Chaikovsky Illya, Dziuba Dmytro, Kryvova Olga, Marushko Katerina, Vakulenko Julia, Malakhov Kyrylo, Loskutov Оleg
Department of Anaesthesiology and Intensive Care, Shupyk National Healthcare University, Kyiv, Ukraine.
Department of Contactless Control Systems, V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences, Kyiv, Ukraine.
Front Artif Intell. 2025 Mar 12;8:1561079. doi: 10.3389/frai.2025.1561079. eCollection 2025.
Two years after the COVID-19 pandemic, it became known that one of the complications of this disease is myocardial injury. Electrocardiography (ECG) and cardiac biomarkers play a vital role in the early detection of cardiovascular complications and risk stratification. The study aimed to investigate the value of a new electrocardiographic metric for detecting minor myocardial injury in patients during COVID-19 treatment.
The study was conducted in 2021. A group of 26 patients with verified COVID-19 diagnosis admitted to the intensive care unit for infectious diseases was examined. The severity of a patient's condition was calculated using the NEWS score. The digital ECGs were repeatedly recorded (at the beginning and 2-4 times during the treatment). A total of 240 primary and composite ECG parameters were analyzed for each electrocardiogram. Among these patients, 6 patients died during treatment. Cluster analysis was used to identify subgroups of patients that differed significantly in terms of disease severity (NEWS), SрО and integral ECG index (an indicator of the state of the cardiovascular system).
Using analysis of variance (ANOVA repeated measures), a statistical assessment of changes of indicators in subgroups at the end of treatment was given. These subgroup differences persisted at the end of the treatment. To identify potential predictors of mortality, critical clinical and ECG parameters of surviving (S) and non-surviving patients (D) were compared using parametric and non-parametric statistical tests. A decision tree model to classify survival in patients with COVID-19 was constructed based on partial ECG parameters and NEWS score.
A comparison of potential mortality predictors showed no significant differences in vital signs between survivors and non-survivors at the beginning of treatment. A set of ECG parameters was identified that were significantly associated with treatment outcomes and may be predictors of COVID-19 mortality: T-wave morphology (SVD), Q-wave amplitude, and R-wave amplitude (lead I).
在新冠疫情爆发两年后，人们发现该疾病的并发症之一是心肌损伤。心电图（ECG）和心脏生物标志物在心血管并发症的早期检测和风险分层中起着至关重要的作用。本研究旨在探讨一种新的心电图指标在新冠治疗期间检测患者轻微心肌损伤的价值。
该研究于2021年进行。对一组确诊为新冠且入住传染病重症监护病房的26例患者进行了检查。使用NEWS评分计算患者病情的严重程度。对数字心电图进行重复记录（治疗开始时以及治疗期间2至4次）。对每份心电图分析总共240个主要和复合心电图参数。在这些患者中，有6例在治疗期间死亡。采用聚类分析来识别在疾病严重程度（NEWS）、血氧饱和度（SpO₂）和整体心电图指数（心血管系统状态指标）方面存在显著差异的患者亚组。
使用方差分析（重复测量方差分析）对治疗结束时亚组指标的变化进行了统计学评估。这些亚组差异在治疗结束时仍然存在。为了确定潜在的死亡预测因素，使用参数和非参数统计检验比较了存活（S）和未存活患者（D）的关键临床和心电图参数。基于部分心电图参数和NEWS评分构建了一个用于对新冠患者生存情况进行分类的决策树模型。
潜在死亡预测因素的比较显示，治疗开始时幸存者和非幸存者的生命体征无显著差异。确定了一组与治疗结果显著相关且可能是新冠死亡率预测因素的心电图参数：T波形态（SVD）、Q波振幅和R波振幅（I导联）。
