Clinical Risk Mitigation Strategies in Device Trials

Abstract

Medical device clinical trials carry unique risks absent from pharmaceutical trials: operator learning curves create early-experience adverse events, device malfunctions during procedures expose patients to immediate harm, and the impossibility of blinding in most device trials introduces performance and detection biases that complicate safety signal interpretation. Effective clinical risk mitigation requires strategies that address these device-specific hazards while maintaining trial integrity and participant safety. This study presents the Clinical Risk Mitigation Assessment Framework for Device Trials (CRMAFDT), evaluating five mitigation strategies -- structured operator credentialing with proctoring, independent clinical event adjudication committees, real-time safety monitoring with adaptive stopping rules, simulation-based procedural training, and AI-driven patient risk prediction with enrolment optimisation -- across four device trial risk domains: procedural safety, device performance reliability, adverse event detection accuracy, and participant selection appropriateness. Our Risk Mitigation Effectiveness Score (RMETS) integrates procedural complication reduction, device malfunction management, AE detection completeness, participant risk-benefit optimisation, and trial integrity preservation. AI-driven patient risk prediction achieved the highest RMETS (0.922) through ML models that identified high-risk patient-device combinations before enrolment, reducing serious adverse events by 34% through personalised inclusion/exclusion criteria, while simulation-based training achieved the highest procedural safety score (0.950) by reducing operator learning curve-related complications by 52% through mandatory pre-trial simulation proficiency certification