New American Study Shows That The Antiemetic Drug Zofran Lowers Risk Of Mortality Of Severe COVID-19 Patients On Ventilators
: A new study that analyzes real-world data utilizing artificial intelligence by researchers from AdventHealth Research Institute-Orlando, Oak Ridge National Laboratory-Tennessee and the University of Tennessee has found that the antiemetic drug Zofran (Ondansetron) lowers risk of mortality of severe COVID-19 patients on ventilators and is associated with increased survival of these mechanically ventilated COVID-19 patients.
Ondansetron, sold under the brand name Zofran among others, is a medication used to prevent nausea and vomiting caused by cancer chemotherapy, radiation therapy, or surgery. Ondansetron is in a class of medications called serotonin 5-HT3 receptor antagonists. It works by blocking the action of serotonin, a natural substance that may cause nausea and vomiting. It is also effective for treating gastroenteritis. However, it is ineffective for treating vomiting caused by motion sickness.
There are many cheaper generic versions of Zofran sold under the names Dantron 8, (Not Dantron the carcinogenic drug) Emeset, Emistop, Onsia and Zetron.
For the study, 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 novel 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.
The study team 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.
The study findings
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.
The study findings were published on a preprint server and are currently being peer reviewed. https://www.medrxiv.org/content/10.1101/2021.10.05.21264578v1
This study analyzes real-world data using artificial intelligence to provide insights into using ondansetron, an antiemetic agent, among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive patients. SARS-CoV-2 infection typically progresses to coronavirus disease 2019 (COVID-19).
The study team found that these patients who used ondansetron were at a lower risk for mortality. The mechanically ventilated COVID-19 patients had increased survival rates with ondansetron use.
The COVID-19 Drugs
study demonstrates how data from the real world can provide valuable observations to h
elp decision-making in the clinical setting.
The current COVID-19 pandemic has in the last 22 months infected more than 240 million individuals and caused more than 4.88 million COVID-19 deaths and these numbers are expected to escalate in coming weeks and months despite a mass vaccination program globally as newer more potent and immune evasive SARS-CoV-2 variants are emerging constantly.
The increased incidence of breakthrough infections, especially in the younger population, and the evolving understanding of infection, therapeutics, and re-emergence is driving a strong impetus for continued investigation of real-world data (RWD).
It has been found that artificial intelligence and machine learning (AI/ML) with high-performance computing abilities achieve detailed analytics of large-population-based databases, facilitating a deep understanding of issues and identifying novel information. With this tool applied in pertinent to diseases, possible therapeutic solutions, support to clinical decision-making, and reduced loss of lives can be realized.
Artificial intelligence or AI has been recently extensively applied to analyze various COVID-19-related RWD. Few examples where RWD AI/ML helped include: 1) to predict the probability of ARDS (acute respiratory distress syndrome) based on the clinical symptoms of COVID-19 patients, and 2) to predict hospitalization of COVID-19 patients using medical records at the time of RT-PCR testing.
Utilizing a large computational power, the study team in the current research used a Bayesian statistics-driven platform to find causal relationships for disease outcomes and identify those variables likely to have a causal association with mortality.
The study team used a Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within Interrogative Biology® platform for network learning, inference causality, and hypothesis generation to analyze the data during the pandemic year in Central Florida.
The study team combined many COVID-19-focused RWD from AdventHealth, analyzed on a supercomputer at Oak Ridge National Laboratory (ORNL).
Importantly this enabled them to identify factors associated with disease severity, increased survival, and mortality, including drugs that can improve outcomes in COVID-19 patients. The study reported that this is the first study to use Bayesian network analysis of clinical data to report disease outcomes in COVID-19 patients.
The study team selected only the 16,277 patients' data found positive by PCR (due to higher confidence in this method) from the RECOVER-19, a registry of all patients tested for SARS-CoV-2 within the AdventHealth Enterprise. The registry included 279,281 inpatients and outpatients tested from January to December 2020 and continues to do presently.
A significant finding of this study is that the use of ondansetron, a widely used antiemetic medication, is associated with improved survival in mechanically ventilated COVID-19 patients. Antiemetic drugs are used against vomiting and nausea.
The study team presented their analysis-approach by illustrating the linkage between ondansetron use and mortality.
The study team also added that an initial unbiased search for predictors of mortality at any time and within any patient population found the ondansetron as the only medication associated with decreased mortality.
Importantly, the study findings showed that the 'ondansetron use on mortality' effect equally applies to all age groups. However, the benefit is seen as significant only in patients on a ventilator.
The study team tested the interaction of ondansetron use with other variables that are found to predict death. Significantly, the team found that the beneficial effects of ondansetron use on mortality are specifically seen only in patients on the mechanical ventilator.
The study findings also showed a negative association between neoplastic disease and mortality, possibly due to an indirect ondansetron effect in cancer patients prescribed ondansetron during their treatment, which may involve chemotherapy, radiation therapy, and/or surgery.
In the case of convalescent plasma, the study team found that it is not a significant predictor of death. Instead, patients on ondansetron and convalescent plasma were more likely to die. They suggested that this reflects a complex interaction between ondansetron, ventilator use, and convalescent plasma.
The study team found that remdesivir, an FDA-approved drug for COVID-19, not increasing survival in large, randomized control trials. However, they find that age and remdesivir use interact to increase mortality. Therefore, this observation may be a similar confounding bias seen with convalescent plasma since remdesivir was reserved for more severe patients earlier on, just as convalescent plasma.
Interestingly another feature with a significant relationship with decreased mortality in the study is the 'Diagnostic code Z20.828' - i.e., contact with and suspected exposure to other viral communicable diseases.
The researchers observed that out of the approximately 200,000 unique patients seen with this diagnostic code in 2020, about 16,000 were found to be positive. This mortality benefit maybe because the SARS-CoV-2 positive patients with the Z20.828 code might have had a less severe form of COVID-19 or arrived earlier in the course of the disease and were designated patients under investigation (PUI), benefiting from early precautions, explained the study team.
The study team concluded, “Artificial intelligence or AI plays a major role in COVID-19 related decision-making. This study finds that Ondansetron use is associated with lower COVID-19-related mortality. It further validates some already established factors associated with COVID-19 increased mortality, such as higher BUN, CRP, ferritin, and D-dimer levels. The results from this study confirm the validity of our approach and the hypothesis-generating potential of the bAIcis® platform.”
The study team is calling for clinical trials examining the effect of the FDA-approved drug, ondansetron in COVID-19 patients to validate its potential effectiveness against COVID-19.
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