ABSTRACT
A binary model for the classification of chronic diseases has formerly been proposed. The model classifies chronic diseases as "high Treg" or "low Treg" diseases according to the extent of regulatory T cells (Treg) activity (frequency or function) observed. The present paper applies this model to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The model correctly predicts the efficacy or inefficacy of several immune-modulating drugs in the treatment of severe coronavirus disease 2019 (COVID-19) disease. It also correctly predicts the class of pathogens mostly associated with SARS-CoV-2 infection. The clinical implications are the following: (a) any search for new immune-modulating drugs for the treatment of COVID-19 should exclude candidates that do not induce "high Treg" immune reaction or those that do not spare CD8+ T cells; (b) immune-modulating drugs, which are effective against SARS-CoV-2, may not be effective against any variant of the virus that does not induce "low Treg" reaction; (c) any immune-modulating drug, which is effective in treating COVID-19, will also alleviate most coinfections; and (d) severe COVID-19 patients should avoid contact with carriers of "low Treg" pathogens.
Subject(s)
CD8-Positive T-Lymphocytes/immunology , COVID-19 Drug Treatment , Immunomodulation/drug effects , T-Lymphocytes, Regulatory/immunology , Adrenal Cortex Hormones/therapeutic use , COVID-19/immunology , Chronic Disease/classification , Humans , Hydroxymethylglutaryl-CoA Reductase Inhibitors/pharmacology , Janus Kinase Inhibitors/therapeutic use , SARS-CoV-2/drug effects , SARS-CoV-2/immunology , Sirolimus/therapeutic useABSTRACT
BACKGROUND: At the beginning of June 2020, there were nearly 7 million reported cases of coronavirus disease 2019 (COVID-19) worldwide and over 400,000 deaths in people with COVID-19. The objective of this study was to determine associations between comorbidities listed in the Charlson comorbidity index and mortality among patients in the United States with COVID-19. METHODS AND FINDINGS: A retrospective cohort study of adults with COVID-19 from 24 healthcare organizations in the US was conducted. The study included adults aged 18-90 years with COVID-19 coded in their electronic medical records between January 20, 2020, and May 26, 2020. Results were also stratified by age groups (<50 years, 50-69 years, or 70-90 years). A total of 31,461 patients were included. Median age was 50 years (interquartile range [IQR], 35-63) and 54.5% (n = 17,155) were female. The most common comorbidities listed in the Charlson comorbidity index were chronic pulmonary disease (17.5%, n = 5,513) and diabetes mellitus (15.0%, n = 4,710). Multivariate logistic regression analyses showed older age (odds ratio [OR] per year 1.06; 95% confidence interval [CI] 1.06-1.07; p < 0.001), male sex (OR 1.75; 95% CI 1.55-1.98; p < 0.001), being black or African American compared to white (OR 1.50; 95% CI 1.31-1.71; p < 0.001), myocardial infarction (OR 1.97; 95% CI 1.64-2.35; p < 0.001), congestive heart failure (OR 1.42; 95% CI 1.21-1.67; p < 0.001), dementia (OR 1.29; 95% CI 1.07-1.56; p = 0.008), chronic pulmonary disease (OR 1.24; 95% CI 1.08-1.43; p = 0.003), mild liver disease (OR 1.26; 95% CI 1.00-1.59; p = 0.046), moderate/severe liver disease (OR 2.62; 95% CI 1.53-4.47; p < 0.001), renal disease (OR 2.13; 95% CI 1.84-2.46; p < 0.001), and metastatic solid tumor (OR 1.70; 95% CI 1.19-2.43; p = 0.004) were associated with higher odds of mortality with COVID-19. Older age, male sex, and being black or African American (compared to being white) remained significantly associated with higher odds of death in age-stratified analyses. There were differences in which comorbidities were significantly associated with mortality between age groups. Limitations include that the data were collected from the healthcare organization electronic medical record databases and some comorbidities may be underreported and ethnicity was unknown for 24% of participants. Deaths during an inpatient or outpatient visit at the participating healthcare organizations were recorded; however, deaths occurring outside of the hospital setting are not well captured. CONCLUSIONS: Identifying patient characteristics and conditions associated with mortality with COVID-19 is important for hypothesis generating for clinical trials and to develop targeted intervention strategies.