Predicting Clinical Outcomes of SARS-CoV-2 Infection During the Omicron Wave Using Machine Learning (preprint)

The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilitate the identification of patients at highest risk of adverse outcomes is a priority. The study objectives are to develop population-scale predictive models that: 1) identify predictors of adverse outcomes with Omicron surge SARS-CoV-2 infections, and 2) predict the impact of prioritized vaccination of high-risk groups for said outcome. We prepared a retrospective longitudinal observational study of a national cohort of 192,984 patients in the U.S. Veteran Health Administration who tested positive for SARS-CoV-2 from January 15 to August 15, 2022. We utilized sociodemographic characteristics, comorbidities, vaccination status, and prior COVID-19 infections, at time of testing positive for SARS-CoV-2 to predict hospitalization, escalation of care (high-flow oxygen, mechanical ventilation, vasopressor use, dialysis, or extracorporeal membrane oxygenation), and death within 30 days. Machine learning models demonstrated that advanced age, high comorbidity burden, lower body mass index, unvaccinated status, prior SARS-CoV-2 infection, and oral anticoagulant use were the important predictors of hospitalization and escalation of care. Similar factors predicted death. However, prior SARS-CoV-2 infection was associated with lower 30-day mortality, and anticoagulant use did not predict mortality risk. The all-cause death model showed the highest discrimination (Area Under the Curve (AUC) = 0.895, 95% Confidence Interval (CI): 0.885, 0.906) followed by hospitalization (AUC = 0.829, CI: 0.825, 0.834), then escalation of care (AUC=0.805, CI: 0.795, 0.814). Assuming a vaccine efficacy range of 70.8 to 78.7%, our simulations projected that targeted prevention in the highest risk group may have reduced 30-day hospitalization, care escalation, and death in more than 2 of 5 unvaccinated patients.

A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program (preprint)

The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n=35) or hospitalization (n=42) due to severe COVID-19 using genome-wide association summary from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (nrs495828= 53 and nrs505922=59); strongest association with venous embolism, odds ratio (ORrs495828 1.33 (p=1.32 x 10-199), and thrombosis ORrs505922 1.33, p=2.2 x10-265. Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p=4.12 x 10-191; CRHR1 (rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p=2.26x 10-12. The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p=6.48 x10-23, lupus OR 0.84, p=3.97 x 10-06. PheWAS stratified by genetic ancestry demonstrated differences in genotype-phenotype associations across ancestry. LMNA (rs581342) associated with neutropenia OR 1.29 p=4.1 x 10-13 among Veterans of African ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets.

A population-level analysis of the protective effects of androgen deprivation therapy against COVID-19 disease incidence and severity (preprint)

ABSTRACT Importance: The incidence and severity of coronavirus disease 19 (COVID-19) is higher in men. Sex hormones potentially offer one explanation for differences by sex. Objective: To determine whether men exposed to androgen deprivation therapy (ADT) have lower incidence and severity of COVID-19. Design: We conducted an observational study of male Veterans treated in the Veterans Health Administration from February 15th to July 15th, 2020. We developed a propensity score model to predict the likelihood to undergo Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) testing. We performed multivariable logistic regression modeling adjusted with inverse probability weighting to examine the relationship between ADT and COVID-19 incidence. We conducted logistic regression analysis among COVID-19 patients to test the association between ADT and COVID-19 severity. Setting: The U.S. Department of Veterans Affairs Participants: The study sample consisted of 6,250,417 male Veterans who were alive as of February 15, 2020. Exposure: Exposure to ADT was defined as having any prescription for a luteinizing hormone releasing hormone analogue or an antiandrogen in the six months prior to the index date. Main Outcomes and Measures: To assess incidence, we used a binary variable indicating any positive reverse transcriptase polymerase chain reaction SARS-CoV-2 test result through July 15, 2020. To measure severity, we constructed a binary variable indicating whether a patient was admitted to the intensive care unit, placed on mechanical ventilation, or dead in the 60 days following a positive test up to July 15, 2020. Results: We identified 246,087 patients who had been tested for SARS-CoV-2, of whom 3,057 were exposed to ADT, and 36,096 patients with cancer and no ADT exposure. Of these, 295 ADT patients and 2,427 other cancer patients had COVID-19 illness. In the primary, propensity-weighted comparison of ADT patients to cancer patients not on ADT, ADT was associated with decreased likelihood of testing positive for SARS-CoV-2 (adjusted OR, 0.88 [95% CI, 0.81-0.95]; p=0.001). ADT was associated with fewer severe COVID-19 outcomes (OR 0.72 [95% CI 0.53-0.96]; p=0.03). Conclusions and Relevance: ADT is associated with reduced incidence and severity of COVID-19 amongst male Veterans. Repurposing of drugs that modulate androgen production and/or action may represent viable potential treatments for COVID-19.