ABSTRACT
ObjectiveThe COVID-19 pandemic generated a massive amount of clinical data, which potentially holds yet undiscovered answers related to COVID-19 morbidity, mortality, long term effects, and therapeutic solutions. The objective of this study was to generate insights on COVID-19 mortality-associated factors and identify potential new therapeutic options for COVID-19 patients by employing artificial intelligence analytics on real-world data. MethodsA Bayesian statistics-based artificial intelligence data analytics tool (bAIcis(R)) within Interrogative Biology(R) platform was used for network learning, inference causality and hypothesis generation to analyze 16,277 PCR positive patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated causal networks that enabled unbiased identification of significant predictors of mortality for specific COVID-19 patient populations. These findings were validated by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping. ResultsWe found that in the SARS-CoV-2 PCR positive patient cohort, early use of the antiemetic agent ondansetron was associated with increased survival in mechanically ventilated patients. ConclusionsThe results demonstrate how real world COVID-19 focused data analysis using artificial intelligence can generate valid insights that could possibly support clinical decision-making and minimize the future loss of lives and resources.
ABSTRACT
ImportanceThe ACE D allele is more prevalent among African Americans (AA) compared to other races/ethnicities and has previously been associated with severe COVID-19 pathogenesis through excessive ACE1 activity. ACE-I/ARBs may counteract this mechanism, but their association with COVID-19 outcomes has not been specifically tested in the AA population. ObjectivesTo determine whether the use of ACE-I/ARBs is associated with COVID-19 in-hospital mortality in AA compared with non-AA population. Design, Setting, and ParticipantsIn this observational, retrospective study, patient-level data were extracted from the Mount Sinai Health Systems (MSHS) electronic medical record (EMR) database, and 6,218 patients with a laboratory-confirmed COVID-19 diagnosis from February 24 to May 31, 2020 were identified as ACE-I/ARB users. ExposuresPatients with an active prescription from January 1, 2019 up to the date of admission for ACE-I/ARB (outpatient use) and patients administered ACE-I/ARB during hospitalization (in-hospital use) were identified. Main Outcomes and MeasuresThe primary outcome was in-hospital mortality, assessed in the entire, AA, and non-AA population. ResultsOf the 6,218 COVID-19 patients, 1,138 (18.3%) were ACE-I/ARB users. In a multivariate logistic regression model, ACE-I/ARB use was independently associated with reduced risk of in-hospital mortality in the entire population (OR, 0.655; 95% CI, 0.505-0.850; P=0.001), AA population (OR, 0.44; 95% CI, 0.249-0.779; P=0.005), and non-AA population (OR, 0.748, 95% CI, 0.553-1.012, P=0.06). In the AA population, in-hospital use of ACE-I/ARBs was associated with improved mortality (OR, 0.378; 95% CI, 0.188-0.766; P=0.006) while outpatient use was not (OR, 0.889; 95% CI, 0.375-2.158; P=0.812). When analyzing each medication class separately, ARB in-hospital use was significantly associated with reduced in-hospital mortality in the AA population (OR, 0.196; 95% CI, 0.074-0.516; P=0.001), while ACE-I use was not associated with impact on mortality in any population. Conclusion and RelevanceIn-hospital use of ARBs was associated with a significant reduction in in-hospital mortality among COVID-19-positive AA patients. These results support further investigation of ARBs to improve outcomes in AA patients at high risk for COVID-19-related mortality.
