Evidence map›Paper›PMID 35809196›Full record

ArticleDrugs - real world outcomes2022

Hypothesis-Agnostic Network-Based Analysis of Real-World Data Suggests Ondansetron is Associated with Lower COVID-19 Any Cause Mortality.

Gregory M Miller, J Austin Ellis, Rangaprasad Sarangarajan, Amay Parikh, Leonardo O Rodrigues, Can Bruce, Nischal Mahaveer Chand, Steven R Smith, Kris Richardson, Raymond Vazquez and 9 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Drugs - real world outcomes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 50% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors at 4 institutions in 1 country.

Gregory M MillerBERG, Framingham, MA, 01701, USA.
J Austin EllisNational Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.
Rangaprasad SarangarajanBERG, Framingham, MA, 01701, USA.
Amay ParikhTranslational Research Institute, AdventHealth Research Institute, 301 E Princeton St, Orlando, FL, 32804, USA.
Leonardo O RodriguesBERG, Framingham, MA, 01701, USA.
Can BruceBERG, Framingham, MA, 01701, USA.
Nischal Mahaveer ChandBERG, Framingham, MA, 01701, USA.
Steven R SmithTranslational Research Institute, AdventHealth Research Institute, 301 E Princeton St, Orlando, FL, 32804, USA.
Kris RichardsonBERG, Framingham, MA, 01701, USA.
Raymond VazquezAdventHealth Information Technology, Orlando, FL, 32804, USA.
Michael A KiebishBERG, Framingham, MA, 01701, USA.
Chandran HaneeshTranslational Research Institute, AdventHealth Research Institute, 301 E Princeton St, Orlando, FL, 32804, USA.
Elder GrangerBERG, Framingham, MA, 01701, USA.
Judy HoltzTranslational Research Institute, AdventHealth Research Institute, 301 E Princeton St, Orlando, FL, 32804, USA.
Jacob HinkleComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.
Niven R NarainBERG, Framingham, MA, 01701, USA.
Bret GoodpasterTranslational Research Institute, AdventHealth Research Institute, 301 E Princeton St, Orlando, FL, 32804, USA.
Jeremy C SmithDepartment of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN, 37996, USA.
Daniel S LupuTranslational Research Institute, AdventHealth Research Institute, 301 E Princeton St, Orlando, FL, 32804, USA. Daniel.Lupu@adventhealth.com.ORCID http://orcid.org/0000-0001-8174-5482
Framingham State University · USTranslational Research Institute for Metabolism and Diabetes · USOak Ridge National Laboratory · USAdventHealth Orlando · US

Funding

Office of Science of the U.S. Department of Energy DE-AC05-00OR22725
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic generated a massive amount of clinical data, which potentially hold yet undiscovered answers related to COVID-19 morbidity, mortality, long-term effects, and therapeutic solutions.

objectivesThe objectives of this study were (1) to identify novel predictors of COVID-19 any cause mortality by employing artificial intelligence analytics on real-world data through a hypothesis-agnostic approach and (2) to determine if these effects are maintained after adjusting for potential confounders and to what degree they are moderated by other variables.

methodsA Bayesian statistics-based artificial intelligence data analytics tool (bAIcis

resultsWe found that in the COVID-19 PCR+ patient cohort, early use of the antiemetic agent ondansetron was associated with decreased any cause mortality 30 days post-PCR+ testing in mechanically ventilated patients.

conclusionsThe results demonstrate how a real-world COVID-19-focused data analysis using artificial intelligence can generate unexpected yet valid insights that could possibly support clinical decision making and minimize the future loss of lives and resources.

Identifiers

PMID35809196
PMCPMC9281575
OpenAlexW4284991089

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.