A New Algorithm to Identify the Causality of Adverse Drug Reactions

Excerpt

Adverse drug reactions (ADRs) are recognised as a major contributor in iatrogenic illness. They are known to complicate existing disease, affect quality of life and may delay cure of the original disease.[1] The clinical, legal and financial consequences of ADRs have made reporting a major initiative for healthcare organisations,[2] and pharmacovigilance has become an integral part of governmental drug regulation.[3] However, for the regulatory authorities, assessing causality of a reported ADR unambiguously remains a major challenge. To assess causality, differential diagnosis of ADRs can usually be achieved with the use of clinical judgements and/or algorithms.[4] Algorithms are either flowcharts or questionnaires that attempt to determine drug causation in the occurrence of an ADR. They are preferred over clinical judgment for assigning ADR causalities because of their systematic approach to information acquisition, which helps to improve the reliability of assessments, as well as intra- and inter-rater agreement.[5,6] However, the problem of uncertainties pertaining to the causal involvement of the suspected drugs still remains because of the structure or data requirement of several commonly used algorithms. Therefore, a new or improved algorithm that can provide more consistent risk probability and differential diagnosis assessment of a plausible adverse drug event, but without the disadvantages of the existing algorithms, would be beneficial. …