Prospective adverse event risk evaluation in clinical trials

Area of Science:

• Clinical trial methodology
• Regulatory science
• Predictive analytics in healthcare

Background:

• Regulatory risk management for clinical trials, particularly concerning safety, is often reactive.
• Existing frameworks for predicting trial risks may require extensive, context-specific data.

Purpose of the Study:

• To prospectively evaluate the risk of adverse outcomes using standardized, routinely collected clinical trial protocol data.
• To develop predictive models for identifying trials at risk of adverse events before enrollment.

Main Methods:

• Retrospective cohort study of 2860 Phase 2 and 3 trials from ClinicalTrials.gov (1993-2017).
• Development of random-forest-based prediction models using pre-enrollment protocol data.
• Evaluation of adverse outcomes including Serious and Non-Serious events.

Main Results:

• Classification models achieved an Area Under the Curve (AUC) from 0.865 to 0.971.
• Continuous-score models showed significant rank correlation (0.6-0.66, p < 0.001) with actual outcomes.
• Adverse Event (AE) risks were reliably predicted with minimal data requirements.

Conclusions:

• Routine protocol data can effectively predict clinical trial adverse outcomes.
• The proposed framework supports proactive regulatory oversight and enhances informed consent processes.
• This approach offers a scalable solution for improving clinical trial safety and management.