Prospective adverse event risk evaluation in clinical trials

Abhishake Kundu, Felipe Feijoo, Diego A. Martinez, Manuel Hermosilla, Timothy Matis

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalHealth Care Management Science
DOIs
StateAccepted/In press - 2021

Keywords

  • Adverse event risk
  • Clinical trials
  • Drug regulation
  • Machine learning

Fingerprint

Dive into the research topics of 'Prospective adverse event risk evaluation in clinical trials'. Together they form a unique fingerprint.

Cite this