Abstract：
OBJECTIVE: We aim to integrate the parameters of two-dimensional (2D) echocardiography and identify the high-risk population for all-cause mortality in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI).
METHODS: The study involved a retrospective cohort population with STEMI who were admitted to Yongchuan Hospital of Chongqing Medical University between January 2016 and January 2019. Baseline data were collected, including 2D echocardiography parameters and left ventricular ejection fraction (LVEF). The parameters of 2D echocardiography were subjected to cluster analysis. Logistic regression models were employed to assess univariate and multivariate adjusted odds ratios (ORs) of cluster information in relation to all-cause mortality. Four logistic regression models were generated, utilizing cluster information, clinical variables, clinical variables in conjunction with LVEF, and clinical variables in conjunction with LVEF and cluster information as predictive variables, respectively. The area under the curve (AUC) were utilized to evaluate the incremental risk stratification value of cluster information.
RESULTS: The study included 633 participants with 28.8% female, a mean age of 65.68 ± 11.98 years. Over the course of a 3-year follow-up period, 108 (17.1%) patients experienced all-cause mortality. Utilizing cluster analysis of 2D echocardiography parameters, the patients were categorized into two distinct clusters, with statistically significant differences observed in most clinical variables, echocardiography, and survival outcomes between the clusters. Multivariate regression analysis revealed that cluster information was independently associated with the risk of all-cause mortality with adjusted OR 7.33 (95% confidence interval [CI] 3.99-14.06, P < 0.001). The inclusion of LVEF enhanced the predictive capacity of the model utilized with clinical variables with AUC 0.848 (95% CI 0.809-0.888) versus AUC 0.872 (95% CI 0.836-0.908) (P < 0.001), and the addition of cluster information further improved its predictive performance with AUC 0.906 (95% CI 0.878-0.934, P < 0.001). This cluster analysis was translated into a free available online calculator (https://app-for-mortality-prediction-cluster.streamlit.app/).
CONCLUSIONS: The 2D echocardiographic diagnostic information based on cluster analysis had good prognostic value for STEMI population, which was helpful for risk stratification and individualized intervention.
METHODS: The study involved a retrospective cohort population with STEMI who were admitted to Yongchuan Hospital of Chongqing Medical University between January 2016 and January 2019. Baseline data were collected, including 2D echocardiography parameters and left ventricular ejection fraction (LVEF). The parameters of 2D echocardiography were subjected to cluster analysis. Logistic regression models were employed to assess univariate and multivariate adjusted odds ratios (ORs) of cluster information in relation to all-cause mortality. Four logistic regression models were generated, utilizing cluster information, clinical variables, clinical variables in conjunction with LVEF, and clinical variables in conjunction with LVEF and cluster information as predictive variables, respectively. The area under the curve (AUC) were utilized to evaluate the incremental risk stratification value of cluster information.
RESULTS: The study included 633 participants with 28.8% female, a mean age of 65.68 ± 11.98 years. Over the course of a 3-year follow-up period, 108 (17.1%) patients experienced all-cause mortality. Utilizing cluster analysis of 2D echocardiography parameters, the patients were categorized into two distinct clusters, with statistically significant differences observed in most clinical variables, echocardiography, and survival outcomes between the clusters. Multivariate regression analysis revealed that cluster information was independently associated with the risk of all-cause mortality with adjusted OR 7.33 (95% confidence interval [CI] 3.99-14.06, P < 0.001). The inclusion of LVEF enhanced the predictive capacity of the model utilized with clinical variables with AUC 0.848 (95% CI 0.809-0.888) versus AUC 0.872 (95% CI 0.836-0.908) (P < 0.001), and the addition of cluster information further improved its predictive performance with AUC 0.906 (95% CI 0.878-0.934, P < 0.001). This cluster analysis was translated into a free available online calculator (https://app-for-mortality-prediction-cluster.streamlit.app/).
CONCLUSIONS: The 2D echocardiographic diagnostic information based on cluster analysis had good prognostic value for STEMI population, which was helpful for risk stratification and individualized intervention.
摘要：
目的：我们旨在整合二维（2D）超声心动图的参数，并确定接受经皮冠状动脉介入治疗（PCI）的急性ST段抬高型心肌梗死（STEMI）患者全因死亡的高危人群。
方法：本研究纳入2016年1月至2019年1月重庆医科大学永川医院收治的STEMI患者的回顾性队列。收集基线数据，包括二维超声心动图参数和左心室射血分数(LVEF)。对二维超声心动图参数进行聚类分析。采用Logistic回归模型评估与全因死亡率相关的聚类信息的单变量和多变量调整比值比(OR)。生成了四个逻辑回归模型，利用集群信息，临床变量，与LVEF相关的临床变量，临床变量与LVEF和聚类信息一起作为预测变量，分别。曲线下面积（AUC）用于评估聚类信息的增量风险分层值。
结果：该研究包括633名参与者，其中28.8%为女性，平均年龄65.68±11.98岁。在3年的随访期间，108例（17.1%）患者出现全因死亡。利用二维超声心动图参数的聚类分析，患者被分为两个不同的集群，在大多数临床变量中观察到统计学上的显著差异，超声心动图,和集群之间的生存结果。多因素回归分析显示，聚类信息与全因死亡风险独立相关，校正OR为7.33（95%置信区间[CI]3.99-14.06，P<0.001）。纳入LVEF增强了模型与临床变量的预测能力，AUC0.848（95％CI0.809-0.888）与AUC0.872（95％CI0.836-0.908）（P<0.001），聚类信息的添加进一步提高了其预测性能，AUC为0.906(95%CI0.878-0.934，P<0.001)。此聚类分析已转换为免费的在线计算器（https://app-for-malty-prediction-cluster。流光。app/)。
结论：基于聚类分析的二维超声心动图诊断信息对STEMI人群具有良好的预后价值，这有助于风险分层和个体化干预。
方法：本研究纳入2016年1月至2019年1月重庆医科大学永川医院收治的STEMI患者的回顾性队列。收集基线数据，包括二维超声心动图参数和左心室射血分数(LVEF)。对二维超声心动图参数进行聚类分析。采用Logistic回归模型评估与全因死亡率相关的聚类信息的单变量和多变量调整比值比(OR)。生成了四个逻辑回归模型，利用集群信息，临床变量，与LVEF相关的临床变量，临床变量与LVEF和聚类信息一起作为预测变量，分别。曲线下面积（AUC）用于评估聚类信息的增量风险分层值。
结果：该研究包括633名参与者，其中28.8%为女性，平均年龄65.68±11.98岁。在3年的随访期间，108例（17.1%）患者出现全因死亡。利用二维超声心动图参数的聚类分析，患者被分为两个不同的集群，在大多数临床变量中观察到统计学上的显著差异，超声心动图,和集群之间的生存结果。多因素回归分析显示，聚类信息与全因死亡风险独立相关，校正OR为7.33（95%置信区间[CI]3.99-14.06，P<0.001）。纳入LVEF增强了模型与临床变量的预测能力，AUC0.848（95％CI0.809-0.888）与AUC0.872（95％CI0.836-0.908）（P<0.001），聚类信息的添加进一步提高了其预测性能，AUC为0.906(95%CI0.878-0.934，P<0.001)。此聚类分析已转换为免费的在线计算器（https://app-for-malty-prediction-cluster。流光。app/)。
结论：基于聚类分析的二维超声心动图诊断信息对STEMI人群具有良好的预后价值，这有助于风险分层和个体化干预。
