Skip to main content
Critical Care logoLink to Critical Care
letter
. 2020 Jul 14;24:429. doi: 10.1186/s13054-020-03154-4

Atorvastatin associated with decreased hazard for death in COVID-19 patients admitted to an ICU: a retrospective cohort study

Guillermo Rodriguez-Nava 1,, Daniela Patricia Trelles-Garcia 1, Maria Adriana Yanez-Bello 1, Chul Won Chung 1, Valeria Patricia Trelles-Garcia 2, Harvey J Friedman 3,4
PMCID: PMC7358561  PMID: 32664990

To the editor,

Since the World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) a pandemic, the medical community started a race against time to find effective treatments for this disease [1]. Atorvastatin as adjuvant immunomodulatory therapy is of particular interest given its low cost, known safety profile, and availability. The severe acute respiratory syndrome coronavirus (SARS-CoV) has been shown to interact with Toll-like receptors on the host cell membrane, increasing the expression of the MYD88 gene, ultimately activating NF-κB and triggering inflammatory pathways. Experimental models have demonstrated that statins stabilize MYD88 levels after a pro-inflammatory trigger, and, in a murine model, atorvastatin significantly attenuated NF-κB activation [2]. Furthermore, in the real world, two retrospective cohort studies reported a reduced risk of influenza death among statin users [3, 4]. Therefore, we assessed whether statin users at a dose of 40 mg daily had reduced inpatient mortality hazard from COVID-19.

In this retrospective cohort study, we used a de-identified dataset that included 87 adult patients with laboratory-confirmed COVID-19 admitted to our community hospital intensive care unit (ICU) located in Evanston, IL, from March to May 2020. We performed a Cox proportional hazards (PH) regression model to examine the relationship between adjuvant treatments and inpatient mortality. To minimize confounders, we adjusted for age, hypertension, cardiovascular disease, invasive mechanical ventilation, severity according to the National Institutes of Health criteria (respiratory rate > 30, SpO2 < 94%, PaO2/FiO2 < 300 mmHg or lung infiltrates > 50%), number of comorbidities (as a continuous variable), and other adjuvant therapies (including hydroxychloroquine, intravenous steroids, azithromycin, tocilizumab, colchicine, and antibiotics), forcing these variables into the model. We also performed a sensitivity analysis calculating the E value (with the lower confidence limit) as described by VanderWeele et al. [5, 6] for the obtained point estimate. The E value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome, conditional on the measured covariates, to explain away a specific exposure-outcome association fully.

The median age was 68 years (interquartile range [IQR], 58–75 years), 56 (64.4%) were males, and 50 (57.5%) were skilled nursing facility residents. Of these patients, 39 (44.8%) were ultimately discharged from the hospital, median length of stay 13 days (IQR, 7 to 21 days), and 48 (55.2%) had died, median length of stay 9.5 days (IQR, 3 to 14.75 days), a statistically significant difference (p = 0.032 by Mann-Whitney U test). A total of 24 (61.5%) survivors received atorvastatin 40 mg daily compared to 23 (47.9%) of non-survivors (p = 0.20 by chi-squared). In the multivariable Cox PH regression model, atorvastatin non-users had a 73% chance of faster progression to death compared with atorvastatin users (when probability = HR/HR + 1) (Table 1). The E value for the point estimate was 3.29 and the E value for the lower confidence interval was 1.69, meaning that a confounder not included in the multivariable Cox PH regression model associated with atorvastatin use and inpatient mortality in patients with COVID-19 by a hazard ratio of 1.69-fold each could explain away the lower confidence limit, but weaker confounding could not.

Table 1.

Multivariable Cox regression of target interventions for COVID-19

Intervention Adjusted HRa 95% CI pvalueb
Atorvastatin 0.38 0.18–0.77 0.008
Steroids 1.93 0.81–4.59 0.136
Hydroxychloroquine 0.81 0.31–2.10 0.675
Colchicine 0.41 0.17–0.98 0.045
Tocilizumab 1.17 0.42–3.25 0.765
Azithromycin 0.49 0.20–1.23 0.132

Abbreviations: CI confidence interval, COVID-19 coronavirus disease 2019, HR hazard ratio

aAdjusted for age, number of comorbidities, hypertension, cardiovascular disease, severity, invasive mechanical ventilation, and antibiotics other than azithromycin

bp < 0.05 was considered statistically significant

In conclusion, we found a slower progression to death associated with atorvastatin in patients with COVID-19 admitted to our ICU. Given the observational nature of this study, results should be taken with caution; randomized controlled trials are needed to confirm this benefit (STATCO19, identifier NCT04380402). To date, supportive care remains the mainstay of therapy, and the clinical efficacy for various treatments is still under investigation.

Acknowledgements

We dedicate this work to the intensive care unit nurses who did a fantastic job during the peak of the crisis, taking care of a higher patient ratio than usual, providing compassionate and high-quality care regardless of the increased workload, and risking their health selflessly to help the community. Without them, our work would have been harder.

Abbreviations

CI

Confidence interval

COVID-19

Coronavirus disease 2019

HR

Hazard ratio

ICU

Intensive care unit

IQR

Interquartile range

PaO2/FiO2

Arterial oxygen partial pressure to fractional inspired oxygen

PH

Proportional hazards

SARS-CoV

Severe acute respiratory syndrome coronavirus

SpO2

Oxygen saturation

WHO

World Health Organization

Authors’ contributions

GRN: conceptualization, acquisition of the dataset, statistical analysis, original draft preparation, reviewing, literature review, and editing. DPTG: conceptualization, writing, and reviewing. MAYB, CWC, and VPTG: manuscript revision. HJF: study supervisor. All the authors read and approved the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Research using de-identified data does not constitute research with human subjects because there is no interaction with any individual, and no identifiable private information is used. The project did not, therefore, require IRB or ethics committee review by AMITA Health.

Consent for publication

The research presents no more than minimal risk of harm to subjects and involves no procedures; hence, written consent requirement was waived.

Competing interests

The authors declare no conflict of interest.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Mccreary EK, Pogue JM. Coronavirus disease 2019 treatment: a review of early and emerging options. Open Forum Infect Dis. 2020;7(4):ofaa105. doi: 10.1093/ofid/ofaa105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Castiglione V, Chiriacò M, Emdin M, Taddei S, Vergaro G. Statin therapy in COVID19 infection. Eur Heart J Cardiovasc Pharmacother. 2020. [Epub ahead of print]. [DOI] [PMC free article] [PubMed]
  • 3.Frost FJ, Petersen H, Tollestrup K, Skipper B. Influenza and COPD mortality protection as pleiotropic, dose-dependent effects of statins. Chest. 2007;131(4):1006–1012. doi: 10.1378/chest.06-1997. [DOI] [PubMed] [Google Scholar]
  • 4.Vandermeer ML, Thomas AR, Kamimoto L, et al. Association between use of statins and mortality among patients hospitalized with laboratory-confirmed influenza virus infections: a multistate study. J Infect Dis. 2012;205(1):13–19. doi: 10.1093/infdis/jir695. [DOI] [PubMed] [Google Scholar]
  • 5.VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med. 2017;167(4):268–274. doi: 10.7326/M16-2607. [DOI] [PubMed] [Google Scholar]
  • 6.Mathur MB, Ding P, Riddell CA, VanderWeele TJ. Website and R package for computing E-values. Epidemiology. 2018;29(5):e45–e47. doi: 10.1097/EDE.0000000000000864. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.


Articles from Critical Care are provided here courtesy of BMC

RESOURCES