate logistic regression. A nomogram model of coronary heart disease complicated with cardiogenic stroke was drawn; the calibration curve, receiver operating characteristic curve (ROC), and decision curve analysis (DCA) were used to evaluate the predictive performance of the nomogram model. t test and χ2 test were used as the statistical methods. Results The proportion of the patients with hypertension, arrhythmia, cardiac mural thrombus, carotid plaque, severe coronary artery disease, left atrial diameter (LAD), left atrial diameter index (LADi), and homocysteine (Hcy) in the case group were higher than those in the control group [65.71% (69/105) vs. 40.95% (43/105), 71.43% (75/105) vs. 29.52% (31/105), 21.90% (23/105) vs. 6.67% (7/105), 48.57% (51/105) vs. 26.67% (28/105), 22.86% (24/105) vs. 5.71% (6/105), (38.16±5.81) mm vs. (35.92±4.73) mm, (22.69±3.24) mm/m2 vs. (19.41±2.78) mm/m2, and (16.92±4.20) μmol/L vs. (13.67±3.74) μmol/L], and the left ventricular ejection fraction (LVEF) was lower [(52.58±11.83)% vs. (57.40±9.89)%], with statistical differences (all P<0.05). The multivariate logistic regression analysis showed that hypertension, arrhythmia, cardiac mural thrombus, carotid plaque, severe coronary artery disease, LAD enlargement, high LADi, and high Hcy were risk factors for coronary heart disease and cardiogenic stroke (all P<0.05), and high LVEF was a protective factor (P<0.05). The internal verification of the nomogram model constructed based on the results of multivariate analysis showed that the predicted curve was similar to the actual curve, and the consistency index was 0.847. The results of ROC showed that the area under the curve (AUC), sensitivity, and specificity of the nomogram model in predicting coronary heart disease complicated with cardiogenic stroke were 0.836 (95%CI 0.779-0.884), 82.86%, and 80.95%, respectively. The results of DCA curve showed that the nomogram model had better clinical benefits when the threshold range was 0.08-0.93. Conclusions Hypertension, arrhythmia, cardiac mural thrombus, carotid plaque, degree of coronary artery disease, LAD, LADi, LVEF, and Hcy are the influencing factors of coronary heart disease and cardiogenic stroke. The nomogram prediction model constructed based on the above factors has good clinical value.

Key words: Coronary heart disease, Cardiogenic stroke, Influencing factors, Nomogram