Using machine learning probabilities to identify effects of COVID-19

COVID-19, the disease caused by the SARS-CoV-2 virus, has had and continues to have extensive economic, social and public health impacts in the United States and around the world. To date, there have been more than 500 million reported cases of SARS-CoV-2 infection worldwide with more than 6 million reported deaths, more than 80 million of those cases and more than 1 million of those deaths have been reported in the United States. Retrospective analysis throughout the pandemic, which identified comorbidities, risk factors and treatments, has underpinned the response COVID-19. As the situation transitions from a pandemic to an endemic, retrospective analyses using electronic health records will be increasingly important to identify long term effects of COVID-19. However, these analyses can be complicated by the incompleteness of electronic health records, which in turns makes it difficult to differentiate visits where the patient has COVID-19. To address this, we trained a random forest classifier to assign a probability of a patient having been diagnosed with COVID-19 during each visit using demographic data, temporal data and visit-specific diagnoses (Training AUROC = 0.9867, Training OOB AUROC = 0.8957, Evaluation AUROC = 0.8958). Using these probabilities, we identified conditions associated with higher COVID-19 probabilities irrespective of clinical history and when accounting for previous diagnosis and estimated the hazards ratio for myocardial infarction (Hazards ratio = 121.736 (87.375, 169.611), p = 3.796E-177 and Hazards ratio = 80.262 (4.134, 4.637), p = 4.543E-256, respectively), urinary tract infection (Hazards ratio = 72.021 (58.116 - 89.253), p < 2.225E-308 and Hazards ratio = 61.380 (51.273 - 73.479), p < 2.225E-308, respectively), acute renal failure (Hazards ratio = 1.264E4 (9.278E4 - 1.724E4), p < 2.225E-308 and Hazards ratio = 6.333E3 (4.947E3 - 8.108E3), p < 2.225E-308, respectively) and type 2 diabetes (Hazards ratio = 345.730 (283.180 - 422.098), p < 2.225E-308 and Hazards ratio = 217.271 (187.898 - 251.235), p = 1.39E-22, respectively) when accounting for demographics and the ten most common clinical conditions.

Identification of Immune complement function as a determinant of adverse SARS-CoV-2 infection outcome

SO_SCPLOWUMMARYC_SCPLOWUnderstanding the pathophysiology of SARS-CoV-2 infection is critical for therapeutics and public health intervention strategies. Viral-host interactions can guide discovery of regulators of disease outcomes, and protein structure function analysis points to several immune pathways, including complement and coagulation, as targets of the coronavirus proteome. To determine if conditions associated with dysregulation of the complement or coagulation systems impact adverse clinical outcomes, we performed a retrospective observational study of 11,116 patients who presented with suspected SARS-CoV-2 infection. We found that history of macular degeneration (a proxy for complement activation disorders) and history of coagulation disorders (thrombocytopenia, thrombosis, and hemorrhage) are risk factors for morbidity and mortality in SARS-CoV-2 infected patients - effects that could not be explained by age, sex, or history of smoking. Further, transcriptional profiling of nasopharyngeal (NP) swabs from 650 control and SARS-CoV-2 infected patients demonstrated that in addition to innate Type-I interferon and IL-6 dependent inflammatory immune responses, infection results in robust engagement and activation of the complement and coagulation pathways. Finally, we conducted a candidate driven genetic association study of severe SARS-CoV-2 disease. Among the findings, our scan identified putative complement and coagulation associated loci including missense, eQTL and sQTL variants of critical regulators of the complement and coagulation cascades. In addition to providing evidence that complement function modulates SARS-CoV-2 infection outcome, the data point to putative transcriptional genetic markers of susceptibility. The results highlight the value of using a multi-modal analytical approach, combining molecular information from virus protein structure-function analysis with clinical informatics, transcriptomics, and genomics to reveal determinants and predictors of immunity, susceptibility, and clinical outcome associated with infection.

Testing the association between blood type and COVID-19 infection, intubation, and death

The rapid global spread of the novel coronavirus SARS-CoV-2 has strained healthcare and testing resources, making the identification and prioritization of individuals most at-risk a critical challenge. Recent evidence suggests blood type may affect risk of severe COVID-19. We used observational healthcare data on 14,112 individuals tested for SARS-CoV-2 with known blood type in the New York Presbyterian (NYP) hospital system to assess the association between ABO and Rh blood types and infection, intubation, and death. We found slightly increased infection prevalence among non-O types. Risk of intubation was decreased among A and increased among AB and B types, compared with type O, while risk of death was increased for type AB and decreased for types A and B. We estimated Rh-negative blood type to have a protective effect for all three outcomes. Our results add to the growing body of evidence suggesting blood type may play a role in COVID-19.

Host, Viral, and Environmental Transcriptome Profiles of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.