Chen Bingxin, Men Li, Wang Hongli, Yang Long, Li Mingxi, Hu Jingcheng, Fan Ping
Department of Heart Function, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Front Cardiovasc Med. 2024 Jul 23;11:1401343. doi: 10.3389/fcvm.2024.1401343. eCollection 2024.
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.
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.
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); = 0.005] and SDANN index [OR: 0.982; 95% CI: (0.969, 0.996); = 0.009] were negatively associated with the incidence of MACEs. Conversely, the slowest heart rate [OR: 1.075; 95% CI: (1.022, 1.131); = 0.005] and frequent PVCs [OR: 2.685; 95% CI: (1.204, 5.987); = 0.016] demonstrated a positive association with MACEs. Furthermore, SDNN [OR: 0.957; 95% CI: (0.933, 0.981); = 0.001], DC [OR: 0. 702; 95% CI: (0.526, 0.938); = 0.017]) and DC4 [OR: 0.020; 95% CI: (0.001, 0.664); = 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)].
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.
评估急性ST段抬高型心肌梗死(STEMI)患者并接受经皮冠状动脉介入治疗(PCI)时的心血管风险,对于早期干预和改善长期预后至关重要。24小时动态心电图监测可提供连续的心脏电生理数据,能够检测出常规检查中未发现的心律失常和自主神经功能障碍。本研究旨在探讨动态心电图监测指标与STEMI患者PCI术后院外主要不良心血管事件(MACE)发生之间的关系,为心血管风险评估提供见解。
这项前瞻性队列研究纳入了接受PCI的STEMI患者。记录24小时动态心电图监测数据,包括心率、心率变异性(HRV)指标如标准差(SDNN)和标准差指数(SDANN index)、不同时间尺度下的心率减速能力(DC)(DC2、DC4、DC8)以及室性早搏(PVC)的频率。使用多因素逻辑回归研究这些指标与MACE以及心血管死亡之间的独立相关性。通过受试者工作特征(ROC)曲线评估预测能力。
本研究共纳入172名参与者。在3年的随访期内,57名患者发生了MACE,其中包括20例心源性死亡。在调整混杂变量的逻辑回归模型中,SDNN [比值比(OR):0.980;95%置信区间(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]和频发PVC [OR:2.685;95% CI:(1.204,5.987);P = 0.016]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:(0.526,0.93);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 [曲线下面积(AUC):0.688(95% CI:0.601 - 0.776)]和心源性死亡[AUC:0.752(95% CI:0.625 - 0.879)]的有效预测指标。
24小时动态心电图监测获得的HRV、DC指标和频发PVC与STEMI患者的MACE风险相关。这些指标可帮助临床医生早期识别高危患者,以便及时进行干预。