A tailor-made approach for causality assessment for ADR reports on drugs and vaccines.

PURPOSE: To estimate causation of adverse drug reaction (ADR) reports, causality methods were developed from a theoretical perspective. In daily practice, not all information is relevant or available, decreasing the applicability. We developed a new causality documentation tool (CausDoc) where an algorithm is combined with expert judgement. The aim of this study is to test the validity and reliability of CausDoc for ADR reports on drugs and vaccines.

METHODS: CausDoc provides 9 structured relevant questions. If information is available, an answer will be chosen. If not, the question is excluded. Causality outcome is based on the sum score of all answers divided by the included questions: ≤30%: unlikely, 31% to 70%: possible, 71% to 90%: probable, and >90%: certain. Other relevant information is taken into account by expert judgement in the final step by adjusting the outcome to a limited extent. After testing face validity on 12 ADR reports, sensitivity and specificity were tested on 40 ADR reports, compared with the Naranjo algorithm and WHO AEFI criteria, using the expert panel's judgements as a standard. Inter-rater reliability was tested using weighted Cohen kappa coefficient.

RESULTS: Average sensitivity and specificity with CausDoc were 47% and 83% for drugs (29% and 78% with Naranjo) and 72% and 89% for vaccines (65% and 87% with WHO AEFI criteria). Reliability between the 2 couples of assessors: κ 0.48 and 0.75.

CONCLUSIONS: CausDoc shows a better performance and allows for a better documentation of ADRs in clinical practice. This approach is useful in assessing the causality of adverse drug reactions.