老年慢性心力衰竭患者心肌能量代谢改变及其与不良心血管事件的关联

Changes in myocardial energy metabolism and its association with adverse cardiovascular events in elderly patients with chronic heart failure

目的 观察老年慢性心力衰竭(chronic heart failure, CHF)患者心肌能量代谢改变情况,分析其对主要不良心血管事件(major adverse cardiovascular event, MACE)的影响。方法 回顾性选取2021年1月至2024年1月南阳市第一人民医院心血管内科收治的老年CHF患者90例作为观察组,另选取本院同期健康体检者30例作为对照组。比较2组心肌能量代谢指标[血清游离脂肪酸(ree fatty acid, FFA)、左心室收缩末圆周室壁应力(circumferential end-systolic wall stress, cESS)和心肌能量消耗(myocardial energy expenditure, MEE)]。观察组出院后随访6个月,根据是否发生MACE分为MACE组40例和非MACE组50例。比较2组FFA、cESS、MEE,采用Spearman相关系数分析相关性,多因素logistic回归分析影响因素,ROC曲线分析预测价值。结果 观察组Ⅱ级、Ⅲ级、Ⅳ级FFA、cESS、MEE依次升高(P<0.01)。观察组Ⅱ级、Ⅲ级、Ⅳ级FFA、cESS、MEE显著高于对照组(P<0.05)。MACE组年龄、纽约心脏病协会(New York Heart Association, NYHA)心功能分级、N末端B型钠尿肽前体(N-terminal pro-B-type natriuretic peptide, NT-proBNP)、FFA、cESS、MEE显著高于非MACE组,左心室射血分数(left ventricular ejection fraction, LVEF)显著低于非MACE组(P<0.01)。FFA、cESS、MEE与老年CHF患者发生MACE正相关(r=0.512、0.495、0.503,P<0.05,P<0.01)。年龄(OR=2.344,95%CI:1.255~3.464)、NYHA心功能分级(OR=2.079,95%CI:1.354~2.804)、LVEF(OR=2.173,95%CI:1.179~3.167)、NT-proBNP(OR=2.257,95%CI:1.149~3.365)、FFA(OR=2.713,95%CI:1.103~4.323)、cESS(OR=1.868,95%CI:1.111~2.625)、MEE(OR=3.010,95%CI:1.064~4.956)为老年CHF患者发生MACE的危险因素。FFA、cESS、MEE单独预测老年CHF患者发生MACE的曲线下面积(area under curve, AUC)分别为0.732、0.707、0.768,三项联合检测的AUC为0.893,显著优于单独检测(Z=6.325、6.581、6.247,P=0.022、0.015、0.026)。结论 FFA、cESS及MEE与老年CHF患者发生MACE呈正相关,为MACE发生的危险因素,且各自具备一定的预测MACE的能力,而三项联合检测可进一步提升预测价值。

Objective To observe the changes in myocardial energy metabolism in elderly patients with chronic heart failure(CHF),and analyze its influence on major adverse cardiovascular events(MACE).Methods A retrospective study was conducted on 90 elderly CHF patients(observation group)admitted to our department from January 2021 to January 2024.They were assigned into ClassⅡ(23 cases),Ⅲ(37 cases)andⅣsubgroups(30 cases)according to NYHA functional classification.And another 30 healthy individuals who taking physical examination in our hospital during same period served as control group.Their myocardial energy metabolism indicators,including free fatty acids(FFA),circumferential end-systolic wall stress(cESS)and myocardial energy expenditure(MEE)were compared between the two groups.The patients in the observation group were followed up for 6 months after discharge,and then divided into MACE subgroup(40 cases)and non-MACE subgroup(50 cases)according to whether MACE occurred.The differences of FFA,cESS and MEE were compared between the two subgroups,and Spearman correlation analysis was used to analyze the correlation of the indicators,multivariate logistic regression analysis was employed to identify the influencing factors,and ROC curve was plotted to analyze the predictive values of the indicators.Results The values of FFA,cESS and MEE were significantly increased in the NYHA ClassⅡ,ⅢandⅣsubgroups in turn(P<0.01),and these values were obviously higher in the observation group than the control group(P<0.05).The MACE group had advanced age,larger proportion of NYHA ClassⅣ,and higher NT-proBNP and FFA levels,cESS and MEE values,but lower LVEF than the non-MACE group(P<0.01).FFA,cESS and MEE were positively correlated with MACE in elderly CHF patients(r=0.512,0.495,0.503,P<0.05,P<0.01).Age(OR=2.344,95%CI:1.255-3.464),NYHA cardiac function grade(OR=2.079,95%CI:1.354-2.804),LVEF(OR=2.173,95%CI:1.179-3.167),NT-proBNP(OR=2.257,95%CI:1.149-3.365),FFA(OR=2.713,95%CI:1.103-4.323),cESS(OR=1.868,95%CI:1.111-2.625)and MEE(OR=3.010,95%CI:1.064-4.956)were risk factors for MACE in elderly CHF patients with.The AUC value of FFA,cESS and MEE in predicting the occurrence of MACE in elderly CHF patients was 0.732,0.707 and 0.768,respectively,and the AUC value of their combination was 0.893,which was greater than that of the single indicator(Z=6.325,6.581,6.247;P=0.022,0.015,0.026).Conclusion FFA,cESS and MEE are positively correlated with the occurrence of MACE in elderly CHF patients,and are risk factors for MACE occurrence in them.The three indicators have certain predictive performance for MACE,and their combination can further improve the predictive value.