MOdified NARanjo Causality Scale for ICSRs (MONARCSi): A Decision Support Tool for Safety Scientists.

INTRODUCTION

Within the field of Pharmacovigilance, the most common approaches for assessing causality between a report of a drug and a corresponding adverse event are clinical judgment, probabilistic methods and algorithms. Although multiple methods using these three approaches have been proposed, there is currently no universally accepted method for assessing drug-event causality in ICSRs and variability in drug-event causality assessments is well documented.

OBJECTIVE

This study describes the development and validation of an Individual Case Safety Report (ICSR) Causality Decision Support Tool to assist Safety Professionals (SPs) performing causality assessments.

METHODS

Roche developed this model with nine drug-event pair features capturing important aspects of Naranjo's scoring system, selected Bradford-Hill criteria, and internal Roche safety practices. Each of the features was weighted based on individual safety professional (n = 65) assessments of the importance of that feature when assessing causality, using an ordinal weighting scale (0 = no importance, 4 = very high importance). The mean and associated standard deviation for each feature weight was calculated and were used as inputs to a fitted logistic equation, which calculated the probability of a causal relationship between the drug and adverse event. Model training, validation, and testing were conducted by comparing MONARCSi causality classifications to previous company causality assessments for 978 randomly selected, clinical trial drug-event pairs based on their respective features and weights.

RESULTS

The final model test, a two-by-two comparison of the results, showed substantial agreement (Gwet Kappa = 0.77) between MONARCSi and Roche safety professionals' assessments of causality, using global introspection. The model exhibited moderate sensitivity (65%) and high specificity (93%), high positive and negative predictive values (79 and 88%, respectively), and an F score of 71%.

CONCLUSION

Analysis suggests that the MONARCSi model could potentially be a useful decision support tool to assist pharmacovigilance safety professionals when evaluating drug-event causality in a consistent and documentable manner.