Long-term cardiovascular outcomes of COVID-19
Yan Xie, Evan Xu, Benjamin Bowe, Ziyad Al-Aly, Yan Xie, Evan Xu, Benjamin Bowe, Ziyad Al-Aly
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.
Conflict of interest statement
The authors declare no competing interests.
© 2022. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
Figures
![Fig. 1. Flowchart of cohort construction.](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig1_HTML.jpg)
![Fig. 2. Risks and 12-month burdens of…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig2_HTML.jpg)
![Fig. 3. Risks and 12-month burdens of…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig3_HTML.jpg)
![Fig. 4. Subgroup analyses of the risks…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig4_HTML.jpg)
![Fig. 5. Risks and 12-month burdens of…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig5_HTML.jpg)
![Fig. 6. Risks and 12-month burdens of…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig6_HTML.jpg)
![Extended Data Fig. 1. Standardized mean difference…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig7_ESM.jpg)
![Extended Data Fig. 2. Risks and 12-month…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig8_ESM.jpg)
![Extended Data Fig. 3. Risks and 12-month…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig9_ESM.jpg)
![Extended Data Fig. 4. Risks and 12-month…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig10_ESM.jpg)
![Extended Data Fig. 5. Risks and 12-month…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig11_ESM.jpg)
![Extended Data Fig. 6. Subgroup analyses of…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig12_ESM.jpg)
![Extended Data Fig. 7. Risks and 12-month…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig13_ESM.jpg)
![Extended Data Fig. 8. Risks and 12-month…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig14_ESM.jpg)
![Extended Data Fig. 9. Risks and 12-month…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig15_ESM.jpg)
![Extended Data Fig. 10. Risks and 12-month…](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/8938267/bin/41591_2022_1689_Fig16_ESM.jpg)
References
- Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of post-acute sequelae of COVID-19. Nature. 2021;594:259–264. doi: 10.1038/s41586-021-03553-9.
- Ayoubkhani D, et al. Post-COVID syndrome in individuals admitted to hospital with COVID-19: retrospective cohort study. BMJ. 2021;372:n693. doi: 10.1136/bmj.n693.
- Huang, C. et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet397, 220−232 (2021).
- Carfi, A., Bernabei, R., Landi, F. & the Gemelli Against COVID-19 Post-Acute Care Study Group. Persistent symptoms in patients after acute COVID-19. JAMA324, 603–605 (2020).
- Daugherty SE, et al. Risk of clinical sequelae after the acute phase of SARS-CoV-2 infection: retrospective cohort study. BMJ. 2021;373:n1098. doi: 10.1136/bmj.n1098.
- Katsoularis I, Fonseca-Rodriguez O, Farrington P, Lindmark K, Fors Connolly AM. Risk of acute myocardial infarction and ischaemic stroke following COVID-19 in Sweden: a self-controlled case series and matched cohort study. Lancet. 2021;398:599–607. doi: 10.1016/S0140-6736(21)00896-5.
- 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. 2021;371:m4677.
- Gupta A, et al. Extrapulmonary manifestations of COVID-19. Nat. Med. 2020;26:1017–1032. doi: 10.1038/s41591-020-0968-3.
- Alwan NA. The road to addressing long COVID. Science. 2021;373:491–493. doi: 10.1126/science.abg7113.
- Briggs A, Vassall A. Count the cost of disability caused by COVID-19. Nature. 2021;593:502–505. doi: 10.1038/d41586-021-01392-2.
- Farshidfar, F., Koleini, N. & Ardehali, H. Cardiovascular complications of COVID-19. JCI Insight6, e148980 (2021).
- Nalbandian A, et al. Post-acute COVID-19 syndrome. Nat. Med. 2021;27:601–615. doi: 10.1038/s41591-021-01283-z.
- Nishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Nat. Rev. Cardiol. 2020;17:543–558. doi: 10.1038/s41569-020-0413-9.
- Chung MK, et al. COVID-19 and cardiovascular disease. Circ. Res. 2021;128:1214–1236. doi: 10.1161/CIRCRESAHA.121.317997.
- Delorey TM, et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature. 2021;595:107–113. doi: 10.1038/s41586-021-03570-8.
- Song W-C, FitzGerald GA. COVID-19, microangiopathy, hemostatic activation, and complement. J. Clin. Invest. 2020;130:3950–3953.
- Varga Z, et al. Endothelial cell infection and endotheliitis in COVID-19. Lancet. 2020;395:1417–1418. doi: 10.1016/S0140-6736(20)30937-5.
- Long-term Immunological Health Consequences of COVID-19 (British Society for Immunology, 2020);
- Di Toro A, et al. Long COVID: long-term effects? Eur. Heart J. Suppl. 2021;23:E1–E5. doi: 10.1093/eurheartj/suab080.
- 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. USA118, e2105968118 (2021).
- 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. 2021;11:e047369. doi: 10.1136/bmjopen-2020-047369.
- Kind AJH, Buckingham WR. Making neighborhood-disadvantage metrics accessible—The Neighborhood Atlas. N. Engl. J. Med. 2018;378:2456–2458. doi: 10.1056/NEJMp1802313.
- 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. 2021;12:6571. doi: 10.1038/s41467-021-26513-3.
- Bowe B, et al. Acute kidney injury in a national cohort of hospitalized US veterans with COVID-19. Clin. J. Am. Soc. Nephrol. 2020;16:14–25. doi: 10.2215/CJN.09610620.
- Bowe B, Xie Y, Xu E, Al-Aly Z. Kidney outcomes in long COVID. J. Am. Soc. Nephrol. 2021;32:2851–2862. doi: 10.1681/ASN.2021060734.
- Harrell, F. E. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (Springer, 2015).
- Schneeweiss S, et al. High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology. 2009;20:512–522. doi: 10.1097/EDE.0b013e3181a663cc.
- Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav. Res. 2011;46:399–424. doi: 10.1080/00273171.2011.568786.
- Wing C, Simon K, Bello-Gomez RA. Designing difference in difference studies: best practices for public health policy research. Annu. Rev. Public Health. 2018;39:453–469. doi: 10.1146/annurev-publhealth-040617-013507.
- Lechner M. The estimation of causal effects by difference-in-difference methods. Found. Trends Econom. 2011;4:165–224. doi: 10.1561/0800000014.
- Imbens GW, Angrist JD. Identification and estimation of local average treatment effects. Econometrica. 1994;62:467–475. doi: 10.2307/2951620.
- Dimick JB, Ryan AM. Methods for evaluating changes in health care policy: the difference-in-differences approach. JAMA. 2014;312:2401–2402. doi: 10.1001/jama.2014.16153.
- Funk MJ, et al. Doubly robust estimation of causal effects. Am. J. Epidemiol. 2011;173:761–767. doi: 10.1093/aje/kwq439.
- Davis HE, et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine. 2021;38:101019. doi: 10.1016/j.eclinm.2021.101019.
- Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology. 2010;21:383–388. doi: 10.1097/EDE.0b013e3181d61eeb.
Source: PubMed