Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Objective
The 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.
Methods
A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within Interrogative Biology® 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.
Results
We 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.
Conclusions
The 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.
Article activity feed
-
-
SciScore for 10.1101/2021.10.05.21264578: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics IRB: This study was approved by the AdventHealth Institutional Review Board (#1590483). Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis To increase the statistical power, imputation of missing values was accomplished by a multiple imputation approach using the predictive mean matching method [9], as implemented by the R package mice [10]. Table 2: Resources
Antibodies Sentences Resources Cohort selection: The RECOVER-19 registry, which is continuing to collect data, included 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods from January to December 2020. antigen ,suggested: NoneResults from …
SciScore for 10.1101/2021.10.05.21264578: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics IRB: This study was approved by the AdventHealth Institutional Review Board (#1590483). Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis To increase the statistical power, imputation of missing values was accomplished by a multiple imputation approach using the predictive mean matching method [9], as implemented by the R package mice [10]. Table 2: Resources
Antibodies Sentences Resources Cohort selection: The RECOVER-19 registry, which is continuing to collect data, included 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods from January to December 2020. antigen ,suggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-