Prospective adverse event risk evaluation in clinical trials.
Adverse event risk
Clinical trials
Drug regulation
Machine learning
Journal
Health care management science
ISSN: 1572-9389
Titre abrégé: Health Care Manag Sci
Pays: Netherlands
ID NLM: 9815649
Informations de publication
Date de publication:
Mar 2022
Mar 2022
Historique:
received:
30
09
2020
accepted:
10
09
2021
pubmed:
25
9
2021
medline:
8
4
2022
entrez:
24
9
2021
Statut:
ppublish
Résumé
Proactive and objective regulatory risk management of ongoing clinical trials is limited, especially when it involves the safety of the trial. We seek to prospectively evaluate the risk of facing adverse outcomes from standardized and routinely collected protocol data. We conducted a retrospective cohort study of 2860 Phase 2 and Phase 3 trials that were started and completed between 1993 and 2017 and documented in ClinicalTrials.gov. Adverse outcomes considered in our work include Serious or Non-Serious as per the ClinicalTrials.gov definition. Random-forest-based prediction models were created to determine a trial's risk of adverse outcomes based on protocol data that is available before the start of a trial enrollment. A trial's risk is defined by dichotomic (classification) and continuous (log-odds) risk scores. The classification-based prediction models had an area under the curve (AUC) ranging from 0.865 to 0.971 and the continuous-score based models indicate a rank correlation of 0.6-0.66 (with p-values < 0.001), thereby demonstrating improved identification of risk of adverse outcomes. Whereas related frameworks highlight the prediction benefits of incorporating data that is highly context-specific, our results indicate that Adverse Event (AE) risks can be reliably predicted through a framework of mild data requirements. We propose three potential applications in leading regulatory remits, highlighting opportunities to support regulatory oversight and informed consent decisions.
Identifiants
pubmed: 34559339
doi: 10.1007/s10729-021-09584-y
pii: 10.1007/s10729-021-09584-y
doi:
Types de publication
Clinical Trial, Phase II
Clinical Trial, Phase III
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
89-99Subventions
Organisme : Comisión Nacional de Investigación Científica y Tecnológica (CL)
ID : mec 80180112
Organisme : Comisión Nacional de Investigación Científica y Tecnológica
ID : MEC 80180112
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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