The effect of coronavirus infection on QT and QTc intervals of hospitalized patients in Qazvin, Iran

Q1 Decision Sciences Annals of Data Science Pub Date : 2022-08-04 DOI:10.1007/s40745-022-00425-5
Azadeh Najjar, Abbas Allami, Samira Dodangeh, Mohammad Mahdi Daei
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Abstract

Electrocardiographic (ECG) changes have been investigated in the condition of coronavirus disease (COVID-19) indicating that COVID-19 infection exacerbates arrhythmias and triggers conduction abnormalities. However, the specific type of ECG abnormalities in COVID-19 and their impact on mortality fail to have been fully elucidated. The present retrospective, tertiary care hospital-based cross-sectional study was conducted by reviewing the medical records of all patients diagnosed with COVID-19 infection who were admitted to Booali Sina Hospital in Qazvin, Iran from March to July 2020. Demographic information, length of hospital stay, treatment outcome, and electrocardiographic information (heart rate, QTc interval, arrhythmias, and blocks) were extracted from the medical records of the patients. Finally, a total of 231 patients were enrolled in the study. Atrial fibrillation was a common arrhythmia, and the left anterior fascicular block was a common cardiac conduction defect other than sinus arrhythmia. The deceased patients were significantly older than the recovered ones (71 ± 14 vs. 57 ± 16 years, p < 0.001). Longer hospital stay (p = 0.036), non-sinus rhythm (p < 0.001), bundle and node blocks (p = 0.002), ST-T waves changes (p = 0.003), and Tachycardia (p = 0.024) were significantly prevalent in the deceased group. In baseline ECGs, no significant difference was observed in terms of the absolute size of QT; however, a prolonged QTc in the deceased was about twice of the recovered patients (using Bazett, Sagie, and Fridericia’s formula). Serial ECGs are recommended to be taken from all hospitalized patients with COVID-19 due to increased in-hospital mortality in patients with prolonged QTc interval, non-sinus rhythms, ST-T changes, tachycardia, and bundle, and node blocks.

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冠状病毒感染对伊朗Qazvin住院患者QT和QTc间期的影响
冠状病毒病(COVID-19)的心电图(ECG)变化研究表明,COVID-19 感染会加重心律失常并引发传导异常。然而,COVID-19 中心电图异常的具体类型及其对死亡率的影响尚未完全阐明。本项以三级医院为基础的横断面回顾性研究通过回顾 2020 年 3 月至 7 月期间伊朗加兹温市 Booali Sina 医院收治的所有确诊感染 COVID-19 的患者的病历进行。从患者病历中提取了人口统计学信息、住院时间、治疗结果和心电图信息(心率、QTc 间期、心律失常和阻滞)。最后,共有 231 名患者被纳入研究。心房颤动是常见的心律失常,左前束传导阻滞是窦性心律失常以外常见的心脏传导缺陷。死亡患者的年龄明显大于康复患者(71 ± 14 岁对 57 ± 16 岁,p < 0.001)。死亡组中,住院时间较长(p = 0.036)、非窦性心律(p < 0.001)、束和结阻滞(p = 0.002)、ST-T 波变化(p = 0.003)和心动过速(p = 0.024)明显多见。在基线心电图中,没有观察到 QT 绝对值的显著差异;然而,死亡患者的 QTc 延长约为康复患者的两倍(使用 Bazett、Sagie 和 Fridericia 的公式)。由于 QTc 间期延长、非窦性心律、ST-T 改变、心动过速、束状和结节阻滞患者的院内死亡率增加,建议对所有 COVID-19 住院患者进行连续心电图检查。
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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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