The FMB scale: a multifactorial metric to assess the driving hazard of medicines beyond the DRUID system

Background: Driving while undergoing pharmacological treatment poses a significant risk to road safety. The Driving under the Influence of Drugs, Alcohol, and Medicines (DRUID) system, currently used to classify medicines according to their impact on driving ability, has important limitations, including the absence of classification for numerous drugs, low reproducibility, and limited clinical applicability. Objectives: To develop a continuous, multifactorial metric capable of refining the estimation of medication-related driving risk and to assess its preliminary performance compared with the traditional DRUID system. Design: Methodological development and initial validation study. Methods: In this study, we propose a new multifactorial risk scale, validated by healthcare professionals and engineers, which integrates key pharmacological and clinical variables. The scale combines six weighted criteria: DRUID category, frequency and severity of adverse reactions, number of driving-related adverse reactions, marketed dose, treatment initiation versus chronic treatment, and pharmaceutical dosage form. Each variable was normalized to a 0–1 scale to ensure comparability. In addition, correction mechanisms were introduced to avoid bias arising from the presence of multiple adverse reactions with unknown frequencies, ensuring robustness to incomplete data. Results: When applied to different clinically used medicines, the scale showed greater sensitivity and accuracy in discriminating risk compared with the traditional DRUID system, reproducing its qualitative categorization while providing finer intraclass resolution, particularly for medicines situated near risk thresholds. Conclusion: This tool offers a more flexible, reproducible, and clinically applicable approach with the potential for integration into e-prescribing, mobile health applications, and community pharmacy support systems, supporting more nuanced and evidence-based clinical decision-making.