目的：分析冠状动脉CT血管成像（CTA）联合动态心电图（DCG）与冠心病患者冠脉狭窄程度及预后情况的关联。方法：研究对象选择我院2024年1月~2025年3月收治的210例冠心病患者及同期接受检查的210例非冠心病患者，分别列为病例组和对照组，比较两组CTA参数、DCG参数间差异。依据入院测得（Gensini）评分不同，将入组患者分别列为轻度组（60例，Gensini评分≤30分）、中度组（75例，Gensini评分＞30分、≤60分）和重度组（75例，Gensini评分＞60分），比较三组CTA参数、DCG参数间差异，分析CTA参数、DCG参数与Gensini评分的相关性。统计入组患者不良预后发生情况，比较不同预后患者CTA参数、DCG参数间差异，归纳冠心病患者预后影响因素，检验CTA参数、DCG参数对患者不良预后的预测效能。结果：病例组的最小管腔直径（MLD）、最小管腔面积（MLA）、血流储备分数（FFR）、正常窦性间期的标准差（SDNN）、每5min平均RR间期的标准差（SDANN）、相邻RR间期差值的均方根（RMSSD）均低于对照组，斑块总体积（TPV）、低频/高频比值（LF/HF）均高于对照组（t=24.128，25.811，15.613，37.636，26.858，9.195，59.862，29.389；P＜0.05）。重度组的MLD、MLA、FFR、SDNN、SDANN、RMSSD均低于中度组，轻度组，TPV、LF-HF均高于中度组，轻度组（F=190.291，51.562，186.482，42.084，44.413，22.541，56.503，109.983；P＜0.05）。MLD、MLA、FFR、SDNN、SDANN、RMSSD均与Gensini评分负相关，TPV、LF-HF均与Gensini评分正相关（r=-0.352，-0.377，-0.445，-0.472，-0.332，-0.356，0.401，0.355；P＜0.05）。经统计，210例冠心病患者的不良预后发生率为38.10%（80/210）。预后不良组的MLD、MLA、FFR、SDNN、SDANN、RMSSD均低于预后良好组，TPV、LF-HF均高于预后良好组（t=6.827，12.219，19.313，6.097，7.097，5.027，7.088，12.465；P＜0.05）。MLA、FFR、SDNN升高为冠心病不良预后的保护因素，LF/HF升高为冠心病不良预后的危险因素。FFR、SDNN联合检测预测不良预后的 AUC 值优于两项指标单独检测（Delong检验，P＜0.05）。结论：CTA、DCG能客观评估冠心病患者冠脉狭窄程度，联合检测FFR、SDNN可作为预测冠心病不良预后的重要辅助手段。
Objective:To analyze the correlation between CTA combined with DCG and the degree of coronary stenosis and prognosis in patients with coronary heart disease.Methods:The research subjects selected were 210 patients with coronary heart disease admitted to our hospital from January 2024 to March 2025, as well as 210 non coronary heart disease patients who underwent examinations during the same period. They were divided into a case group and a control group. The differences in CTA parameters and DCG parameters between the two groups were compared. According to the different Gensini scores obtained upon admission, the enrolled patients were divided into mild group (60 cases, Gensini score ≤ 30 points), moderate group (75 cases, Gensini score>30 points, ≤ 60 points), and severe group (75 cases, Gensini score>60 points). The differences in CTA parameters and DCG parameters among the three groups were compared, and the correlation between CTA parameters, DCG parameters, and Gensini score was analyzed. Statistically analyze the occurrence of poor prognosis in enrolled patients, compare the differences in CTA and DCG parameters among patients with different prognoses, summarize the factors affecting the prognosis of coronary heart disease patients, and test the predictive power of CTA and DCG parameters for poor prognosis in patients.Results:The MLD, MLA, FFR, SDNN, SDANN, and RMSSD in the case group were all lower than the control group, while the TPV and LF/HF were higher than the control group (t=24.128，25.811，15.613，37.636，26.858，9.195，59.862，29.389; P<0.05). The MLD, MLA, FFR, SDNN, SDANN, and RMSSD of the severe group were lower than the moderate group, mild group, while the TPV and LF-HF of the mild group were higher than the moderate group, mild group (F=190.291，51.562，186.482，42.084，44.413，22.541，56.503，109.983; P<0.05). MLD, MLA, FFR, SDNN, SDANN, and RMSSD are all negatively correlated with Gensini score, while TPV and LF-HF are positively correlated with Gensini score (r=-0.352，-0.377，-0.445，-0.472，-0.332，-0.356，0.401，0.355; P<0.05). According to statistics, the incidence of poor prognosis in 210 patients with coronary heart disease was 38.10% (80/210). The MLD, MLA, FFR, SDNN, SDANN, and RMSSD of the poor prognosis group were lower than the good prognosis group, while TPV and LF-HF were higher than the good prognosis group (t=6.827，12.219，19.313，6.097，7.097，5.027，7.088，12.465; P<0.05). High MLA, FFR, and SDNN are protective factors for poor prognosis of coronary heart disease, while higher values than LF/HF are risk factors for poor prognosis of coronary heart disease. The combined detection of FFR and SDNN has a better AUC value for predicting poor prognosis of coronary heart disease than the detection of FFR and SDNN alone (Delong test, P<0.05）.Conclusion:CTA and DCG can objectively evaluate the degree of coronary stenosis in patients with coronary heart disease, and combined detection of FFR and SDNN can be an important auxiliary tool for predicting poor prognosis of coronary heart disease.
