评估急性ST段抬高型心肌梗死(STEMI)和经皮冠状动脉介入治疗(PCI)患者的心血管风险对于早期干预和改善长期预后至关重要。24hHolter监测提供连续的心脏电生理数据,能够检测在常规检查期间未捕获的心律失常和自主神经功能障碍。本研究旨在研究STEMI患者PCI后Holter监测指标与院外主要不良心血管事件(MACE)发生之间的关系。提供对心血管风险评估的见解。
■这项前瞻性队列研究包括接受PCI的STEMI患者。记录24hHolter监测数据,包括心率,心率变异性(HRV)指标,如SDNN和SDANN指数,心率减速能力(DC)在不同的时间尺度(DC2,DC4,DC8),和室性早搏(PVC)的频率。这些指数与MACE之间的独立相关性,以及心血管死亡,使用多因素Logistic回归进行调查。通过受试者工作特征(ROC)曲线评估预测能力。
■共有172名参与者参加了这项研究。在3年的随访期内,在57例患者中观察到MACEs,包括20例心源性死亡。在针对混杂变量进行调整的逻辑回归模型中,SDNN[OR:0.980;95%CI:(0.967,0.994);p=0.005]和SDANN指数[OR:0.982;95%CI:(0.969,0.996);p=0.009]与MACE的发生率呈负相关。相反,最慢心率[OR:1.075;95%CI:(1.022,1.131);p=0.005]和频繁PVCs[OR:2.685;95%CI:(1.204,5.987);p=0.016]显示与MACE呈正相关。此外,SDNN[OR:0.957;95%CI:(0.933,0.981);p=0.001],DC[OR:0。702;95%CI:(0.526,0.938);p=0.017])和DC4[OR:0.020;95%CI:(0.001,0.664);p=0.029]与心源性死亡呈负相关。ROC分析结果表明,SDNN是MACE[AUC:0.688(95%CI:0.601-0.776)]和心脏死亡[AUC:0.752(95%CI:0.625-0.879)]的有效预测因子。
■HRV,DC指标,通过24hHolter监测获得的频繁PVC与STEMI患者的MACE风险相关。这些指标可以帮助临床医生及早识别有风险的患者,以便及时进行干预。
UNASSIGNED: Evaluating cardiovascular risk in patients experiencing acute ST-elevation myocardial infarction (STEMI) and undergoing percutaneous coronary intervention (PCI) is crucial for early intervention and improving long-term outcomes. 24 h
Holter monitoring provides continuous cardiac electrophysiological data, enabling the detection of arrhythmias and autonomic dysfunction that are not captured during routine examinations. This study aimed to examine the relationship between
Holter monitoring metrics and the occurrence of out-of-hospital major adverse cardiovascular events (MACEs) following PCI in patients with STEMI, offering insights into cardiovascular risk evaluation.
UNASSIGNED: This prospective cohort study included STEMI patients undergoing PCI. 24 h
Holter monitoring data were recorded, including heart rate, heart rate variability (HRV) metrics such as SDNN and SDANN index, heart rate deceleration capacity (DC) at different time scales (DC2, DC4, DC8), and the frequency of premature ventricular contractions (PVCs). Independent correlations between these indices and MACEs, as well as cardiovascular deaths, were investigated using multifactorial logistic regression. Predictive capacities were assessed through receiver operating characteristic (ROC) curves.
UNASSIGNED: A total of 172 participants were enrolled in this study. Over the 3-year follow-up period, MACEs were observed in 57 patients, including 20 cases of cardiac death. In logistic regression models adjusted for confounding variables, SDNN [OR: 0.980; 95% CI: (0.967, 0.994); p = 0.005] and SDANN index [OR: 0.982; 95% CI: (0.969, 0.996); p = 0.009] were negatively associated with the incidence of MACEs. Conversely, the slowest heart rate [OR: 1.075; 95% CI: (1.022, 1.131); p = 0.005] and frequent PVCs [OR: 2.685; 95% CI: (1.204, 5.987); p = 0.016] demonstrated a positive association with MACEs. Furthermore, SDNN [OR: 0.957; 95% CI: (0.933, 0.981); p = 0.001], DC [OR: 0. 702; 95% CI: (0.526, 0.938); p = 0.017]) and DC4 [OR: 0.020; 95% CI: (0.001, 0.664); p = 0.029] were negatively associated with cardiac death. The ROC analysis results indicated that SDNN was an effective predictor of both MACEs [AUC: 0.688 (95% CI: 0.601-0.776)] and cardiac death [AUC: 0.752 (95% CI: 0.625-0.879)].
UNASSIGNED: HRV, DC metrics, and frequent PVCs obtained by 24 h
Holter monitoring were associated with the risk of MACEs in STEMI patients. These metrics can help clinicians identify at-risk patients early so that timely interventions.