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Health & Medical

Ondansetron use associated with increased survival in mechanically ventilated COVID-19 patients


A recent study, published in the preprint server, medRxiv*, 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.

Study: Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis. Image Credit: Sonis Photography/ ShutterstockStudy: Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis. Image Credit: Sonis Photography/ Shutterstock

SARS-CoV-2 infection progresses to coronavirus disease 2019 (COVID-19). The researchers 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.

This study demonstrates how data from the real world can provide valuable observations to help decision-making in the clinical setting.

Background

To date, the SARS-CoV-2 has infected over 239 million individuals and caused over 4.87 million deaths. Despite mitigation interventions to control this COVID-19 pandemic and vaccination efforts to save lives, the threat of further infection persists with the emerging viral variants.

Because of the increased incidence of breakthrough infections, especially in the younger population, and the evolving understanding of infection, therapeutics, and re-emergence, there is a strong impetus for continued investigation of real-world data (RWD).

To this end, 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.

Recently, AI has been 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.

Using a large computational power, the researchers in the current study 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. They 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 researchers combined many COVID-19-focused RWD from AdventHealth, analyzed on a supercomputer at Oak Ridge National Laboratory (ORNL).

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 researchers reported that this is the first study to use Bayesian network analysis of clinical data to report disease outcomes in COVID-19 patients.

For this study, the researchers 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 key 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 researchers presented their analysis-approach by illustrating the linkage between ondansetron use and mortality. Interestingly, the researchers 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.

Notably, the study 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.

They tested the interaction of ondansetron use with other variables that are found to predict death. Significantly, they found that the beneficial effects of ondansetron use on mortality are specifically seen only in patients on the mechanical ventilator.

Further, this study confirmed the beneficial effects of the drug tocilizumab. It validated some established factors associated with COVID-19 that increased mortality, such as higher blood urea nitrogen (BUN), C-reactive peptide (CRP), ferritin, and D-dimer levels.

This study 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.

Interestingly, in the case of convalescent plasma, the researchers 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 researchers 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.

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 researchers.

Conclusion

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. The study also establishes other beneficial effects of tocilizumab.

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.

Currently, as no clinical trials are examining the effect of the FDA-approved drug, ondansetron, in COVID-19 patients, the researchers from this study recommend an investigation into this for its potential effectiveness against COVID-19.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:

  • Miller, G. et al. (2021) “Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis”. medRxiv. doi: 10.1101/2021.10.05.21264578.



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