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.

Recommendations

  • 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).

Rationale

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

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

Results

  • 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).

Limitations

  • 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.

The PRINCIPLE Trial

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.

Results

  • 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.

Limitations

  • 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

The RECOVERY Trial

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

Results

  • 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.

Limitations

  • 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

Results

  • 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).

Limitations

  • 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).

References

  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: https://www.ncbi.nlm.nih.gov/pubmed/25123076.
  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: https://www.ncbi.nlm.nih.gov/pubmed/33137319.
  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: https://www.ncbi.nlm.nih.gov/pubmed/33293273.
  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: https://www.ncbi.nlm.nih.gov/pubmed/34051877.
  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: https://www.medrxiv.org/content/10.1101/2021.09.20.21263828v1.
  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: https://www.ncbi.nlm.nih.gov/pubmed/34672950.
  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: https://www.ncbi.nlm.nih.gov/pubmed/32579195.
  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: https://www.ncbi.nlm.nih.gov/pubmed/32937800.
  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: https://www.ncbi.nlm.nih.gov/pubmed/33133323.
  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: https://www.ncbi.nlm.nih.gov/pubmed/33542047.
  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: https://www.researchsquare.com/article/rs-69374/v1.
  12. Colchicine (Colcrys) [package insert]. Food and Drug Administration. 2012. Available at: https://www.accessdata.fda.gov/drugsatfda_docs/label/2014/022352s017lbl.pdf.
  13. American College of Cardiology. AHA statement on drug-drug interactions with statins. 2016. Available at: https://www.acc.org/latest-in-cardiology/ten-points-to-remember/2016/10/20/21/53/recommendations-for-management-of-clinically-significant-drug. 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: https://www.ncbi.nlm.nih.gov/pubmed/29029311.

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

Long-term cardiovascular outcomes of COVID-19

Abstract

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.

Main

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).

Results

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).

Dysrhythmias

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).

Discussion

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.

Methods

Setting

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.

Cohort

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.

Covariates

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 https://www.virec.research.va.gov or contact the VA Information Resource Center at VIReC@va.gov.

Code availability

SAS codes are available at https://github.com/yxie618/longCVD and https://doi.org/10.5281/zenodo.5799457.

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Acknowledgements

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.

Covid-19 Vaccine Analysis: The most common adverse events reported so far

Authors: DATED: AUGUST 6, 2021 BY SHARYL ATTKISSON 

As of July 19, 2021 there were 419,513 adverse event reports associated with Covid-19 vaccination in the U.S., with a total of 1,814,326 symptoms reported. That’s according to the federal Vaccine Adverse Event Reporting System (VAERS) database.

Report an adverse event after vaccination online here.

Each symptom reported does not necessarily equal one patient. Adverse event reports often include multiple symptoms for a single patient.

Reporting of illnesses and symptoms that occur after Covid-19 vaccination does not necessarily mean they were caused by the vaccine. The system is designed to collect adverse events that occur after vaccination to uncover any patterns of illnesses that were not captured during vaccine studies.

Read CDC info on Covid-19 vaccine here.

Scientists have estimated that adverse events occur at a rate many fold higher than what is reported in VAERS, since it is assumed that most adverse events are not reported through the tracking system. Reports can be made by doctors, patients or family members and/or acquaintances, or vaccine industry representatives. 

Read: Exclusive summary: Covid-19 vaccine concerns.

Some observers claim Covid-19 vaccine adverse events are not as likely to be underreported as those associated with other medicine, due to close monitoring and widespread publicity surrounding Covid-19 vaccination.

Approximately 340 million doses of Covid-19 vaccine have been given in the U.S. Slightly less than half of the U.S. population is fully vaccinated.

According to the Centers for Disease Control (CDC) and Food and Drug Administration (FDA), the benefits of Covid-19 vaccine outweigh the risks for all groups and age categories authorized to receive it.

Watch: CDC disinformation re: studies on Covid-19 vaccine effectiveness in people who have had Covid-19.

The following is a summary of some of the most frequent adverse events reported to VAERS after Covid-19 vaccination. (It is not the entire list.)

Most common Covid-19 vaccine adverse events reported as of July 19, 2021

Yellow highlighted adverse events are subjects of investigations, warnings or stated concerns by public health officials. For details, click here.

128,370 Muscle, bone, joint pain and swelling including:

  • 39,902 Pain in extremity
  • 37,819 Myalgia, muscle pain, weakness, fatigue, spasms, disorders, related
  • 30,138 Arthralgia, joint pain or arthritis, swelling, joint disease, bone pain, spinal osteoarthritis
  • 14,682 Back pain, neck pain
  • 5,829 Muscle and skeletal pain, stiffness, weakness

119,866 Injection site pain, bleeding, hardening, bruising, etc.

105,332 Skin reddening, at injection site or elsewhere, rash, hives

100,564 Fatigue, lethargy, malaise, asthenia, abnormal weakness, loss of energy

89,302 Headache, incl. migraine, sinus

68,252 Vomiting, nausea

68,064 Fever

63,133 Chills

60,913 Pain

49,574 Dizziness

34,076 Flushing, hot flush, feeling hot, abnormally warm skin

31,785 Lung pain or abnormalities, fluid in lung, respiratory tract or lung congestion or infection, wheezing, acute respiratory failure including:

  • 23,005 Dyspnoea, difficulty breathing
  • 1,398 Pneumonia
  • 1,128 Respiratory arrest, failure, stopped or inefficient breathing, abnormal breathing
  • 563 Covid-19 pneumonia
  • 265 Mechanical ventilation
  • 217 Bronchitis

30,909 Skin swelling, pain, tightness, face swelling, swelling under skin, hives, angioedema including:

  • 7,579 Skin pain, sensitivity, burning, discoloration, tenderness

25,319 Heart failure, heart rhythm and rate abnormalities, atrial fibrillation, palpitations, flutter, murmur, pacemaker added, fluid in heart, abnormal echocardiogram including:

  • 3,105 Heart attack or cardiac arrest, sudden loss of blood flow from failure to pump to heart effectively, cardiac failure, disorder

22,085 Itchiness

29,861 Sensory disturbance including:

  • 8,236 Tinnitus, hearing noise
  • 7,951 Abnormal vision, blindness
  • 6,349 Ageusia, loss of taste, altered taste, disorders
  • 2,249 Anosmia, loss of smell, parosmia (rotten smell)
  • 2,075 Hypersensitivity
  • 1,560 Sensitivity or reaction to light 
  • 890 Hearing loss, deafness

High sensitivity troponin and COVID-19 outcomes

Authors: Nikolaos Papageorgiou,a,bCatrin Sohrabi,aDavid Prieto Merino,c,dAngelos Tyrlis,aAbed Elfattah Atieh,aBunny Saberwal,aWei-Yao Lim,aAntonio Creta,aMohammed Khanji,aReni Rusinova,aBashistraj Chooneea,aRaj Khiani,d,eNadeev Wijesuriya,e,fAnna Chow,e,fHaroun Butt,e,fStefan Browne,e,fNikhil Joshi,e,fJamie Kay,e,fSyed Ahsan,a and Rui Providenciaa,g

Abstract

Background

Recent reports have demonstrated high troponin levels in patients affected with COVID-19. In the present study, we aimed to determine the association between admission and peak troponin levels and COVID-19 outcomes.

Methods

This was an observational multi-ethnic multi-centre study in a UK cohort of 434 patients admitted and diagnosed COVID-19 positive, across six hospitals in London, UK during the second half of March 2020.

Results

Myocardial injury, defined as positive troponin during admission was observed in 288 (66.4%) patients. Age (OR: 1.68 [1.49–1.88], p < .001), hypertension (OR: 1.81 [1.10–2.99], p = .020) and moderate chronic kidney disease (OR: 9.12 [95% CI: 4.24–19.64], p < .001) independently predicted myocardial injury. After adjustment, patients with positive peak troponin were more likely to need non-invasive and mechanical ventilation (OR: 2.40 [95% CI: 1.27–4.56], p = .007, and OR: 6.81 [95% CI: 3.40–13.62], p < .001, respectively) and urgent renal replacement therapy (OR: 4.14 [95% CI: 1.34–12.78], p = .013). With regards to events, and after adjustment, positive peak troponin levels were independently associated with acute kidney injury (OR: 6.76 [95% CI: 3.40–13.47], p < .001), venous thromboembolism (OR: 11.99 [95% CI: 3.20–44.88], p < .001), development of atrial fibrillation (OR: 10.66 [95% CI: 1.33–85.32], p = .026) and death during admission (OR: 2.40 [95% CI: 1.34–4.29], p = .003). Similar associations were observed for admission troponin. In addition, median length of stay in days was shorter for patients with negative troponin levels: 8 (5–13) negative, 14 (7–23) low-positive levels and 16 (10–23) high-positive (p < .001).

Conclusions

Admission and peak troponin appear to be predictors for cardiovascular and non-cardiovascular events and outcomes in COVID-19 patients, and their utilization may have an impact on patient management.

For More Information: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7970632/