Causality assessment of adverse drug reactions: comparison of the results obtained from published decisional algorithms and from the evaluations of an expert panel, according to different levels of imputability.
OBJECTIVE
To evaluate agreement between causality assessments of reported adverse drug reactions (ADRs) obtained from decisional algorithms, with those obtained from an expert panel using the WHO global introspection method (GI), according to different levels of imputability and to evaluate the influence of confounding variables.


METHODS
Two hundred reports were included in this study. An independent researcher used decisional algorithms, while an expert panel assessed the same ADR reports using the GI, both aimed at evaluating causality. Reports were divided according to the presence, absence or lack of information on confounding variables.


RESULTS
The rates of concordance between assessments made using the algorithms and GI according to levels of imputability were: 45% for 'certain', 61% for 'probable', 46% for 'possible' and 17% for drug unrelated terms. When confounding variables were taken into account, the rates of concordance for the 'absence of information', 'lack of information' and 'presence of confounding variables' in the 'certain' group were 49, 69 and 7%, respectively. The corresponding values for the 'probable' group were 80, 68 and 24% and 30, 51 and 51%, respectively for the 'possible' group.


CONCLUSIONS
Full agreement with global introspection was not found for any level of causality assessment. Confounding variables were found to be associated with low levels of agreement between decision algorithms and the GI method compromising the algorithms' sensitivity and specificity.
Macedo
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AF
Núcleo de Farmacovigilância do Centro, Faculdade de Medicina, Faculdade de Farmácia, Universidade de Coimbra e Administração Regional de Saúde do Centro, Portugal. farmacovigilancia@nfc.pt
Marques
F B
FB
Ribeiro
C F
CF
Teixeira
F
F
D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards / Q000706:statistics & numerical data; D000465:Algorithms; D015986:Confounding Factors, Epidemiologic; D006801:Humans; D015203:Reproducibility of Results