Long-Term Electrocardiographic Changes in Healthcare Workers Following Mild to Moderate Cases of Coronavirus (COVID-19): A Longitudinal Observational Study

<b>Background</b>: The cardiovascular effects of SARS-CoV-2, including autonomic dysregulation, are becoming increasingly recognized, even following mild infections. However, long-term electrocardiographic (ECG) changes remain poorly characterized. <b>Methods</b>: We conducted a prospective study of 151 unvaccinated healthcare workers with RT-PCR-confirmed mild to moderate SARS-CoV-2 infection. Standard 12-lead ECGs were recorded before infection (T0) and at 6-12 months (T1) and >12 months (T2) after infection. Key parameters included heart rate (HR), PR interval, QRS duration, and corrected QT interval (QTc). <b>Results</b>: Heart rate (HR) increased transiently at T1 (<i>p</i> < 0.05) and normalized by T2. Mild but persistent PR interval shortening was observed at both follow-ups (<i>p</i> < 0.01). There were no significant changes in QRS or QTc intervals. No arrhythmias or conduction blocks occurred. ECG alterations were not associated with sex or age, except for greater PR shortening in males. <b>Conclusions</b>: Mild SARS-CoV-2 infection can result in transient sinus tachycardia and subtle PR shortening, which is likely to be a post-viral autonomic effect. Long-term ECG surveillance appears unnecessary in asymptomatic cases.

Authors

Institutions

Publication Details

Journal
Healthcare
Published
2025-07-24
DOI
https://doi.org/10.3390/healthcare13151799
Primary Topic
Long-Term Effects of COVID-19
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Long-Term Electrocardiographic Changes in Healthcare Workers Following Mild to Moderate Cases of Coronavirus (COVID-19): A Longitudinal Observational Study

Cristiana Ferrari, Luca Coppeta, Andrea Attanasio, Giuseppina Somma et al.
Healthcare
Long-Term Effects of COVID-19
article

Long-Term Electrocardiographic Changes in Healthcare Workers Following Mild to Moderate Cases of Coronavirus (COVID-19): A Longitudinal Observational Study

Cristiana Ferrari, Luca Coppeta, Andrea Attanasio, Giuseppina Somma, A Magrini, Stella Andreadi
article en

Abstract

<b>Background</b>: The cardiovascular effects of SARS-CoV-2, including autonomic dysregulation, are becoming increasingly recognized, even following mild infections. However, long-term electrocardiographic (ECG) changes remain poorly characterized. <b>Methods</b>: We conducted a prospective study of 151 unvaccinated healthcare workers with RT-PCR-confirmed mild to moderate SARS-CoV-2 infection. Standard 12-lead ECGs were recorded before infection (T0) and at 6-12 months (T1) and >12 months (T2) after infection. Key parameters included heart rate (HR), PR interval, QRS duration, and corrected QT interval (QTc). <b>Results</b>: Heart rate (HR) increased transiently at T1 (<i>p</i> < 0.05) and normalized by T2. Mild but persistent PR interval shortening was observed at both follow-ups (<i>p</i> < 0.01). There were no significant changes in QRS or QTc intervals. No arrhythmias or conduction blocks occurred. ECG alterations were not associated with sex or age, except for greater PR shortening in males. <b>Conclusions</b>: Mild SARS-CoV-2 infection can result in transient sinus tachycardia and subtle PR shortening, which is likely to be a post-viral autonomic effect. Long-term ECG surveillance appears unnecessary in asymptomatic cases.

HealthcareVol. 13(15)
University of Rome Tor Vergata (IT)
Good health and well-being
Openalex Percentile: Top 72%
Long-Term Effects of COVID-19
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.