Imputation of adverse drug reactions: Causality assessment in hospitals.
OBJECTIVE
Different algorithms have been developed to standardize the causality assessment of adverse drug reactions (ADR). Although most share common characteristics, the results of the causality assessment are variable depending on the algorithm used. Therefore, using 10 different algorithms, the study aimed to compare inter-rater and multi-rater agreement for ADR causality assessment and identify the most consistent to hospitals.


METHODS
Using ten causality algorithms, four judges independently assessed the first 44 cases of ADRs reported during the first year of implementation of a risk management service in a medium complexity hospital in the state of Sao Paulo (Brazil). Owing to variations in the terminology used for causality, the equivalent imputation terms were grouped into four categories: definite, probable, possible and unlikely. Inter-rater and multi-rater agreement analysis was performed by calculating the Cohen´s and Light´s kappa coefficients, respectively.


RESULTS
None of the algorithms showed 100% reproducibility in the causal imputation. Fair inter-rater and multi-rater agreement was found. Emanuele (1984) and WHO-UMC (2010) algorithms showed a fair rate of agreement between the judges (k = 0.36).


CONCLUSIONS
Although the ADR causality assessment algorithms were poorly reproducible, our data suggest that WHO-UMC algorithm is the most consistent for imputation in hospitals, since it allows evaluating the quality of the report. However, to improve the ability of assessing the causality using algorithms, it is necessary to include criteria for the evaluation of drug-related problems, which may be related to confounding variables that underestimate the causal association.
São Paulo State University (UNESP), School of Pharmaceutical Sciences, Araraquara, São Paulo, Brazil.|CAPES Foundation, Ministry of Education of Brazil, Brasília-DF, Brazil.
Fabiana Rossi
FR
Varallo
São Paulo State University (UNESP), School of Pharmaceutical Sciences, Araraquara, São Paulo, Brazil.
Cleopatra S
CS
Planeta
Departamento de Ciências Médicas-Universidade de Aveiro, Aveiro, Portugal.
Maria Teresa
http://orcid.org/0000-0002-0500-4049
MT
Herdeiro
São Paulo State University (UNESP), School of Pharmaceutical Sciences, Araraquara, São Paulo, Brazil.
Patricia de Carvalho
PC
Mastroianni
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000465:Algorithms; D015984:Causality; D006761:Hospitals; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results