Colchicine: A Possible COVID-19 Long haul Cardiac Therapy

Last Updated: December 16, 2021

Last Updated: December 16, 2021

Colchicine is an anti-inflammatory drug that is used to treat a variety of conditions, including gout, recurrent pericarditis, and familial Mediterranean fever.1 Recently, the drug has been shown to potentially reduce the risk of cardiovascular events in those with coronary artery disease.2 Colchicine has several potential mechanisms of action, including reducing the chemotaxis of neutrophils, inhibiting inflammasome signaling, and decreasing the production of cytokines, such as interleukin-1 beta.3 When colchicine is administered early in the course of COVID-19, these mechanisms could potentially mitigate or prevent inflammation-associated manifestations of the disease. These anti-inflammatory properties coupled with the drug’s limited immunosuppressive potential, favorable safety profile, and widespread availability have prompted investigation of colchicine for the treatment of COVID-19.


  • The COVID-19 Treatment Guidelines Panel (the Panel) recommends against the use of colchicine for the treatment of nonhospitalized patients with COVID-19, except in a clinical trial (BIIa).
  • The Panel recommends against the use of colchicine for the treatment of hospitalized patients with COVID-19 (AI).


For Nonhospitalized Patients With COVID-19

COLCORONA, a large randomized placebo-controlled trial that evaluated colchicine in outpatients with COVID-19, did not reach its primary efficacy endpoint of reducing hospitalizations and death.4 However, in the subset of patients whose diagnosis was confirmed by a positive SARS-CoV-2 polymerase chain reaction (PCR) result from a nasopharyngeal (NP) swab, a slight reduction in hospitalizations was observed among those who received colchicine.

PRINCIPLE, another randomized, open-label, adaptive-platform trial that evaluated colchicine versus usual care, was stopped for futility when no significant difference in time to first self-reported recovery from COVID-19 between the colchicine and usual care recipients was found.5

The PRINCIPLE trial showed no benefit of colchicine, and the larger COLCORONA trial failed to reach its primary endpoint, found only a very modest effect of colchicine in the subgroup of patients with positive SARS-CoV-2 PCR results, and reported more gastrointestinal adverse events in those receiving colchicine. Therefore, the Panel recommends against the use of colchicine for the treatment of COVID-19 in nonhospitalized patients, except in a clinical trial (BIIa).

For Hospitalized Patients With COVID-19

In the RECOVERY trial, a large randomized trial in hospitalized patients with COVID-19, colchicine demonstrated no benefit with regard to 28-day mortality or any secondary outcomes.6 Based on the results from this large trial, the Panel recommends against the use of colchicine for the treatment of COVID-19 in hospitalized patients (AI).

Clinical Data for COVID-19

Colchicine in Nonhospitalized Patients With COVID-19


The COLCORONA trial was a contactless, double-blind, placebo-controlled, randomized trial in outpatients who received a diagnosis of COVID-19 within 24 hours of enrollment. Participants were aged ≥70 years or aged ≥40 years with at least 1 of the following risk factors for COVID-19 complications: body mass index ≥30, diabetes mellitus, uncontrolled hypertension, known respiratory disease, heart failure or coronary disease, fever ≥38.4°C within the last 48 hours, dyspnea at presentation, bicytopenia, pancytopenia, or the combination of high neutrophil count and low lymphocyte count. Participants were randomized 1:1 to receive colchicine 0.5 mg twice daily for 3 days and then once daily for 27 days or placebo. The primary endpoint was a composite of death or hospitalization by Day 30; secondary endpoints included components of the primary endpoint, as well as the need for mechanical ventilation by Day 30. Participants reported by telephone the occurrence of any study endpoints at 15 and 30 days after randomization; in some cases, clinical data were confirmed or obtained by medical chart reviews.4


  • The study enrolled 4,488 participants.
  • The primary endpoint occurred in 104 of 2,235 participants (4.7%) in the colchicine arm and 131 of 2,253 participants (5.8%) in the placebo arm (OR 0.79; 95% CI, 0.61–1.03; P = 0.08).
  • There were no statistically significant differences in the secondary outcomes between the arms.
  • In a prespecified analysis of 4,159 participants who had a SARS-CoV-2 diagnosis confirmed by PCR testing of an NP specimen (93% of those enrolled), those in the colchicine arm were less likely to reach the primary endpoint (96 of 2,075 participants [4.6%]) than those in the placebo arm (126 of 2,084 participants [6.0%]; OR 0.75; 95% CI, 0.57–0.99; P = 0.04). In this subgroup of patients with PCR-confirmed SARS-CoV-2 infection, there were fewer hospitalizations (a secondary outcome) in the colchicine arm (4.5% of patients) than in the placebo arm (5.9% of patients; OR 0.75; 95% CI, 0.57–0.99).
  • More participants in the colchicine arm experienced gastrointestinal adverse events, including diarrhea which occurred in 13.7% of colchicine recipients versus 7.3% of placebo recipients (P < 0.0001). Unexpectedly, more pulmonary emboli were reported in the colchicine arm than in the placebo arm (11 events [0.5% of patients] vs. 2 events [0.1% of patients]; P= 0.01).


  • Due to logistical difficulties with staffing, the trial was stopped at approximately 75% of the target enrollment, which may have limited the study’s power to detect differences for the primary outcome.
  • There was uncertainty as to the accuracy of COVID-19 diagnoses in presumptive cases.
  • Some patient-reported clinical outcomes were potentially misclassified.


PRINCIPLE is a randomized, open-label, platform trial that evaluated colchicine in symptomatic, nonhospitalized patients with COVID-19 who were aged ≥65 years or aged ≥18 years with comorbidities or shortness of breath, and who had symptoms for ≤14 days. Participants were randomized to receive colchicine 0.5 mg daily for 14 days or usual care. The coprimary endpoints, which included time to first self-reported recovery or hospitalization or death due to COVID-19 by Day 28, were analyzed using a Bayesian model. Participants were followed through symptom diaries that they completed online daily; those who did not complete the diaries were contacted by telephone on Days 7, 14, and 29. The investigators developed a prespecified criterion for futility, specifying a clinically meaningful benefit in time to first self-reported recovery as a hazard ratio ≥1.2, corresponding to about 1.5 days of faster recovery in the colchicine arm.


  • The study enrolled 4,997 participants: 212 participants were randomized to receive colchicine; 2,081 to receive usual care alone; and 2,704 to receive other treatments.
  • The prespecified primary analysis included participants with SARS-CoV-2 positive test results (156 in the colchicine arm; 1,145 in the usual care arm; and 1,454 in the other treatments arm).
  • The trial was stopped early because the criterion for futility was met; the median time to self-reported recovery was similar in the colchicine arm and the usual care arm (HR 0.92; 95% CrI, 0.72–1.16).
  • Analyses of self-reported time to recovery and hospitalizations or death due to COVID-19 among concurrent controls also showed no significant differences between the colchicine and usual care arms.
  • There were no statistically significant differences in the secondary outcomes between the colchicine and usual care arms in both the primary analysis population and in subgroups, including subgroups based on symptom duration, baseline disease severity, age, or comorbidities.
  • The occurrence of adverse events was similar in the colchicine and usual care arms.


  • The design of the study was open-label treatment.
  • The sample size of the colchicine arm was small.

Colchicine in Hospitalized Patients With COVID-19


In the RECOVERY trial, hospitalized patients with COVID-19 were randomized to receive colchicine (1 mg loading dose, followed by 0.5 mg 12 hours later, and then 0.5 mg twice daily for 10 days or until discharge) or usual care.6


  • The study enrolled 11,340 participants.
  • At randomization, 10,603 patients (94%) were receiving corticosteroids.
  • The primary endpoint of all-cause mortality at Day 28 occurred in 1,173 of 5,610 participants (21%) in the colchicine arm and 1,190 of 5,730 participants (21%) in the placebo arm (rate ratio 1.01; 95% CI, 0.93–1.10; P = 0.77).
  • There were no statistically significant differences between the arms for the secondary outcomes of median time to being discharged alive, discharge from the hospital within 28 days, and receipt of mechanical ventilation or death.
  • The incidence of new cardiac arrhythmias, bleeding events, and thrombotic events was similar in the 2 arms. Two serious adverse events were attributed to colchicine: 1 case of severe acute kidney injury and one case of rhabdomyolysis.


  • The trial’s open-label design may have introduced bias for assessing some of the secondary endpoints.

The GRECCO-19 Trial

GRECCO-19 was a small, prospective, open-label randomized clinical trial in 105 patients hospitalized with COVID-19 across 16 hospitals in Greece. Patients were assigned 1:1 to receive standard of care with colchicine (1.5 mg loading dose, followed by 0.5 mg after 60 minutes and then 0.5 mg twice daily until hospital discharge or for up to 3 weeks) or standard of care alone.7


  • Fewer patients in the colchicine arm (1 of 55 patients) than in the standard of care arm (7 of 50 patients) reached the primary clinical endpoint of deterioration in clinical status from baseline by 2 points on a 7-point clinical status scale (OR 0.11; 95% CI, 0.01–0.96).
  • Participants in the colchicine group were significantly more likely to experience diarrhea (occurred in 45.5% of participants in the colchicine arm vs. 18.0% in the standard of care arm; P = 0.003).


  • The overall sample size and the number of clinical events reported were small.
  • The study design was open-label treatment assignment.

The results of several small randomized trials and retrospective cohort studies that have evaluated various doses and durations of colchicine in hospitalized patients with COVID-19 have been published in peer-reviewed journals or made available as preliminary, non-peer-reviewed reports.8-11 Some have shown benefits of colchicine use, including less need for supplemental oxygen, improvements in clinical status on an ordinal clinical scale, and reductions in certain inflammatory markers. In addition, some studies have reported higher discharge rates or fewer deaths among patients who received colchicine than among those who received comparator drugs or placebo. However, the findings of these studies are difficult to interpret due to significant design or methodological limitations, including small sample sizes, open-label designs, and differences in the clinical and demographic characteristics of participants and permitted use of various cotreatments (e.g., remdesivir, corticosteroids) in the treatment arms.

Adverse Effects, Monitoring, and Drug-Drug Interactions

Common adverse effects of colchicine include diarrhea, nausea, vomiting, abdominal cramping and pain, bloating, and loss of appetite. In rare cases, colchicine is associated with serious adverse events, such as neuromyotoxicity and blood dyscrasias. Use of colchicine should be avoided in patients with severe renal insufficiency, and patients with moderate renal insufficiency who receive the drug should be monitored for adverse effects. Caution should be used when colchicine is coadministered with drugs that inhibit cytochrome P450 (CYP) 3A4 and/or P-glycoprotein (P-gp) because such use may increase the risk of colchicine-induced adverse effects due to significant increases in colchicine plasma levels. The risk of myopathy may be increased with the concomitant use of certain HMG-CoA reductase inhibitors (e.g., atorvastatin, lovastatin, simvastatin) due to potential competitive interactions mediated by CYP3A4 and P-gp pathways.12,13 Fatal colchicine toxicity has been reported in individuals with renal or hepatic impairment who received colchicine in conjunction with P-gp inhibitors or strong CYP3A4 inhibitors.

Considerations in Pregnancy

There are limited data on the use of colchicine in pregnancy. Fetal risk cannot be ruled out based on data from animal studies and the drug’s mechanism of action. Colchicine crosses the placenta and has antimitotic properties, which raises a theoretical concern for teratogenicity. However, a recent meta-analysis did not find that colchicine exposure during pregnancy increased the rates of miscarriage or major fetal malformations. There are no data for colchicine use in pregnant women with acute COVID-19. Risks of use should be balanced against potential benefits.12,14

Considerations in Children

Colchicine is most commonly used in children to treat periodic fever syndromes and autoinflammatory conditions. Although colchicine is generally considered safe and well tolerated in children, there are no data on the use of the drug to treat pediatric acute COVID-19 or multisystem inflammatory syndrome in children (MIS-C).


  1. van Echteld I, Wechalekar MD, Schlesinger N, Buchbinder R, Aletaha D. Colchicine for acute gout. Cochrane Database Syst Rev. 2014(8):CD006190. Available at:
  2. Xia M, Yang X, Qian C. Meta-analysis evaluating the utility of colchicine in secondary prevention of coronary artery disease. Am J Cardiol. 2021;140:33-38. Available at:
  3. Reyes AZ, Hu KA, Teperman J, et al. Anti-inflammatory therapy for COVID-19 infection: the case for colchicine. Ann Rheum Dis. 2021 May;80(5):550-557. Available at:
  4. Tardif JC, Bouabdallaoui N, L’Allier PL, et al. Colchicine for community-treated patients with COVID-19 (COLCORONA): a phase 3, randomised, double-blinded, adaptive, placebo-controlled, multicentre trial. Lancet Respir Med. 2021;9(8):924-932. Available at:
  5. PRINCIPLE Trial Collaborative Group, Dorward J, Yu L, et al. Colchicine for COVID-19 in adults in the community (PRINCIPLE): a randomised, controlled, adaptive platform trial. medRxiv. 2021;Preprint. Available at:
  6. RECOVERY Collaborative Group. Colchicine in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial. Lancet Respir Med. 2021;Published online ahead of print. Available at:
  7. Deftereos SG, Giannopoulos G, Vrachatis DA, et al. Effect of colchicine vs standard care on cardiac and inflammatory biomarkers and clinical outcomes in patients hospitalized with coronavirus disease 2019: the GRECCO-19 randomized clinical trial. JAMA Netw Open. 2020;3(6):e2013136. Available at:
  8. Brunetti L, Diawara O, Tsai A, et al. Colchicine to weather the cytokine storm in hospitalized patients with COVID-19. J Clin Med. 2020;9(9). Available at:
  9. Sandhu T, Tieng A, Chilimuri S, Franchin G. A case control study to evaluate the impact of colchicine on patients admitted to the hospital with moderate to severe COVID-19 infection. Can J Infect Dis Med Microbiol. 2020. Available at:
  10. Lopes MI, Bonjorno LP, Giannini MC, et al. Beneficial effects of colchicine for moderate to severe COVID-19: a randomised, double-blinded, placebo-controlled clinical trial. RMD Open. 2021;7(1). Available at:
  11. Salehzadeh F, Pourfarzi F, Ataei S. The impact of colchicine on the COVID-19 patients; a clinical trial. Research Square. 2020;Preprint. Available at:
  12. Colchicine (Colcrys) [package insert]. Food and Drug Administration. 2012. Available at:
  13. American College of Cardiology. AHA statement on drug-drug interactions with statins. 2016. Available at: Accessed November 2, 2021.
  14. Indraratna PL, Virk S, Gurram D, Day RO. Use of colchicine in pregnancy: a systematic review and meta-analysis. Rheumatology (Oxford). 2018;57(2):382-387. Available at:

www.covid19treatmentguidelines.nih.govAn official website of the National Institutes of Health

Electrocardiographic Changes in COVID-19 Patients: A Hospital-based Descriptive Study

Authors: Deepalakshmi Kaliyaperumal,1Kumar Bhargavi,2Karthikeyan Ramaraju,3Krishna S Nair,4Sudha Ramalingam,5 and Murali Alagesan6

Indian J Crit Care Med. 2022 Jan; 26(1): 43–48.doi: 10.5005/jp-journals-10071-24045PMCID:  PMC8783240PMID: 35110843



Coronavirus disease-2019 (COVID-19) infection is a multisystem disease not restricted to the lungs. It has a negative impact on the cardiovascular system by causing myocardial damage, vascular inflammation, plaque instability, and myocardial infarction. The presence of myocardial injury is a poor prognostic sign. Electrocardiogram (ECG), a simple bedside diagnostic test with high prognostic value, can be employed to assess early cardiovascular involvement in such patients. Various abnormalities in ECG like ST-T changes, arrhythmia, and conduction defects have been reported in COVID-19. We aimed to find out the ECG abnormalities of COVID-19 patients.


We performed a cross-sectional, hospital-based descriptive study among 315 COVID-19 in-patients who underwent ECG recording on admission. Patients’ clinical profiles were noted from their records, and the ECG abnormalities were studied.


Among the abnormal ECGs 255 (81%), rhythm abnormalities were seen in 9 patients (2.9%), rate abnormalities in 115 patients (36.5%), and prolonged PR interval in 2.9%. Short QRS complex was seen in 8.3%. QT interval was prolonged in 8.3% of the patients. Significant changes in the ST and T segments (42.9%) were observed. In logistic regression analysis, ischemic changes in ECG were associated with systemic hypertension and respiratory failure.


In our study, COVID-19 patients had ischemic changes, rate, rhythm abnormalities, and conduction defects in their ECG. With this ongoing pandemic of COVID-19 and limited health resources, ECG—a simple bedside noninvasive tool is highly beneficial and helps in the early diagnosis and management of cardiac injury.

How to cite this article

Kaliyaperumal D, Bhargavi K, Ramaraju K, Nair KS, Ramalingam S, Alagesan M. Electrocardiographic Changes in COVID-19 Patients: A Hospital-based Descriptive Study. Indian J Crit Care Med 2022;26(1):43–48.Keywords: Coronavirus disease-2019, Electrocardiogram change, Rate abnormalities, ST-T changes


A cluster of pneumonia cases were reported due to “severe acute respiratory syndrome coronavirus 2” (SARS-CoV-2) at the end of 2019 in the city of Wuhan, in the Hubei Province of China. Soon coronavirus disease-2019 (COVID-19) was declared as a pandemic owing to its rapid spread across the countries.1 Initially regarded as a respiratory infection, COVID-19 is now known to affect all major systems in the body. Quite a lot is discussed in literature last year about COVID-19 and its effect on lungs and systemic response. However, very little is debated about cardiovascular involvement in COVID. It has been observed that lung involvement is more severe in patients with preexisting cardiac involvement. However, in sharp contrast new-onset cardiac involvement is also noted in a few patients and few patients do present with cardiac symptoms alone without lung involvement.2 The spectrum of presentation is wide-ranging from patients having no cardiac disease at all, asymptomatic but with elevated cardiac markers, having symptoms of overt cardiac disease such as angina, cardiogenic shock, heart failure, cardiac arrhythmias, and sudden cardiac death.

Arrhythmia and acute cardiac injury were reported in 16.7 and 7.2% of the COVID patients.3 In addition to the systemic inflammatory response, the physiological mechanisms identified to cause cardiac involvement in COVID-19 patients are hypoxemia-related myocardial cell injury and endothelial cell damage due to upregulated expression of angiotensin-converting enzyme 2 (ACE 2) in the heart and lungs.4

The electrocardiogram (ECG) changes reflect cardiac involvement with diverse manifestations. Arrhythmia and conduction defects are found to be more prevalent among SARS-CoV-2-infected individuals.5 Myocardial ischemia, myocarditis, shock, hypoxia, and electrolyte abnormalities were the factors identified to cause arrhythmias.6 The presence of cardiac involvement may imply poor prognosis and an adverse outcome.7 Therefore, it is pertinent to assess and monitor the cardiac abnormalities paving way for a prompt action. ECG, a simple bedside diagnostic test with high prognostic value, can be employed to assess cardiovascular involvement in COVID-19 patients. We aimed to find out the ECG abnormalities of patients with SARS-CoV-2 infection.

Materials and Methods

This cross-sectional, hospital-based descriptive study was conducted among 315 COVID-19 patients admitted in our tertiary care center during October to December 2020 after obtaining the human institutional ethics committee clearance and informed consent from the patients participating in the study [IHEC NO: Project No: 20/217]. Patients whose COVID status was confirmed by real-time reverse transcriptase polymerase chain reaction on nasopharyngeal and oropharyngeal swabs were included in the study.

Consecutive patients admitted to our hospital with SARS-CoV-2-positive status underwent ECG testing on admission and were included in the study. Patients’ clinical profiles that include symptoms, duration, and severity of illness, and comorbid status were noted from their clinical records. ECGs were reviewed and interpreted by two physicians (together responsible for the interpretation of >100,000 ECGs per year) who were blinded to the clinical status of the patients. Patients with ventricular pacing, immune suppression, stroke, malignancy and patients on beta blockers and anti-arrhythmic drugs were excluded.

The ECG data include heart rate, rhythm categorized as normal sinus rhythm or atrial fibrillation/flutter, atrial premature contractions, ventricular premature contractions, atrioventricular block, axis deviation, bundle branch block, intraventricular conduction block (QRS duration of >110 ms), Bazett-corrected QT interval (in milliseconds), presence of left or right ventricular hypertrophy, myocardial infarction, and the presence of ST segment or T-wave changes (localized ST elevation, localized T-wave inversion, or other nonspecific repolarization abnormalities).

Statistical Analysis

The data collected from the patients were tabulated using Microsoft Excel. Descriptive statistics were employed for analysis. Data were expressed as mean ± standard deviation for continuous variables and proportions for categorical variables. Logistic regression analysis was employed to study the association between clinical variables and occurrence of various types of ECG abnormalities. The results were expressed in odds ratio with 95% confidence interval after adjusting for important confounders.


A total of 315 patients satisfying the inclusion criteria were included in the study. Out of the total 315 patients studied, 92 (29.2%) were females and 223 (70.8%) were males with an average age of 52.6 ± 16.3 years. Clinical characteristics like symptoms on admission, severity and duration of illness, duration of the hospital stay, disease course, and outcomes are depicted in Table 1.

Table 1

Demographic and clinical characteristics of the study population

Demographic and clinical variablesN = 315
Age (mean±SD)52.6±16.3
Age distribution 
15–30years29 (9.2%)
31–45years77 (24.4%)
46–60years100 (31.7%)
61–75years83 (26.3%)
>75years26 (8.2%)
Male223 (70.8%)
Female92 (29.2%)
Duration of illness (at admission) 
Median duration (days)3
Range (days)0–30
Asymptomatic69 (21.9%)
Symptomatic (at least one of the below)246 (78.1%)
Fever154 (62.6%)
Cough133 (54.0%)
Breathlessness74 (30.0%)
Diarrhea32 (13.0%)
Anosmia/ageusia21 (8.5%)
Others98 (39.8%)
Diabetes mellitus116 (36.8%)
Systemic hypertension96 (30.5%)
Heart diseases30 (9.5%)
Respiratory diseases15 (4.6%)
Thyroid diseases13 (4.1%)
Kidney diseases4 (1.3%)
At least one comorbid illness139 (44.1%)
No comorbidities176 (55.9%)
Disease course during hospital stay 
Clinical deterioration68 (21.6%)
Clinically stable and improving231 (73.3%)
Subjects with oxygen requirement108 (34.3%)
Subjects with ICU admission (>48hours)63 (20.0%)
Duration of hospital stay 
Median duration (days)9.00
Range (days)1–32
Discharged296 (93.9%)
Died (in-hospital mortality—all-cause mortality)19 (6.0%)

Open in a separate window

ECG abnormalities encountered in the study population with respect to the rate, rhythm, PR interval, axis deviation, QRS complex, QT interval, and ST and T-wave changes are shown in Figure 1.

An external file that holds a picture, illustration, etc.
Object name is ijccm-26-43-g001.jpg

Open in a separate windowFig. 1

ECG abnormalities in the study population

Among the abnormal ECGs 255 (81%), rhythm abnormalities were seen in 9 patients (2.9%); rate abnormalities in 115 patients (36.5%)—bradycardia (12.7%) and tachycardia (23.8%); and prolonged PR interval in 2.9% patients. Short QRS complex was seen in 8.3%. QT interval was prolonged in 8.3% of the patients. There were significant changes in the ST and T segments (Table 2).

Table 2

Distribution of ECG changes at admission among the study population

ECG changesFrequency (%) (N = 315)
Normal ECG60 (19.0%)
Irregular rhythm9 (2.9%)
Abnormal rate
Sinus bradycardia40 (12.7%)
Sinus tachycardia95 (23.8%)
Axis deviation
Left91 (28.9%)
Right0 (0.0%)
PR interval
Shortened PR interval4 (1.4%)
Prolonged PR interval9 (2.9%)
QRS complex
Short QRS complex26 (8.3%)
Widened QRS complex9 (2.9%)
Poor progression of R-waves91 (28.9%)
QT interval
Shortened QT interval25 (7.9%)
Prolonged QT interval26 (8.3%)
ST segment
ST elevation27 (8.6%)
ST depression16 (5.1%)
ST flattening/coving10 (3.2%)
T-wave inversion75 (23.8%)
Tall T-waves7 (2.2%)

Open in a separate window

In logistic regression model (Table 3), subjects with moderate-to-severe COVID-19 illness were twice likely to have at least one of the above-described abnormalities in ECG independent upon age, gender, and preexisting cardiac diseases [adjusted odds ratio 2.02 (95% confidence interval 1.04–3.95)]. Among all subjects, ischemic changes in ECG (ST segment changes and T-wave inversion) appeared to be associated with systemic hypertension [adjusted odds ratio 1.73 (95% confidence interval 0.96–3.11)] and respiratory failure [adjusted odds ratio 1.58 (95% confidence interval 0.94–2.66)] after adjusting age, gender, and preexisting heart diseases. The above-mentioned associations showed a trend toward statistical significance. No other ECG changes had any significant association with clinical variables studied.

Table 3

Logistic regression analysis of association between ECG changes and clinical variables

Variable-associated ECG abnormalitiesUnadjusted odds ratio (95% confidence interval)Adjusted odds ratio (95% confidence interval)
Ischemic changes in ECG (ST segment elevation/depression and/or T inversion)
Systemic hypertension1.84 (1.113–3.055)*1.73 (0.96–3.11)
Respiratory failure on admission1.71 (1.049–2.79)*1.58 (0.94–2.66)

Open in a separate windowAdjustment model: age, gender, and preexisting heart diseases.*p <0.05

Of the 315 patients, 19 patients died ultimately due to COVID. The ECG abnormalities studied in these patients are shown in Figure 2. Prolongation of QTc interval (42%) and tachycardia (36.8%) were the commonest changes noted in them. The various ECG abnormalities encountered in the study population and the outcomes in each group are depicted in Figure 3. Adverse final outcomes were noted in 11.5% of the patients who had ST-T changes and QTc prolongation and 8.4% of the patients who had tachycardia.

An external file that holds a picture, illustration, etc.
Object name is ijccm-26-43-g002.jpg

Open in a separate windowFig. 2

Pie chart showing the final outcomes of the study population and various ECG abnormalities in the deceased population

An external file that holds a picture, illustration, etc.
Object name is ijccm-26-43-g003.jpg

Open in a separate windowFig. 3

Stacked column chart depicting the various ECG abnormalities and patient outcomes in each category


Myocardial injury associated with cardiac dysfunction and arrhythmias has been reported in infectious diseases. ECG changes observed in infections include hemorrhagic fever,8,9 leptospirosis,10 scrubtyphus,11 diphtheria,12 trichinellosis,13 and trypanosomiasis.14 Myocardial injury observed in dengue viral infection is evidenced by the presence of ECG abnormalities like atrial and ventricular premature beats, prolonged PR interval, bundle branch block s, and ST and T segment changes.15 Abnormal ECG findings were found to be reported in 28% of the hospitalized patients infected with novel H1N1 influenza virus.16 Similarly, now there is growing evidence that SARS-CoV-2 also has the potential to have a negative impact on the cardiovascular system.

There are multiple proposed mechanisms for cardiac damage in COVID-19. These include cytokine release syndrome,17 direct myocardial damage as in viral myocarditis due to the interaction between virus and ACE 2,18,19 coronary spasm, induction of a hypercoagulable state, plaque instability causing rupture, and acute coronary syndrome.20 Other potential mechanisms may include cardiac toxicity due to antivirals, steroids, and electrolyte abnormalities.

Even the earliest cases in China had evidence of myocardial injury21 and previous studies did estimate the prevalence as between 1 and 7% of the patients and 26% required intensive care.22 Studies by Shi et al. also inferred that cardiac involvement was associated with high mortality.23

In our study, we observed sinus tachycardia (23.8%), sinus bradycardia (12.7%), and atrial arrhythmia (3.5%). This is in accordance with a study by Brit Long where the commonest ECG abnormality in COVID patients was sinus tachycardia followed by atrial fibrillation, ventricular arrhythmias, QTc prolongation, and ST-T segment changes.24 Atrial fibrillation (3.5%), bradyarrhythmia (1.2%), and nonsustained VT (10.4%) were reported in another study conducted among 700 patients with severe acute respiratory syndrome due to SARS-CoV-2 infection.25

In our study, we encountered ischemic changes (ST segment elevation, T-wave inversion) in 32.4% of the COVID-19 patients irrespective of their underlying cardiac health. Italy published a research study of 28 COVID-19 patients who underwent angiogram for ST elevation myocardial infarction in whom 86% had STEMI as the first presentation of COVID showing that acute coronary event had preceded systemic inflammation. Of these, 79% had typical chest pain, while 21% presented with dyspnea without any chest pain.26

In the present study, 16.2% of the COVID-19 patients presented with QT segment changes (prolonged and shortened). QT interval prolongation has been noted in about 13% of the COVID-19 patients. Major contributing factors to this particular abnormality may be the list of several (now unapproved) drugs previously used for COVID-19 treatment like hydroxychloroquine and azithromycin.27,28 QT interval prolongation may cause rhythm disturbances and hemodynamic instability requiring ICU admission and if not attended to may cause sudden cardiac death.

Pulmonary embolism may be a presenting issue of COVID-19 as well as its complication. A recent study of ECG findings in pulmonary embolism in COVID patients showed that abnormalities were mostly nonspecific including sinus tachycardia and minimal ST segment or T-wave changes. Specific and classic findings (classic S1Q3T3 pattern) were seen in less than 10% of the patients.29

All the 19 COVID patients who had succumbed to death had abnormal ECG findings. In a retrospective study to highlight the prognostic significance of ECG in COVID, Yang et al. have compared the ECG changes in survivors and nonsurvivors.30 It was observed that the nonsurvivors had significantly higher rates of prolonged QTc interval, axis deviation, arrhythmias, ST-T changes, and an overall higher abnormal ECG score. In our study population, QTc prolongation and tachycardia were the commonest changes in the deceased.

In a retrospective ECG analysis in the COVID-19 patients, Wang et al. have studied the ECG characteristics in the critically severe and severe group of patients.31 He has observed that 84.5% of the patients had abnormal ECG findings in the critically severe group as against 53% in the severe group. ST-T changes (48.5%) and sinus tachycardia (30%) were the most common abnormalities noted in the critically severe group of patients. In our study population, mortality was observed in 11.5% of the patients who had ST-T changes, 11.5% of the patients who had QTc prolongation, and 8.4% of those who had sinus tachycardia.


Other factors that influence the ECG findings such as age, body mass index (BMI), electrolyte imbalances, inflammatory markers, and specifically cardiac markers were not considered in the analysis. We wish to extend the present study to find out the influence of SARS-CoV-2 virus on electrophysiology of cardiac muscle excluding these factors that affect the ECG parameters. Moreover, correlation of ECG findings with echocardiogram, clinical outcomes, and follow-up will help us understand the pathophysiology of cardiac diseases in COVID-19 disease. This will strengthen the race against COVID infection by enriching our knowledge and unraveling further mysteries around this mysterious infection.


In our study, COVID-19 patients presented with ischemic changes, rhythm abnormalities, and conduction defects. With SARS-CoV-2 having already gained momentum worldwide, it is important to deploy simple, cost-effective bedside examination, and diagnostic tests considering our limited health resources. ECG is of paramount importance in the Emergency COVID Department too as it is central to risk stratification and is predictive of an adverse outcome.


  • SARS-CoV-2 extends its prongs well beyond the lungs.
  • There are multiple mechanisms for myocardial damage in COVID-19.
  • Myocardial injury when present is a poor prognostic sign.
  • ECG is a simple bedside diagnostic test to screen for cardiac abnormalities.
  • The commonest ECG abnormalities in our study were sinus tachycardia, ischemic changes, and QTc segment abnormalities.
  • It is crucial to monitor the patients for cardiac manifestations that will help to identify the complications and initiate prompt treatment.


Deepalakshmi Kaliyaperumal

Kumar Bhargavi

Karthikeyan Ramaraju

Krishna S Nair

Sudha Ramalingam

Murali Alagesan to:


Source of support: Nil

Conflict of interest: NoneGo to:


1. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497–506. doi: 10.1016/S0140-6736(20)30183-5. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

2. Inciardi RM, Lupi L, Zaccone G, Italia L, Raffo M, Tomasoni D, et al. Cardiac involvement in a patient with coronavirus disease 2019 (COVID-19). JAMA Cardiol. 2020;5(7):819–824. doi: 10.1001/jamacardio.2020.1096. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

3. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel corona virus infected pneumonia in Wuhan, China. JAMA. 2020;323(11):1061–1069. doi: 10.1001/jama.2020.1585. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

4. Zheng YY, Ma YT, Zhang JY, Xie X. COVID-19 and the cardiovascular system. Nat Rev Cardiol. 2020;17(5):259–260. doi: 10.1038/s41569-020-0360-5. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

5. Lin L, Xu YJ, He DP, Han Y, Tang GH, Yang ZM, et al. A retrospective study on clinical features of and treatment methods for 77 severe cases of SARS. Am J Chin Med. 2003;31(6):821–839. doi: 10.1142/S0192415X03001521. [PubMed] [CrossRef] [Google Scholar]

6. Lakkireddy DR, Chung MK, Gopinathannair R, Patton KK, Gluckman TJ, Turagam M, et al. Guidance for cardiac electrophysiology during the COVID-19 pandemic from the Heart Rhythm Society COVID-19 Task Force; Electrophysiology Section of the American College of Cardiology; and the Electrocardiography and Arrhythmias Committee of the Council on Clinical Cardiology, American Heart Association. Circulation. 2020;141(21):e823. doi: 10.1161/CIRCULATIONAHA. 120.047063. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

7. AjitMagadum A, Raj Kishore R. Cardiovascular manifestations of COVID-19 infection. Cells. 2020;9(11):2508. doi: 10.3390/cells9112508. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

8. Puljiz I, Kuzman I, Markotic A, Turcinov D, Matić M, Makek N. Electrocardiographic changes in patients with haemorrhagic fever with renal syndrome. Scand J Infect Dis. 2005;37(8):594–598. doi: 10.1080/00365540510036606. [PubMed] [CrossRef] [Google Scholar]

9. Makela S, Kokkonen L, Ala-Houhala I, Groundstroem K, Harmoinen A, Huhtala H, et al. More than half of the patients with acute Puumala hantavirus infection have abnormal cardiac findings. Scand J Infect Dis. 2009;41(1):57–62. doi: 10.1080/00365540802502629. [PubMed] [CrossRef] [Google Scholar]

10. Sacramento E, Lopes AA, Costa E, Passos OL, Costa YA, Matos ED. Electrocardiographic alterations in patients hospitalized with leptospirosis in the Brazilian city of Salvador. Arq Bras Cardiol. 2002;78(3):267–270. doi: 10.1590/s0066-782×2002000300002. [PubMed] [CrossRef] [Google Scholar]

11. Watt G, Kantipong P, Jirajarus K. Acute scrub typhus in northern Thailand: EKG changes. Southeast Asian J Trop Med Public Health. 2002;33(2):312–313. 12236430 [PubMed] [Google Scholar]

12. Lumio JT, Groundstroem KW, Melnick OB, Huhtala H, Rakhmanova AG. Electrocardiographic abnormalities in patients with diphtheria: a prospective study. Am J Med. 2004;116(2):78–83. doi: 10.1016/j.amjmed.2003.08.031. [PubMed] [CrossRef] [Google Scholar]

13. Puljiz I, Beus A, Kuzman I, Seiwerth S. Electrocardiographic changes and myocarditis in trichinellosis: a retrospective study of 154 patients. Ann Trop Med Parasitol. 2005;99(4):403–411. doi: 10.1179/136485905X36307. [PubMed] [CrossRef] [Google Scholar]

14. Goldbaum M, Ajimura FY, Litvoc J, de Carvalho SA, Eluf-Neto J. American trypanosomiasis and electrocardiographic alterations among industrial workers in São Paulo, Brazil. Rev Inst Med Trop Sao Paulo. 2004;46(6):299–302. doi: 10.1590/s0036-46652004000600001. [PubMed] [CrossRef] [Google Scholar]

15. Shivanthan, MC, Navinan MR, Constantine GR, Rajapakse S. Cardiac involvement in dengue infection. J Infec Dev Ctries. 2015;9(4):338–346. 25881521 [PubMed] [Google Scholar]

16. Akritidis N, Mastora M, Baxevanos G, Dimos G, Pappas G. Electrocardiographic abnormalities in patients with novel H1N1 influenza virus infection. [Epub 2010 Sep 24.];Am J Cardiol. 2010 Nov 15;106(10):1517–1519. doi: 10.1016/j.amjcard.2010.06.078. 21059446 [PubMed] [CrossRef] [Google Scholar]

17. Shi Y, Wang Y, Shao C, Huang J, Gan J, Huang X, et al. COVID-19 infection: the perspectives on immune responses. Cell Death Differ. 2020;27(5):1451–1454. doi: 10.1038/s41418-020-0530-3. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

18. Clerkin KJ, Fried JA, Raikhelkar J, Sayer G, Griffin JM, Masoumi A, et al. COVID-19 and cardiovascular disease. Circulation. 2020;141(20):1648–1655. doi: 10.1161/CIRCULATIONAHA.120.046941. [PubMed] [CrossRef] [Google Scholar]

19. Xiong TY, Redwood S, Prendergast B, Chen M. Coronaviruses and the cardiovascular system: acute and long-term implications. Eur Heart J. 2020;41(19):1798–1800. doi: 10.1093/eurheartj/ehaa231. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

20. Schoenhagen P, Tuzcu EM, Ellis SG. Plaque vulnerability, plaque rupture, and acute coronary syndromes: (multi)-focal manifestation of a systemic disease process. Circulation. 2002;106(7):760–762. doi: 10.1161/01.cir.0000025708.36290.05. [PubMed] [CrossRef] [Google Scholar]

21. Bergamaschi L, D’Angelo EC, Paolisso P, Toniolo S, Fabrizio M, Angeli F, et al. The value of ECG changes in risk stratification of COVID-19 patients. Ann Noninvasive Electrocardiol. 2021;26(3):e12815. doi: 10.1111/anec.12815. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

22. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA. 2020;323(11):1061. doi: 10.1001/jama.2020.1585. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

23. Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, et al. Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan, China. JAMA Cardiol. 2020;5(7):802–810. doi: 10.1001/jamacardio.2020.0950. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

24. Long B, Brady WJ, Bridwell RE, Ramzy M, Montrief T, Singh M, et al. Electrocardiographic manifestations of COVID-19. Am J Emerg Med. 2021;41:96–103. doi: 10.1016/j.ajem.2020.12.060. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

25. Bhatla A, Mayer MM, Adusumalli S, Hyman MC, Oh E, Tierney A, et al. COVID-19 and cardiac arrhythmias. Heart Rhythm. 2020;17(9):1439–1444. doi: 10.1016/j.hrthm.2020.06.016. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

26. Stefanini GG, Montorfano M, Trabattoni D, Andreini D, Ferrante G, Ancona M, et al. ST-elevation myocardial infarction in patients with COVID-19: clinical and angiographic outcomes. Circulation. 2020;141(25):2113–2116. doi: 10.1161/CIRCULATIONAHA.120.047525. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

27. Roden DM, Harrington RA, Poppas A, Russo AM. Considerations for drug interactions on QTc in exploratory COVID-19 (coronavirus disease 2019) treatment. Circulation. 2020;141(24):e906–e907. doi: 10.1161/CIRCULATIONAHA.120.047521. [PubMed] [CrossRef] [Google Scholar]

28. Ramireddy A, Chugh H, Reinier K, Ebinger J, Park E, Thompson M, et al. Experience with hydroxychloroquine and azithromycin in the COVID-19 pandemic: implications for QT interval monitoring. J Am Heart Assoc. 2020;9(12):e017144. doi: 10.1161/JAHA.120.017144. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

29. Kho J, Ioannou A, Van den Abbeele K, Mandal AKJ, Missouris CG. Pulmonary embolism in COVID-19: clinical characteristics and cardiac implications. Am J Emerg Med. 2020;38(10):2142–2146. doi: 10.1016/j.ajem.2020.07.054. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

30. Yang D, Li J, Gao P, Chen T, Cheng Z, Cheng K, et al. The prognostic significance of electrocardiography findings in patients with coronavirus disease 2019: a retrospective study. Clin Cardiol. 2021;44(7):963–970. doi: 10.1002/clc.23628. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

31. Wang Y, Chen L, Wang J, He X, Huang F, Chen J, et al. Electrocardiogram analysis of patients with different types of COVID-19. Ann Noninvasive Electrocardiol. 2020;25(6):e12806. doi: 10.1111/anec.12806. [PMC free article] [PubMed] [CrossRef] [Google Scholar]

Articles from Indian Journal of Critical Care Medicine : Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine are provided here courtesy of Indian Society of Critical Care Medicine

Heart-disease risk soars after COVID — even with a mild case

Authors: Saima May Sidik 10 February 2022


Massive study shows a long-term, substantial rise in risk of cardiovascular disease, including heart attack and stroke, after a SARS-CoV-2 infection.

Even a mild case of COVID-19 can increase a person’s risk of cardiovascular problems for at least a year after diagnosis, a new study1 shows. Researchers found that rates of many conditions, such as heart failure and stroke, were substantially higher in people who had recovered from COVID-19 than in similar people who hadn’t had the disease.

What’s more, the risk was elevated even for those who were under 65 years of age and lacked risk factors, such as obesity or diabetes.

“It doesn’t matter if you are young or old, it doesn’t matter if you smoked, or you didn’t,” says study co-author Ziyad Al-Aly at Washington University in St. Louis, Missouri, and the chief of research and development for the Veterans Affairs (VA) St. Louis Health Care System. “The risk was there.”

Al-Aly and his colleagues based their research on an extensive health-record database curated by the United States Department of Veterans Affairs. The researchers compared more than 150,000 veterans who survived for at least 30 days after contracting COVID-19 with two groups of uninfected people: a group of more than five million people who used the VA medical system during the pandemic, and a similarly sized group that used the system in 2017, before SARS-CoV-2 was circulating.

Troubled hearts

People who had recovered from COVID-19 showed stark increases in 20 cardiovascular problems over the year after infection. For example, they were 52% more likely to have had a stroke than the contemporary control group, meaning that, out of every 1,000 people studied, there were around 4 more people in the COVID-19 group than in the control group who experienced stroke.

The risk of heart failure increased by 72%, or around 12 more people in the COVID-19 group per 1,000 studied. Hospitalization increased the likelihood of future cardiovascular complications, but even people who avoided hospitalization were at higher risk for many conditions.

“I am actually surprised by these findings that cardiovascular complications of COVID can last so long,” Hossein Ardehali, a cardiologist at Northwestern University in Chicago, Illinois, wrote in an e-mail to Nature. Because severe disease increased the risk of complications much more than mild disease, Ardehali wrote, “it is important that those who are not vaccinated get their vaccine immediately”.COVID’s cardiac connection

Ardehali cautions that the study’s observational nature comes with some limitations. For example, people in the contemporary control group weren’t tested for COVID-19, so it’s possible that some of them actually had mild infections. And because the authors considered only VA patients — a group that’s predominantly white and male — their results might not translate to all populations.

Ardehali and Al-Aly agree that health-care providers around the world should be prepared to address an increase in cardiovascular conditions. But with high COVID-19 case counts still straining medical resources, Al-Aly worries that health authorities will delay preparing for the pandemic’s aftermath for too long. “We collectively dropped the ball on COVID,” he said. “And I feel we’re about to drop the ball on long COVID.”



  1. Xie, Y., Xu, E., Bowe, B. & Al-Aly, Z. Nature Med. (2022).PubMed Article Google Scholar 

Long-term cardiovascular outcomes of COVID-19


The cardiovascular complications of acute coronavirus disease 2019 (COVID-19) are well described, but the post-acute cardiovascular manifestations of COVID-19 have not yet been comprehensively characterized. Here we used national healthcare databases from the US Department of Veterans Affairs to build a cohort of 153,760 individuals with COVID-19, as well as two sets of control cohorts with 5,637,647 (contemporary controls) and 5,859,411 (historical controls) individuals, to estimate risks and 1-year burdens of a set of pre-specified incident cardiovascular outcomes. We show that, beyond the first 30 d after infection, individuals with COVID-19 are at increased risk of incident cardiovascular disease spanning several categories, including cerebrovascular disorders, dysrhythmias, ischemic and non-ischemic heart disease, pericarditis, myocarditis, heart failure and thromboembolic disease. These risks and burdens were evident even among individuals who were not hospitalized during the acute phase of the infection and increased in a graded fashion according to the care setting during the acute phase (non-hospitalized, hospitalized and admitted to intensive care). Our results provide evidence that the risk and 1-year burden of cardiovascular disease in survivors of acute COVID-19 are substantial. Care pathways of those surviving the acute episode of COVID-19 should include attention to cardiovascular health and disease.


Post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—the virus that causes coronavirus disease 2019 (COVID-19)—can involve the pulmonary and several extrapulmonary organs, including the cardiovascular system1. A few studies have investigated cardiovascular outcomes in the post-acute phase of the COVID-19; however, most were limited to hospitalized individuals (who represent the minority of people with COVID-19), and all had a short duration of follow-up and a narrow selection of cardiovascular outcomes2,3,4,5. A comprehensive assessment of post-acute COVID-19 sequelae of the cardiovascular system at 12 months is not yet available, and studies of post-acute COVID-19 sequelae across the spectrum of care settings of the acute infection (non-hospitalized, hospitalized and admitted to intensive care) are also lacking. Addressing this knowledge gap will inform post-acute COVID-19 care strategies.

In this study, we used the US Department of Veterans Affairs national healthcare databases to build a cohort of 153,760 US veterans who survived the first 30 d of COVID-19 and two control groups: a contemporary cohort consisting of 5,637,647 users of the US Veterans Health Administration (VHA) system with no evidence of SARS-CoV-2 infection and a historical cohort (pre-dating the COVID-19 pandemic) consisting of 5,859,411 non-COVID-19-infected VHA users during 2017. These cohorts were followed longitudinally to estimate the risks and 12-month burdens of pre-specified incident cardiovascular outcomes in the overall cohort and according to care setting of the acute infection (non-hospitalized, hospitalized and admitted to intensive care).


There were 153,760, 5,637,647 and 5,859,411 participants in the COVID-19, contemporary control and historical control groups, respectively (Fig. 1). Median follow-up time in the COVID-19, contemporary control and historical control groups was 347 (interquartile range, 317–440), 348 (318–441) and 347 (317–440) d, respectively. The COVID-19, contemporary control and historical control groups had 159,366, 5,854,288 and 6,082,182 person-years of follow-up, respectively, altogether corresponding to 12,095,836 person-years of follow-up. The demographic and health characteristics of the COVID-19, contemporary control and historical control groups before and after weighting are presented in Supplementary Tables 1 and 2, respectively.

figure 1
Fig. 1: Flowchart of cohort construction.

Incident cardiovascular diseases in COVID-19 versus contemporary control

Assessment of covariate balance after application of inverse probability weighting suggested that covariates were well balanced (Extended Data Fig. 1a).

We estimated the risks of a set of pre-specified cardiovascular outcomes in COVID-19 versus contemporary control; we also estimated the adjusted excess burden of cardiovascular outcomes due to COVID-19 per 1,000 persons at 12 months on the basis of the difference between the estimated incidence rate in individuals with COVID-19 and the contemporary control group. Risks and burdens of individual cardiovascular outcomes are provided in Fig. 2 and Supplementary Table 3 and are discussed below. Risks and burdens of the composite endpoints are provided in Fig. 3 and Supplementary Table 3.

figure 2
Fig. 2: Risks and 12-month burdens of incident post-acute COVID-19 cardiovascular outcomes compared with the contemporary control cohort.
figure 3
Fig. 3: Risks and 12-month burdens of incident post-acute COVID-19 composite cardiovascular outcomes compared with the contemporary control cohort.

Cerebrovascular disorders

People who survived the first 30 d of COVID-19 exhibited increased risk of stroke (hazard ratio (HR) = 1.52 (1.43, 1.62); burden 4.03 (3.32, 4.79) per 10,00 persons at 12 months; for all HRs and burdens, parenthetical ranges refer to 95% confidence intervals (CIs)) and transient ischemic attacks (TIA) (HR = 1.49 (1.37, 1.62); burden 1.84 (1.38, 2.34)). The risks and burdens of a composite of these cerebrovascular outcomes were 1.53 (1.45, 1.61) and 5.48 (4.65, 6.35).


There were increased risks of atrial fibrillation (HR = 1.71 (1.64, 1.79); burden 10.74 (9.61, 11.91)), sinus tachycardia (HR = 1.84 (1.74, 1.95); burden 5.78 (5.07, 6.53)), sinus bradycardia (HR = 1.53 (1.45, 1.62); burden 4.62 (3.90, 5.38)), ventricular arrhythmias (HR = 1.84 (1.72, 1.98); burden 4.18 (3.56, 4.85)); and atrial flutter (HR = 1.80 (1.66, 1.96); burden 3.10 (2.55, 3.69)). The risks and burdens of a composite of these dysrhythmia outcomes were 1.69 (1.64, 1.75), and 19.86 (18.31, 21.46).

Inflammatory disease of the heart or pericardium

Inflammatory disease of the heart or pericardium included pericarditis (HR = 1.85 (1.61, 2.13)); burden 0.98 (0.70, 1.30) and myocarditis (HR = 5.38 (3.80, 7.59); burden 0.31 (0.20, 0.46)). The risks and burdens of a composite of these inflammatory diseases of the heart or pericardium were 2.02 (1.77, 2.30) and 1.23 (0.93, 1.57).

Ischemic heart disease

Ischemic heart disease included acute coronary disease (HR = 1.72 (1.56, 1.90); burden 5.35 (4.13, 6.70)), myocardial infarction (HR = 1.63 (1.51, 1.75); burden 2.91 (2.38, 3.49)), ischemic cardiomyopathy (HR = 1.75 (1.44, 2.13); burden 2.34 (1.37, 3.51)) and angina (HR = 1.52 (1.42, 1.64); burden 2.50 (2.00, 3.03)). The risks and burdens of a composite of these ischemic heart disease outcomes were 1.66 (1.52, 1.80) and 7.28 (5.80, 8.88).

Other cardiovascular disorders

Other cardiovascular disorders included heart failure (HR = 1.72 (1.65, 1.80); burden 11.61 (10.47, 12.78)), non-ischemic cardiomyopathy (HR = 1.62 (1.52, 1.73); burden 3.56 (2.97, 4.20)), cardiac arrest (HR = 2.45 (2.08, 2.89); burden 0.71 (0.53, 0.93)) and cardiogenic shock (HR = 2.43 (1.86, 3.16); burden 0.51 (0.31, 0.77)). The risks and burdens of a composite of these other cardiovascular disorders were 1.72 (1.65, 1.79) and 12.72 (11.54, 13.96).

Thromboembolic disorders

Thromboembolic disorders included pulmonary embolism (HR = 2.93 (2.73, 3.15); burden 5.47 (4.90, 6.08)); deep vein thrombosis (HR = 2.09 (1.94, 2.24); burden 4.18 (3.62, 4.79)) and superficial vein thrombosis (HR = 1.95 (1.80, 2.12); burden 2.61 (2.20, 3.07)). The risks and burdens of a composite of these thromboembolic disorders were 2.39 (2.27, 2.51) and 9.88 (9.05, 10.74).

Additional composite endpoints

We then examined the risks and burdens of two composite endpoints, including major adverse cardiovascular event (MACE)—a composite of myocardial infarction, stroke and all-cause mortality—and any cardiovascular outcome (defined as the occurrence of any incident pre-specified cardiovascular outcome included in this study). Compared to the contemporary control group, there were increased risks and burdens of MACE (HR = 1.55 (1.50, 1.60); burden 23.48 (21.54, 25.48)) and any cardiovascular outcome (HR = 1.63 (1.59, 1.68); burden 45.29 (42.22, 48.45)).

Subgroup analyses

We examined the risks of incident composite cardiovascular outcomes in subgroups based on age, race, sex, obesity, smoking, hypertension, diabetes, chronic kidney disease, hyperlipidemia and cardiovascular disease. The risks of incident composite cardiovascular outcomes were evident in all subgroups (Fig. 4 and Supplementary Table 4),

figure 4
Fig. 4: Subgroup analyses of the risks of incident post-acute COVID-19 composite cardiovascular outcomes compared with the contemporary control cohort.

We examined the risks and burdens of the pre-specified outcomes in a cohort of people without any cardiovascular disease at baseline; the results were consistent with those shown in the primary analyses (Extended Data Figs. 2 and 3 and Supplementary Table 5).

Incident cardiovascular diseases in COVID-19 versus contemporary control by care setting of the acute infection

We further examined the risks and burdens of cardiovascular diseases in mutually exclusive groups by the care setting of the acute infection (that is, whether people were non-hospitalized (n = 131,612), hospitalized (n = 16,760) or admitted to intensive care (n = 5,388) during the acute phase of COVID-19); demographic and health characteristics of these groups before weighting can be found in Supplementary Table 6 and after weighting in Supplementary Table 7. Assessment of covariate balance after application of weights suggested that covariates were well balanced (Extended Data Fig. 1b). Compared to the contemporary control group, the risks and 12-month burdens of the pre-specified cardiovascular outcomes increased according to the severity of the acute infection (Fig. 5 and Supplementary Table 8); results for the composite outcomes are shown in Fig. 6 and Supplementary Table 8.

figure 5
Fig. 5: Risks and 12-month burdens of incident post-acute COVID-19 cardiovascular outcomes compared with the contemporary control cohort by care setting of the acute infection.
figure 6
Fig. 6: Risks and 12-month burdens of incident post-acute COVID-19 composite cardiovascular outcomes compared with the contemporary control cohort by care setting of the acute infection.

Incident cardiovascular diseases in COVID-19 versus historical control

We then examined the associations between COVID-19 and the pre-specified outcomes in analyses considering a historical control group as the referent category; the characteristics of the exposure groups were balanced after weighting (Extended Data Fig. 1c and Supplementary Table 2). The results were consistent with analyses using the contemporary control as the referent category and showed increased risks and associated burdens of the pre-specified outcomes in comparisons of COVID-19 versus the overall historical control group (Extended Data Figs. 4 and 5 and Supplementary Table 9). Using the historical control as the referent category, we examined the risks in subgroups and separately in people without any prior cardiovascular disease; the results were consistent with those undertaken versus the contemporary control (Extended Data Figs. 68 and Supplementary Tables 10 and 11). Associations between COVID-19 and our pre-specified outcomes based on care setting of the acute infection were also assessed using the historical control group as the referent category; demographic and clinical characteristics are presented before weighting in Supplementary Table 12 and after weighting in Supplementary Table 13. Characteristics of the exposure groups were balanced after weighting (Extended Data Fig. 1d). The risks and 12-month burdens of the pre-specified outcomes by care setting of the acute infection were also consistent with those shown in analyses considering COVID-19 versus contemporary control (Extended Data Figs. 9 and 10 and Supplementary Table 14).

Cardiovascular diseases before and after COVID-19

To better understand the change in the relative rates of incident cardiovascular outcomes before and after the COVID-19 exposure, we developed a difference-in-differences analysis to estimate the adjusted incident rate ratios of the cardiovascular outcomes relative to both the contemporary and historical control groups in the pre-COVID-19 and post-COVID-19 exposure periods. The results showed that the adjusted incident rate ratios of cardiovascular outcomes in the post-COVID-19 exposure period were significantly higher than those in the pre-exposure period (ratios of incident rate ratios for all cardiovascular outcomes were significantly higher than 1) and exhibited a graded increase by severity of the acute phase of the disease (Supplementary Tables 1518).

Sensitivity analyses

We tested robustness of results in several sensitivity analyses involving the outcomes of MACE and any cardiovascular outcome (Supplementary Tables 17 and 18). The sensitivity analyses were performed in comparisons involving COVID-19 versus the contemporary control and COVID-19 versus the historical control and, additionally, COVID-19 by care setting versus both controls. (1) To test whether the inclusion of additional algorithmically selected covariates would challenge the robustness of study results, we selected and used 300 high-dimensional variables (instead of the 100 used in the primary analyses) to construct the inverse probability weighting. (2) We then also tested the results in models specified to include only pre-defined covariates (that is, without inclusion of algorithmically selected covariates) to build the inverse probability weighting. Finally, (3) we changed the analytic approach by using the doubly robust method (instead of the inverse weighting method used in primary analyses) to estimate the magnitude of the associations between COVID-19 exposure and the pre-specified outcomes. All sensitivity analyses yielded results consistent with those produced using the primary approach (Supplementary Tables 19 and 20).

Risk of myocarditis and pericarditis without COVID-19 vaccination

Because some COVID-19 vaccines might be associated with a very rare risk of myocarditis or pericarditis, and to eliminate any putative contribution of potential vaccine exposure to the outcomes of myocarditis and pericarditis in this study, we conducted two analyses. First, we censored cohort participants at the time of receiving the first dose of any COVID-19 vaccine. Second, we adjusted for vaccination as a time-varying covariate. Both analyses were conducted versus both the contemporary and historical control groups. The results suggested that COVID-19 was associated with increased risk of myocarditis and pericarditis in both analyses (Supplementary Tables 2124).

Positive and negative outcome controls

To assess whether our data and analytic approach would reproduce known associations, we examined the association between COVID-19 and the risk of fatigue (known to be a signature sequela of post-acute COVID-19) as a positive outcome control. The results suggested that COVID-19 was associated with a higher risk of fatigue (Supplementary Table 25).

We then examined the association between COVID-19 and a battery of seven negative-outcome controls where no prior knowledge suggests that an association is expected. The results yielded no significant association between COVID-19 and any of the negative-outcome controls, which were consistent with a priori expectations (Supplementary Table 25).

Negative-exposure controls

To further examine the robustness of our approach, we developed and tested a pair of negative-exposure controls. We hypothesized that receipt of influenza vaccination in odd-numbered and even-numbered calendar days between 1 March 2020 and 15 January 2021 would be associated with similar risks of the pre-specified cardiovascular outcomes examined in this analysis. We, therefore, tested the associations between receipt of influenza vaccine in even-numbered (n = 571,291) versus odd-numbered (n = 605,453) calendar days and the pre-specified cardiovascular outcomes. We used the same data sources, cohort design, analytical approach (including covariate specification and weighting method) and outcomes. The results suggest that receipt of influenza vaccination in odd-numbered calendar days versus even-numbered calendar days was not significantly associated with any of the pre-specified cardiovascular outcomes (Supplementary Table 26).


In this study involving 153,760 people with COVID-19, 5,637,647 contemporary controls and 5,859,411 historical controls—which, altogether, correspond to 12,095,836 person-years of follow-up—we provide evidence that, beyond the first 30 d of infection, people with COVID-19 exhibited increased risks and 12-month burdens of incident cardiovascular diseases, including cerebrovascular disorders, dysrhythmias, inflammatory heart disease, ischemic heart disease, heart failure, thromboembolic disease and other cardiac disorders. The risks were evident regardless of age, race, sex and other cardiovascular risk factors, including obesity, hypertension, diabetes, chronic kidney disease and hyperlipidemia; they were also evident in people without any cardiovascular disease before exposure to COVID-19, providing evidence that these risks might manifest even in people at low risk of cardiovascular disease. Our analyses of the risks and burdens of cardiovascular outcomes across care settings of the acute infection reveal two key findings: (1) that the risks and associated burdens were evident among those who were not hospitalized during the acute phase of the disease—this group represents the majority of people with COVID-19; and (2) that the risks and associated burdens exhibited a graded increase across the severity spectrum of the acute phase of COVID-19 (from non-hospitalized to hospitalized individuals to those admitted to intensive care). The risks and associated burdens were consistent in analyses considering the contemporary control group and, separately, the historical control group as the referent category. The difference-in-differences analyses, which are designed to further investigate the causality of study findings, show that the increased risks of post-acute COVID-19 cardiovascular outcomes are attributable sequelae to COVID-19 itself. The results were robust to challenge in multiple sensitivity analyses. Application of a positive-outcome control yielded results consistent with established knowledge; and testing of a battery of negative-outcome controls and negative-exposure controls yielded results consistent with a priori expectations. Taken together, our results show that 1-year risks and burdens of cardiovascular diseases among those who survive the acute phase of COVID-19 are substantial and span several cardiovascular disorders. Care strategies of people who survived the acute episode of COVID-19 should include attention to cardiovascular health and disease.

The broader implications of these findings are clear. Cardiovascular complications have been described in the acute phase of COVID-19 (refs. 6,7,8). Our study shows that the risk of incident cardiovascular disease extends well beyond the acute phase of COVID-19. First, the findings emphasize the need for continued optimization of strategies for primary prevention of SARS-CoV-2 infections; that is, the best way to prevent Long COVID and its myriad complications, including the risk of serious cardiovascular sequelae, is to prevent SARS-CoV-2 infection in the first place. Second, given the large and growing number of people with COVID-19 (more than 72 million people in the United States, more than 16 million people in the United Kingdom and more than 355 million people globally), the risks and 12-month burdens of cardiovascular diseases reported here might translate into a large number of potentially affected people around the world. Governments and health systems around the world should be prepared to deal with the likely significant contribution of the COVID-19 pandemic to a rise in the burden of cardiovascular diseases. Because of the chronic nature of these conditions, they will likely have long-lasting consequences for patients and health systems and also have broad implications on economic productivity and life expectancy. Addressing the challenges posed by Long COVID will require a much-needed, but so far lacking, urgent and coordinated long-term global response strategy9,10.

The mechanism or mechanisms that underlie the association between COVID-19 and development of cardiovascular diseases in the post-acute phase of the disease are not entirely clear11,12. Putative mechanisms include lingering damage from direct viral invasion of cardiomyocytes and subsequent cell death, endothelial cell infection and endotheliitis, transcriptional alteration of multiple cell types in heart tissue, complement activation and complement-mediated coagulopathy and microangiopathy, downregulation of ACE2 and dysregulation of the renin–angiotensin–aldosterone system, autonomic dysfunction, elevated levels of pro-inflammatory cytokines and activation of TGF-β signaling through the Smad pathway to induce subsequent fibrosis and scarring of cardiac tissue11,13,14,15,16,17. An aberrant persistent hyperactivated immune response, autoimmunity or persistence of the virus in immune-privileged sites has also been cited as putative explanations of extrapulmonary (including cardiovascular) post-acute sequelae of COVID-19 (refs. 11,13,14,18). Integration of the SARS-CoV-2 genome into DNA of infected human cells, which might then be expressed as chimeric transcripts fusing viral with cellular sequences, has also been hypothesized as a putative mechanism for continued activation of the immune-inflammatory-procoagulant cascade19,20. These mechanistic pathways might explain the range of post-acute COVID-19 cardiovascular sequelae investigated in this report. A deeper understanding of the biologic mechanisms will be needed to inform development of prevention and treatment strategies of the cardiovascular manifestations among people with COVID-19.

Our analyses censoring participants at time of vaccination and controlling for vaccination as a time-varying covariate show that the increased risk of myocarditis and pericarditis reported in this study is significant in people who were not vaccinated and is evident regardless of vaccination status.

This study has several strengths. We used the vast and rich national healthcare databases of the US Department of Veterans Affairs to build a large cohort of people with COVID-19. We designed the study cohort to investigate incident cardiovascular disease in the post-acute phase of the disease. We pre-specified a comprehensive list of cardiovascular outcomes. We examined the associations using two large control groups: a contemporary and a historical control; this approach allowed us to deduce that the associations between COVID-19 and risks of cardiovascular outcomes are not related to the broader temporal changes between the pre-pandemic and the pandemic eras but, rather, are related to exposure to COVID-19 itself. Our modeling approach included specification of 19 pre-defined variables selected based on established knowledge and 100 algorithmically selected variables from high-dimensional data domains, including diagnostic codes, prescription records and laboratory test results. We evaluated the associations across care settings of the acute infection. Our difference-in-differences approach further enhances the causal interpretation of study results. We challenged the robustness of results in multiple sensitivity analyses and successfully tested positive-outcome and negative-outcome controls and negative-exposure controls. We provided estimates of risk on both the ratio scale (HRs) and the absolute scale (burden per 1,000 persons at 12 months); the latter also reflects the contribution of baseline risk and provides an estimate of potential harm that is more easily explainable to the public than risk reported on the ratio scale (for example, HR).

This study has several limitations. The demographic composition of our cohort (majority White and male) might limit the generalizability of study findings. We used the electronic healthcare databases of the US Department of Veterans Affairs to conduct this study, and, although we used validated outcome definitions and took care to adjust the analyses for a large set of pre-defined and algorithmically selected variables, we cannot completely rule out misclassification bias and residual confounding. It is possible that some people might have had COVID-19 but were not tested for it; these people would have been enrolled in the control group and, if present in large numbers, might have biased the results toward the null. Our datasets do not include information on causes of death. Finally, as the pandemic, with all its dynamic features, continues to progress, as the virus continues to mutate and as new variants emerge, as treatment strategies of acute and post-acute COVID-19 evolve and as vaccine uptake improves, it is possible that the epidemiology of cardiovascular manifestations in COVID-19 might also change over time21.

In summary, using a national cohort of people with COVID-19, we show that risk and 12-month burden of incident cardiovascular disease are substantial and span several cardiovascular disease categories (ischemic and non-ischemic heart disease, dysrhythmias and others). The risks and burdens of cardiovascular disease were evident even among those whose acute COVID-19 did not necessitate hospitalization. Care pathways of people who survived the acute episode of COVID-19 should include attention to cardiovascular health and disease.



We used the electronic healthcare databases of the US Department of Veterans Affairs to conduct this study. The VHA, within the US Department of Veterans Affairs, provides healthcare to discharged veterans of the US armed forces. It operates the largest nationally integrated healthcare system in the United States, with 1,255 healthcare facilities (including 170 VA Medical Centers and 1,074 outpatient sites) located across the United States. All veterans who are enrolled with the VHA have access to the comprehensive medical benefits package of the VA (which includes preventative and health maintenance, outpatient care, inpatient hospital care, prescriptions, mental healthcare, home healthcare, primary care, specialty care, geriatric and extended care, medical equipment and prosthetics). The VA electronic healthcare databases are updated daily.


A flowchart of cohort construction is provided in Fig. 1. Of 6,241,346 participants who encountered the VHA in 2019, 162,690 participants who had a positive COVID-19 test between 1 March 2020 and 15 January 2021 were selected into the COVID-19 group. To examine post-acute outcomes, we then selected participants from the COVID-19 group who were alive 30 d after the date of the positive COVID-19 test (n = 153,760). The date of the COVID-19-positive test served as T0 for the COVID-19 group.

A contemporary control group of people with no evidence of SARS-CoV-2 infection was constructed from those who had encountered the VHA in 2019 (n = 6,241,346). Of those who were still alive by 1 March 2020 (n = 5,960,737), 5,806,977 participants were not in the COVID-19 group and were selected into the contemporary control group. To ensure that this contemporary control group had a similar follow-up time as the COVID-19 group, we randomly assigned T0 in the contemporary control group based on the distribution of T0 in the COVID-19 group so that the proportion of people enrolled on a certain date would be the same in both the contemporary and COVID-19 groups. Of 5,658,938 participants alive at the assigned T0, 5,637,647 participants in the contemporary control group were alive 30 d after T0. In the COVID-19 and contemporary control groups, 31 October 2021 was the end of follow-up.

To examine the associations between COVID-19 and cardiovascular outcomes compared to those who did not experience the pandemic, a historical control group was constructed from 6,461,205 participants who used the VHA in 2017. Of the 6,150,594 participants who were alive on 1 March 2018, 6,008,499 participants did not enroll into the COVID-19 group and were further selected into the historical control group. To ensure that this historical control group had a similar follow-up time as the COVID-19 group, we randomly assigned T0 in the historical control group with a similar distribution as T0 minus 2 years (730 d) in the COVID-19 group. Of 5,875,818 historical control participants alive at assigned T0, 5,859,411 were alive 30 d after T0. In the historical control group, end of follow-up was set as 31 October 2019.

Data sources

Electronic health records from the VA Corporate Data Warehouse (CDW) were used in this study. Demographic information was collected from the CDW Patient domain. The CDW Outpatient Encounters domain provided clinical information pertaining to outpatient encounters, whereas the CDW Inpatient Encounters domain provided clinical information during hospitalization. Medication information was obtained from the CDW Outpatient Pharmacy and CDW Bar Code Medication Administration domains. The CDW Laboratory Results domain provided laboratory test information, and the COVID-19 Shared Data Resource provided information on COVID-19. Additionally, the Area Deprivation index (ADI), which is a composite measure of income, education, employment and housing, was used as a summary measure of contextual disadvantage at participants’ residential locations22.

Pre-specified outcomes

The pre-specified outcomes were selected based on our previous work on the systematic characterization of Long COVID1,23. Incident cardiovascular outcomes in the post-acute phase of COVID-19 were assessed in the follow-up period between 30 d after T0 until the end of follow-up in those without history of the outcome in the year before T0. Each cardiovascular outcome was defined based on validated diagnostic codes. We also aggregated individual outcomes in a related category of composite outcome (for example, stroke and TIA were aggregated to cerebrovascular disease). We also specified two additional composite outcomes: (1) MACE was a composite outcome of all-cause mortality, myocardial infarction and stroke; and (2) the composite of any cardiovascular outcome was defined as the first incident occurrence of any of the cardiovascular outcomes investigated in this study.


To adjust for the difference in baseline characteristics between groups, we considered both pre-defined and algorithmically selected high-dimensional covariates assessed within 1 year before T0. Pre-defined variables were selected based on prior knowledge1,7,24,25. The pre-defined covariates included age, race (White, Black and Other), sex, ADI, body mass index, smoking status (current, former and never) and healthcare use parameters, including the use number of outpatient and inpatient encounters and use of long-term care. We additionally specified several comorbidities as pre-defined variables, including cancer, chronic kidney disease, chronic lung disease, dementia, diabetes, dysautonomia, hyperlipidemia and hypertension. Additionally, we adjusted for estimated glomerular filtration rate and systolic and diastolic blood pressure. Missing values were accounted for by conditional mean imputation based on value within the group26. Continuous variables were transformed into restricted cubic spline functions to account for potential non-linear relationships.

In addition to pre-defined covariates, we further algorithmically selected additional potential confounders from data domains, including diagnoses, medications and laboratory tests27. To accomplish this, we gathered all patient encounter, prescription and laboratory data and classified the information into 540 diagnostic categories, 543 medication classes and 62 laboratory test abnormalities. For the diagnoses, medications and laboratory abnormalities that occurred in at least 100 participants within each group, univariate relative risk between the variable and exposure was calculated, and the top 100 variables with the strongest relative risk were selected28. The process of algorithmically selecting the high-dimensional covariates was independently conducted for each outcome-specific cohort in each comparison (for example, the COVID-19 versus contemporary control analyses to examine incident heart failure and the COVID-19 versus historical control analyses to examine incident heart failure).

All pre-defined and algorithmically selected covariates were used in the models.

Statistical analyses

Baseline characteristics of the COVID-19 and contemporary and historical control groups, along with standardized mean difference between groups, were described.

We then estimated the risks, burdens and excess burdens of incident cardiovascular outcomes for COVID-19 compared to the contemporary control group and, separately, compared to the historical control group, after adjusting for differences in baseline characteristics through inverse probability weighting. To estimate the risk of each incident cardiovascular outcome, we built a subcohort of participants without a history of the outcome being examined (that is, the risk of incident heart failure was estimated within a subcohort of participants without history of heart failure in the year before enrollment). In each subcohort, a propensity score for each individual was estimated as the probability of belonging to the VHA users group in 2019 (target population) based on both pre-defined and algorithmically selected high-dimensional variables. This propensity score was then used to calculate the inverse probability weight as the probability of belonging in the target population divided by 1 − the probability of being in the target population. Covariate balance after application of weights was assessed by standardized mean differences.

HRs of incident cardiovascular outcomes between the COVID-19 and contemporary cohorts and the COVID-19 and historical cohorts were estimated from cause-specific hazard models where death was considered as a competing risk, and the inverse probability weights were applied. Burden per 1,000 participants at 12 months of follow-up and the excess burden based on the differences between COVID-19 and control groups were estimated.

We conducted analyses in subgroups by age, race, sex, obesity, smoking, hypertension, diabetes, chronic kidney disease, hyperlipidemia and cardiovascular disease. And, separately, we undertook analyses in a cohort without history of any cardiovascular outcomes before cohort enrollment.

We then developed causal difference-in-differences analyses to estimate the adjusted incident rate ratios of all cardiovascular outcomes in the pre-COVID-19 and post-COVID-19 exposure period relative to both contemporary and historical controls29,30,31,32. To enhance the interpretability of difference-in-difference analyses, the pre-exposure period was defined as with same follow-up time as the post-exposure period, and the incident rate ratio for the pre-exposure period was examined within those without history of the outcome within 1 year before the period. Incident rate ratios for all groups in the pre-exposure and post- exposure periods were weighted toward the common target population (VHA users in 2019) based on pre-exposure characteristics. The adjusted incident rate ratios in the pre-exposure and post-exposure periods were then compared. Difference-in-differences analyses were also conducted in mutually exclusive groups according to care setting of the acute phase of the disease. We also evaluated the associations between COVID-19 and risks of post-acute cardiovascular sequelae in mutually exclusive groups according to care setting of the acute phase of the disease (that is, whether people were non-hospitalized, hospitalized or admitted into the intensive care unit during the first 30 d of infection). Inverse probability weights were estimated for each care setting group using the approach outlined in the previous paragraph. Cause-specific hazard models with inverse probability weighting were then applied, and HRs, burdens and excess burdens were reported.

We conducted multiple sensitivity analyses to test the robustness of our study results. (1) To capture additional potential confounders, we expanded our inclusion of high-dimensional variables from the top 100 to the top 300 when constructing the inverse probability weight. (2) We then modified our adjustment strategy by using only pre-defined variables when constructing the inverse probability weight (not including the 100 high-dimensional covariates used in the primary analyses). Finally, (3) we alternatively applied a doubly robust approach, where both covariates and the inverse probability weights were applied to the survival models, to estimate the associations33.

COVID-19 is associated with an increased risk of fatigue in the post-acute phase of the disease, which is generally considered as a signature post-acute sequela34. To test whether our approach would reproduce known associations, we, therefore, examined the association between COVID-19 and fatigue as a positive outcome control. Reproducing this known association (using our data, cohort design and analytic strategy) would provide some measure of assurance that our approach yields result consistent with a priori expectations.

We also subjected our approach to the application of a battery of negative-outcome controls where no prior knowledge supports the existence of a causal association between the exposure and the risks of negative-outcome controls35. The negative-outcome controls included hypertrichosis, melanoma in situ, sickle cell trait, perforation of the tympanic membrane, malignant neoplasm of the tongue, B cell lymphoma and Hodgkin’s lymphoma. We also developed and tested a pair of negative-exposure controls (defined as exposure to influenza vaccine in odd-numbered or even-numbered calendar days between 1 March 2020 and 15 January 2021). Our pre-test expectation was that there would be no differences in risk of any of the pre-specified cardiovascular outcomes examined in this analysis between those who received influenza vaccine in odd-numbered versus even-numbered calendar days. The successful application of negative controls might reduce concern about the presence of spurious biases related to cohort building, study design, covariate selection, analytic approaches, outcome ascertainment, residual confounding and other sources of latent biases.

Estimation of variance when weightings were applied was accomplished by using robust sandwich variance estimators. In all analyses, a 95% confidence interval that excluded unity was considered evidence of statistical significance. This study was approved by the institutional review board of the VA St. Louis Health Care System (protocol number 1606333), which granted a waiver of informed consent. Analyses were conducted using SAS Enterprise Guide version 8.2 (SAS Institute), and results were visualized using R version 4.04.

Ethical approval

This research project was reviewed and approved by the institutional review board of the VA St. Louis Health Care System (protocol number 1606333).

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are available from the US Department of Veterans Affairs. VA data are made freely available to researchers behind the VA firewall with an approved VA study protocol. For more information, visit or contact the VA Information Resource Center at

Code availability

SAS codes are available at and


  1. 1.Al-Aly, Z., Xie, Y. & Bowe, B. High-dimensional characterization of post-acute sequelae of COVID-19. Nature 594, 259–264 (2021).CAS Article Google Scholar 
  2. 2.Ayoubkhani, D. et al. Post-COVID syndrome in individuals admitted to hospital with COVID-19: retrospective cohort study. BMJ 372, n693 (2021).Article Google Scholar 
  3. 3.Huang, C. et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet 397, 220−232 (2021).
  4. 4.Carfi, A., Bernabei, R., Landi, F. & the Gemelli Against COVID-19 Post-Acute Care Study Group. Persistent symptoms in patients after acute COVID-19. JAMA 324, 603–605 (2020).
  5. 5.Daugherty, S. E. et al. Risk of clinical sequelae after the acute phase of SARS-CoV-2 infection: retrospective cohort study. BMJ 373, n1098 (2021).Article Google Scholar 
  6. 6.Katsoularis, I., Fonseca-Rodriguez, O., Farrington, P., Lindmark, K. & Fors Connolly, A. M. Risk of acute myocardial infarction and ischaemic stroke following COVID-19 in Sweden: a self-controlled case series and matched cohort study. Lancet 398, 599–607 (2021).CAS Article Google Scholar 
  7. 7.Xie, Y., Bowe, B., Maddukuri, G. & Al-Aly, Z. Comparative evaluation of clinical manifestations and risk of death in patients admitted to hospital with COVID-19 and seasonal influenza: cohort study. BMJ 371, m4677 (2021).Google Scholar 
  8. 8.Gupta, A. et al. Extrapulmonary manifestations of COVID-19. Nat. Med. 26, 1017–1032 (2020).CAS Article Google Scholar 
  9. 9.Alwan, N. A. The road to addressing long COVID. Science 373, 491–493 (2021).CAS Article Google Scholar 
  10. 10.Briggs, A. & Vassall, A. Count the cost of disability caused by COVID-19. Nature 593, 502–505 (2021).CAS Article Google Scholar 
  11. 11.Farshidfar, F., Koleini, N. & Ardehali, H. Cardiovascular complications of COVID-19. JCI Insight 6, e148980 (2021).
  12. 12.Nalbandian, A. et al. Post-acute COVID-19 syndrome. Nat. Med. 27, 601–615 (2021).CAS Article Google Scholar 
  13. 13.Nishiga, M., Wang, D. W., Han, Y., Lewis, D. B. & Wu, J. C. COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Nat. Rev. Cardiol. 17, 543–558 (2020).CAS Article Google Scholar 
  14. 14.Chung, M. K. et al. COVID-19 and cardiovascular disease. Circ. Res. 128, 1214–1236 (2021).CAS Article Google Scholar 
  15. 15.Delorey, T. M. et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature 595, 107–113 (2021).CAS Article Google Scholar 
  16. 16.Song, W.-C. & FitzGerald, G. A. COVID-19, microangiopathy, hemostatic activation, and complement. J. Clin. Invest. 130, 3950–3953 (2020).CAS PubMed PubMed Central Google Scholar 
  17. 17.Varga, Z. et al. Endothelial cell infection and endotheliitis in COVID-19. Lancet 395, 1417–1418 (2020).CAS Article Google Scholar 
  18. 18.Long-term Immunological Health Consequences of COVID-19 (British Society for Immunology, 2020);
  19. 19.Di Toro, A. et al. Long COVID: long-term effects? Eur. Heart J. Suppl. 23, E1–E5 (2021).Article Google Scholar 
  20. 20.Zhang, L. et al. Reverse-transcribed SARS-CoV-2 RNA can integrate into the genome of cultured human cells and can be expressed in patient-derived tissues. Proc. Natl Acad. Sci. USA 118, e2105968118 (2021).
  21. 21.Cai, M., Bowe, B., Xie, Y. & Al-Aly, Z. Temporal trends of COVID-19 mortality and hospitalisation rates: an observational cohort study from the US Department of Veterans Affairs. BMJ Open 11, e047369 (2021).Article Google Scholar 
  22. 22.Kind, A. J. H. & Buckingham, W. R. Making neighborhood-disadvantage metrics accessible—The Neighborhood Atlas. N. Engl. J. Med. 378, 2456–2458 (2018).Article Google Scholar 
  23. 23.Xie, Y., Bowe, B. & Al-Aly, Z. Burdens of post-acute sequelae of COVID-19 by severity of acute infection, demographics and health status. Nat. Commun. 12, 6571 (2021).CAS Article Google Scholar 
  24. 24.Bowe, B. et al. Acute kidney injury in a national cohort of hospitalized US veterans with COVID-19. Clin. J. Am. Soc. Nephrol. 16, 14–25 (2020).Article Google Scholar 
  25. 25.Bowe, B., Xie, Y., Xu, E. & Al-Aly, Z. Kidney outcomes in long COVID. J. Am. Soc. Nephrol. 32, 2851–2862 (2021).CAS Article Google Scholar 
  26. 26.Harrell, F. E. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Springer, 2015).
  27. 27.Schneeweiss, S. et al. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology 20, 512–522 (2009).Article Google Scholar 
  28. 28.Austin, P. C. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav. Res. 46, 399–424 (2011).Article Google Scholar 
  29. 29.Wing, C., Simon, K. & Bello-Gomez, R. A. Designing difference in difference studies: best practices for public health policy research. Annu. Rev. Public Health 39, 453–469 (2018).Article Google Scholar 
  30. 30.Lechner, M. The estimation of causal effects by difference-in-difference methods. Found. Trends Econom. 4, 165–224 (2011).Article Google Scholar 
  31. 31.Imbens, G. W. & Angrist, J. D. Identification and estimation of local average treatment effects. Econometrica 62, 467–475 (1994).Article Google Scholar 
  32. 32.Dimick, J. B. & Ryan, A. M. Methods for evaluating changes in health care policy: the difference-in-differences approach. JAMA 312, 2401–2402 (2014).CAS Article Google Scholar 
  33. 33.Funk, M. J. et al. Doubly robust estimation of causal effects. Am. J. Epidemiol. 173, 761–767 (2011).Article Google Scholar 
  34. 34.Davis, H. E. et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine 38, 101019 (2021).Article Google Scholar 
  35. 35.Lipsitch, M., Tchetgen Tchetgen, E. & Cohen, T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology 21, 383–388 (2010).Article Google Scholar 

Download references


This study used data from the VA COVID-19 Shared Data Resource. This research was funded by the US Department of Veterans Affairs (to Z.A.-A.) and two American Society of Nephrology and KidneyCure fellowship awards (to Y.X. and B.B.). The contents do not represent the views of the US Department of Veterans Affairs or the US government.

Post-COVID-19 Tachycardia Syndrome: A Distinct Phenotype of Post-Acute COVID-19 Syndrome

Authors: Marcus Ståhlberg, MD, PhD,a,⁎ Ulrika Reistam, MD,a Artur Fedorowski, MD, PhD,b,c Humberto Villacorta, MD,d Yu Horiuchi, MD,e Jeroen Bax, MD,f Bertram Pitt, MD,g Simon Matskeplishvili, MD,h Thomas F. Lüscher, MD, PhD,i,j Immo Weichert, MD,k Khalid Bin Thani, MD,l and Alan Maisel, MDm

Am J Med. 2021 Dec; 134(12): 1451–1456.Published online 2021 Aug. doi: 10.1016/j.amjmed.2021.07.004


In this paper we highlight the presence of tachycardia in post-acute COVID-19 syndrome by introducing a new label for this phenomenon—post-COVID-19 tachycardia syndrome—and argue that this constitutes a phenotype or sub-syndrome in post-acute COVID-19 syndrome. We also discuss epidemiology, putative mechanisms, treatment options, and future research directions in this novel clinical syndrome.

Clinical Significance

  • • Post-acute COVID-19 syndrome is a novel clinical syndrome with symptoms beyond 4-12 weeks after a SARS-CoV-2 infection
  • • Tachycardia is commonly reported in these patients and may be considered a distinct phenotype
  • • Putative mechanism for tachycardia in this setting include dysautonomia
  • • Post-acute COVID-19 syndrome patients reporting palpitations should be subjected to basic cardiovascular evaluation (including head-up tilt testing if concomitant orthostatic intolerance)
  • • Treatment options include cardiovascular drugs and structured rehabilitation program


The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has triggered a pandemic of coronavirus disease 2019 (COVID-19) lasting for more than 1 year, with over 130,000,000 reported cases globally as of April 2021.1 Due to its novelty and lack of historical data, several aspects of COVID-19 remain unclear. So far, COVID-19 research mostly focused on epidemiology, risk factors for disease severity, description of the clinical course, and identification of optimal management strategies in hospitalized COVID-19 patients.

However, there is growing evidence that COVID-19 may cause persistent symptoms and organ damage that stretch beyond the 3-month period after the infection, usually regarded as the normal convalescence phase. This is now considered to constitute a novel clinical long-term condition: post-acute COVID-19 syndrome.2 The clinical characteristics, pathophysiology, and appropriate management strategies for post-acute COVID-19 syndrome remain largely unknown.

Patients with post-acute COVID-19 syndrome have a wide range of symptoms including fatigue, chest pain, reduced exercise tolerance, cognitive impairment, dyspnea, fever, headache, and loss of smell and taste, but rapid heartbeats and palpitations are typical and frequent complaints.3 We have recently reported that a sub-group of patients with post-acute COVID-19 syndrome develop postural orthostatic tachycardia syndrome, a cardiovascular dysautonomia associated with sinus tachycardia and intolerance following orthostatic challenge.4 However, postural orthostatic tachycardia syndrome is likely not the sole explanation for elevated heart rate; several other conditions may explain tachycardia in post-acute COVID-19 syndrome, for example, inappropriate sinus tachycardia, deconditioning, hypoxia, anxiety, sinus node dysfunction, myocarditis/heart failure, and persistent fever.

In this paper we highlight the presence of tachycardia in post-COVID-19 patients with persisting symptoms by introducing a new label for this phenomenon: post-COVID-19 tachycardia syndrome, and argue that this should be considered a phenotype or sub-syndrome in post-acute COVID-19 syndrome. Furthermore, we discuss the epidemiology, putative mechanisms, treatment options, and future directions for clinical and basic research in this novel clinical syndrome.Go to:

Post-Acute COVID-19 Syndrome

Post-acute COVID-19 syndrome is defined as symptoms after COVID-19 infection persisting for 4-12 or >12 weeks.2 The prevalence of post-acute COVID-19 syndrome remains difficult to establish and varies by definition and methodology used. A recently published structural follow-up of Swedish health care workers with mild COVID-19 documented a post-acute COVID-19 syndrome prevalence of 10%.5 The longest follow-up study to date of hospitalized patients reports that >60% suffer fatigue or muscle weakness at 6 months follow-up.3 Given the extremely high number of reported cases and the uncertain long-term prognosis, post-acute COVID-19 syndrome is likely to become a major clinical problem for the foreseeable future.

Unfortunately, post-acute COVID-19 syndrome remains a poorly defined clinical syndrome. Typical symptoms include headache, fatigue, dyspnea, and mental blurring, but a very extensive list of symptoms reflecting involvement of multiple organs have been reported. Moreover, the type of symptoms reported may differ vastly among individuals with post-acute COVID-19 syndrome. In addition, symptoms are likely to be caused by several different mechanisms. All of this taken together suggests that post-acute COVID-19 syndrome should not be considered a single clinical syndrome but rather a uniting term characterized by different sub-syndromes and phenotypes.6Go to:

Post-COVID-19 Tachycardia Syndrome as a Sub-Syndrome or Phenotype of Post-Acute COVID-19 Syndrome

In our experience, approximately 25%-50% of patients at a tertiary post-COVID multidisciplinary clinic report tachycardia or palpitations persisting 12 weeks or longer. Systematic investigations suggest that 9% of post-acute COVID-19 syndrome patients report palpitations at 6 months.3

We and others have recently presented case reports describing patients with postural orthostatic tachycardia syndrome associated with post-acute COVID-19 syndrome.4 , 7 This syndrome is characterized by sinus tachycardia and symptoms of orthostatic intolerance. Inappropriate sinus tachycardia can also be triggered by infections (and associated conditions) and shares some clinical features with postural orthostatic tachycardia syndrome.8 Importantly, apart from the evident tachycardia, both these conditions are characterized by other non-specific symptoms such as headache, fatigue, and cognitive impairment, resembling symptoms reported in post-acute COVID-19 syndrome.

Moreover, Holter electrocardiogram (ECG) monitoring and measures of heart rate during different physiological challenges may not correlate to reported symptoms in post-acute COVID-19 syndrome, that is, patients with and without abnormally elevated heart rate may share several symptoms and there is no typical symptom strongly linked to the presence or absence of tachycardia in post-acute COVID-19 syndrome.

Together, this suggests that tachycardia is a common feature in post-acute COVID-19 syndrome and it may clinically present as postural orthostatic tachycardia syndrome or inappropriate sinus tachycardia. We suggest that persistent symptomatic tachycardia may be a sub-syndrome or specific phenotype of post-acute COVID-19 syndrome, and propose to label it “post-COVID-19 tachycardia syndrome.”

Potential distinctions and overlaps among post-acute COVID-19 syndrome, other sub-syndromes and post-acute COVID-19 syndrome, as well as postural orthostatic tachycardia syndrome, inappropriate sinus tachycardia, and sinus tachycardia in post-COVID-19 tachycardia syndrome are displayed in Figures 1 A and B, respectively.

Figure 1

Figure 1

Potential distinctions and overlaps between post-COVID tachycardia syndrome and other sub-syndromes in post-acute COVID-19 syndrome. COVID = coronavirus disease.

Moreover, tachycardia can be considered a universal and easily obtainable quantitative marker of post-acute COVID-19 syndrome and its severity rather than patient-reported symptoms, blood testing, and thoracic computed tomography scans. Not only does it reflect autonomic dysfunction, chronic inflammation, possible myocardial injury, or neurophysiological distress, but may reveal the general status of the patient being unhealthy. Holter ECG monitoring and the plethora of mobile personal heart rhythm tracking devices may facilitate diagnosis and treatment monitoring in outpatient settings.Go to:

Putative Mechanisms for Symptomatic Tachycardia in Post-COVID-19 Tachycardia Syndrome

Postural orthostatic tachycardia syndrome is characterized by autonomic dysfunction causing a variety of symptoms, including tachycardia following postural change.9 It has previously been documented that viral infections can trigger postural orthostatic tachycardia syndrome.10 The pathophysiological mechanism in postural orthostatic tachycardia syndrome remains elusive but there is evidence of autoimmunity, that is, autoantibodies activating adrenergic and muscarinic receptors;11 a hyper-adrenergic state;12 peripheral denervation, similar to taste and smell loss, causing blood pooling in the lower extremities; and reflex tachycardia13 and deconditioning.9 In addition, magnetic resonance imaging studies revealed lesions in the midbrain, suggesting that central sympathetic activation may be involved as well.14 All these mechanisms may contribute to tachycardia in postural orthostatic tachycardia syndrome. Whether the same mechanisms are responsible for post-acute COVID-19 syndrome-associated postural orthostatic tachycardia syndrome and to what extent they contribute to post-COVID-19 tachycardia syndrome remain to be established.

Inappropriate sinus tachycardia is defined as an average heart rate exceeding 90 beats per minute on 24-hour ECG monitoring or a resting heart rate >100 beats per minute, and may have several causes, such as gain-of-function mutation in the cardiac pacemaker HCN4 channel,15 cardiac intrinsic sinus node abnormality, autoimmunity, excess sympathetic activation, or vagal withdrawal.8 Clearly, several pathophysiological mechanisms are shared between postural orthostatic tachycardia syndrome and inappropriate sinus tachycardia, but the mechanism for inappropriate sinus tachycardia in the context of post-acute COVID-19 syndrome needs to be established.

Regarding tachycardia in post-acute COVID-19 syndrome, there may be several other factors contributing to the observed heart rate elevation. SARS-CoV-2 enters cells by attaching its spike protein to the angiotensin-converting enzyme 2 receptor, which is abundant in several different cell types and tissues, and the virus therefore can cause injury in several organs.16 Structural injury to the lungs, kidneys, pancreas, and heart have been reported in COVID-19, acutely as well as months after the occurrence of first symptoms, also in low-risk non-hospitalized patients.17 , 18 In addition, COVID-19 may damage the cardiovascular system by other mechanisms such as hyperinflammation, hypercoagulability with thrombosis, and dysfunction of the renin-angiotensin-aldosterone system.19 , 20 These factors may contribute to the observed and reported tachycardia in post-acute COVID-19 syndrome.

In addition to direct and indirect damage caused by the viral infection, there may be several other mechanisms contributing to post-COVID-19 tachycardia syndrome, for example: 1) Persistent pulmonary injury or exacerbation of underlying lung disease causing desaturation and reflex tachycardia;21 2) persistent or intermittent fever, which may increase heart rate;3 3) pain; 4) anxiety and depression;3 5) neuroinflammation; and 6) hypovolemia. Given the novelty of the disease and the lack of basic and clinical data, several unknown mechanisms may also play a role in post-COVID-19 tachycardia syndrome.Go to:

Proposed Cardiovascular Assessment in Patients with Post-COVID-19 Tachycardia Syndrome

We suggest liberal use of at least basic cardiovascular assessment in patients with post-acute COVID-19 syndrome to identify patients with post-COVID-19 tachycardia syndrome (and associated postural orthostatic tachycardia syndrome and inappropriate sinus tachycardia). A 24-hour ambulatory ECG is recommended to detect arrhythmias, assess average heart rate, detect abnormal pulse reactions, and link symptoms to heart rate abnormalities. Figure 2 displays 2 ECGs from patients who meet the criteria of post-acute COVID-19 syndrome. The first ECG (Figure 2A) shows short runs of symptomatic sinus tachycardia (marked with orange arrows) and a typical excessive increase in heart rate in the morning when shifting from bedrest to upright body position (green arrow). These are 24-hour ECG patterns raising suspicion of postural orthostatic tachycardia syndrome. The second ECG shows an elevated average sinus rate of 93 beats per minute, which is consistent with inappropriate sinus tachycardia.

Figure 2

Figure 2

Examples of 24-hour Holter electrocardiogram monitoring from patients with post-COVID tachycardia syndrome due to (A) postural orthostatic tachycardia syndrome and (B) inappropriate sinus tachycardia. COVID = coronavirus disease.

Patients with Holter ECG findings suggestive of postural orthostatic tachycardia syndrome or presenting with symptoms of orthostatic intolerance should optimally perform a head-up tilt test or, at least, an active standing test to confirm the diagnosis.9 A 30-beat-per-minute increase in heart rate within the first 10 minutes of head-up tilt or active standing test without concomitant blood pressure decrease and with reproduction of symptoms is diagnostic of postural orthostatic tachycardia syndrome.9

A transthoracic echocardiogram should be performed to exclude cardiac abnormalities.

Cardiovascular magnetic resonance (CMR) studies have reported a prevalence of myocarditis ranging from 27%-60% in patients recovering from COVID-19.17 , 22 Because perimyocarditis may cause tachycardia, we argue that CMR should be considered in the setting of typical or atypical chest pain, elevated cardiac biomarkers, or typical ECG changes. Moreover, CMR should be performed when cardiovascular autonomic testing did not lead to a diagnosis of cardiac autonomic disturbance (postural orthostatic tachycardia syndrome or inappropriate sinus tachycardia), and the patient reports abnormal or rapid heartbeats.

Blood tests are recommended also, to evaluate extracardiac causes of tachycardia (autoimmune biomarkers, endocrine tests, inflammation biomarkers, autoimmune biomarkers, and hemoglobin levels). Pulmonary pathology is a common source of tachycardia, and basic evaluation should also include peripheral oxygen saturation (at rest and during physiological stress, such as a 6-minute walk test), thoracic computed tomography scan, and spirometry.Go to:

Possible Treatment for Post-COVID Tachycardia Syndrome

Current treatment of postural orthostatic tachycardia syndrome includes the selective sinus node inhibitor ivabradine,23 beta-blockers,9 and compression garments24 to stabilize cardiovascular regulation. Other pharmacological options to reduce associated symptoms are midodrine (symptoms of low blood pressure or cerebral hypoperfusion; peripheral blood pooling), pyridostigmine (muscle weakness; associated gastrointestinal dysfunction) and modafinil (brain fog).25 A structured, regular, and supervised rehabilitation program is also recommended.25 Immunomodulation and drugs targeting possible associated mast cell activation syndrome have not been systematically evaluated in postural orthostatic tachycardia syndrome, but might be considered ex iuvantibus if the typical clinical manifestation is present.26

Although postural orthostatic tachycardia syndrome in the context of COVID-19 may be different from the “traditional” postural orthostatic tachycardia syndrome (pre-COVID-19), we suggest starting patients with post-acute COVID-19 syndrome and postural orthostatic tachycardia syndrome on heart rate-lowering drugs and a rehabilitation program. Other pharmacological interventions may also be considered but should be carefully monitored.

Whether patients with post-COVID-19 tachycardia syndrome are responsive to heart rate-lowering drugs and other symptomatic treatment previously used in postural orthostatic tachycardia syndrome remains to be established.Go to:

Future Endeavors

Basic and clinical research programs to characterize post-COVID-19 tachycardia syndrome and determine similarities and disparities with other sub-syndromes of post-acute COVID-19 syndrome are highly warranted. A clear aim should be to improve our understanding of the pathophysiology of long-term post-COVID-19 complications and to find novel targets for interventions that may provide disease-modifying effects rather than focusing on pure symptom control.

We therefore call for large registries containing both clinical data and biomarkers, and interventional studies testing the efficacy of drugs used previously in traditional postural orthostatic tachycardia syndrome, alone or in combination with experimental drugs targeting putative mechanism in post-COVID-19 tachycardia syndrome.Go to:


We highlight the phenomenon of abnormal sinus tachycardia in patients with post-acute COVID-19 syndrome. We propose that post-COVID-19 tachycardia syndrome should be considered a phenotype or sub-syndrome of post-acute COVID-19 syndrome. This provides a safety net for those who have multiple symptoms besides the tachycardia and who subsequently may not even mention this to their health care provider.

Post-COVID-19 tachycardia syndrome may present as postural orthostatic tachycardia syndrome or inappropriate sinus tachycardia, and likely contributes to several symptoms and the physical and mental disabilities in post-acute COVID-19 syndrome. Future studies should focus on biological and clinical characterization of this novel clinical syndrome and interventional studies, testing established and novel pharmacological approaches.Go to:


Funding: None.

Conflicts of Interest: None.

Authorship: All authors had access to the data and have read and approved the final version of the manuscript.Go to:


1. Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Covid-19 Map – Johns Hopkins Coronavirus Resource Center. Available at: Accessed April 14, 2021.2. Nalbandian A, Sehgal K, Gupta A, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27(4):601–615. [PubMed] [Google Scholar]3. Huang C, Huang L, Wang Y, et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet. 2021;397(10270):220–232. [PMC free article] [PubMed] [Google Scholar]4. Johansson M, Stahlberg M, Runold M, et al. Long-haul post-COVID-19 symptoms presenting as a variant of postural orthostatic tachycardia syndrome: the Swedish experience. JACC Case Rep. 2021;3(4):573–580. [PMC free article] [PubMed] [Google Scholar]5. Havervall S, Rosell A, Phillipson M, et al. Symptoms and functional impairment assessed 8 months after mild COVID-19 among health care workers. JAMA. 2021;325(19):2015–2016. [PMC free article] [PubMed] [Google Scholar]6. Yong SJ. Long-haul COVID-19: putative pathophysiology, risk factors, and treatments. Preprints. 2020 Available at: Accessed August 14, 2021. [Google Scholar]7. Miglis MG, Prieto T, Shaik R, Muppidi S, Sinn DI, Jaradeh S. A case report of postural tachycardia syndrome after COVID-19. Clin Auton Res. 2020;30(5):449–451. [PMC free article] [PubMed] [Google Scholar]8. Olshansky B, Sullivan RM. Inappropriate sinus tachycardia. Europace. 2019;21(2):194–207. [PubMed] [Google Scholar]9. Fedorowski A. Postural orthostatic tachycardia syndrome: clinical presentation, aetiology and management. J Intern Med. 2019;285(4):352–366. [PubMed] [Google Scholar]10. Vernino S, Stiles LE. Autoimmunity in postural orthostatic tachycardia syndrome: current understanding. Auton Neurosci. 2018;215:78–82. [PubMed] [Google Scholar]11. Kharraziha I, Axelsson J, Ricci F, et al. Serum activity against G protein-coupled receptors and severity of orthostatic symptoms in postural orthostatic tachycardia syndrome. J Am Heart Assoc. 2020;9(15) [PMC free article] [PubMed] [Google Scholar]12. Schondorf R, Low PA. Idiopathic postural orthostatic tachycardia syndrome: an attenuated form of acute pandysautonomia? Neurology. 1993;43(1):132–137. [PubMed] [Google Scholar]13. Jacob G, Costa F, Shannon JR, et al. The neuropathic postural tachycardia syndrome. N Engl J Med. 2000;343(14):1008–1014. [PubMed] [Google Scholar]14. Raman B, Cassar MP, Tunnicliffe EM, et al. Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge. EClinicalMedicine. 2021;31 [PMC free article] [PubMed] [Google Scholar]15. Baruscotti M, Bucchi A, Milanesi R, et al. A gain-of-function mutation in the cardiac pacemaker HCN4 channel increasing cAMP sensitivity is associated with familial Inappropriate Sinus Tachycardia. Eur Heart J. 2017;38(4):280–288. [PubMed] [Google Scholar]16. Wiese O, Zemlin AE, Pillay TS. Molecules in pathogenesis: angiotensin converting enzyme 2 (ACE2) J Clin Pathol. 2021;74(5):285–290. [PMC free article] [PubMed] [Google Scholar]17. Kotecha T, Knight DS, Razvi Y, et al. Patterns of myocardial injury in recovered troponin-positive COVID-19 patients assessed by cardiovascular magnetic resonance. Eur Heart J. 2021;42(19):1866–1878. [PMC free article] [PubMed] [Google Scholar]18. Dennis A, Wamil M, Alberts J, et al. Multiorgan impairment in low-risk individuals with post-COVID-19 syndrome: a prospective, community-based study. BMJ Open. 2021;11(3) [PMC free article] [PubMed] [Google Scholar]19. Gupta A, Madhavan MV, Sehgal K, et al. Extrapulmonary manifestations of COVID-19. Nat Med. 2020;26(7):1017–1032. [PubMed] [Google Scholar]20. van der Linden J, Almskog L, Liliequist A, et al. Thromboembolism, hypercoagulopathy, and antiphospholipid antibodies in critically ill coronavirus disease 2019 patients: a before and after study of enhanced anticoagulation. Crit Care Explor. 2020;2(12):e0308. [PMC free article] [PubMed] [Google Scholar]21. Zhao YM, Shang YM, Song WB, et al. Follow-up study of the pulmonary function and related physiological characteristics of COVID-19 survivors three months after recovery. EClinicalMedicine. 2020;25 [PMC free article] [PubMed] [Google Scholar]22. Puntmann VO, Carerj ML, Wieters I, et al. Outcomes of cardiovascular magnetic resonance imaging in patients recently recovered from coronavirus disease 2019 (COVID-19) JAMA Cardiol. 2020;5(11):1265–1273. [PMC free article] [PubMed] [Google Scholar]23. Taub PR, Zadourian A, Lo HC, Ormiston CK, Golshan S, Hsu JC. Randomized trial of ivabradine in patients with hyperadrenergic postural orthostatic tachycardia syndrome. J Am Coll Cardiol. 2021;77(7):861–871. [PubMed] [Google Scholar]24. Bourne KM, Sheldon RS, Hall J, et al. Compression garment reduces orthostatic tachycardia and symptoms in patients with postural orthostatic tachycardia syndrome. J Am Coll Cardiol. 2021;77(3):285–296. [PubMed] [Google Scholar]25. Sheldon RS, Grubb BP, 2nd, Olshansky B, et al. 2015 Heart Rhythm Society expert consensus statement on the diagnosis and treatment of postural tachycardia syndrome, inappropriate sinus tachycardia, and vasovagal syncope. Heart Rhythm. 2015;12(6):e41–e63. [PMC free article] [PubMed] [Google Scholar]26. Shibao C, Arzubiaga C, Roberts LJ, 2nd, et al. Hyperadrenergic postural tachycardia syndrome in mast cell activation disorders. Hypertension. 2005;45(3):385–390. [PubMed] [Google Scholar]

See all…


Recent Activity

ClearTurn Off

See more…Support CenterSupport Center