Evidence map›Paper›PMID 28166274›Full record

ArticlePloS one2017

Imputation of adverse drug reactions: Causality assessment in hospitals.

Fabiana Rossi Varallo, Cleopatra S Planeta, Maria Teresa Herdeiro, Patricia de Carvalho Mastroianni

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In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Article
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  6. Observational
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  20. New Insights in Computational Methods for Pharmacovigilance:International journal of environmental research and public health · 2019
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Fabiana Rossi VaralloSão Paulo State University (UNESP), School of Pharmaceutical Sciences, Araraquara, São Paulo, Brazil.
Cleopatra S PlanetaSão Paulo State University (UNESP), School of Pharmaceutical Sciences, Araraquara, São Paulo, Brazil.
Maria Teresa HerdeiroDepartamento de Ciências Médicas-Universidade de Aveiro, Aveiro, Portugal.ORCID http://orcid.org/0000-0002-0500-4049
Patricia de Carvalho MastroianniSão Paulo State University (UNESP), School of Pharmaceutical Sciences, Araraquara, São Paulo, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND &

objectivesDifferent 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.

methodsUsing 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.

resultsNone 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). INTERPRETATION &

conclusionsAlthough 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.

Indexed as

HospitalsModels, StatisticalAdverse Drug Reaction Reporting SystemsAlgorithmsCausalityHumansReproducibility of Results

Identifiers

PMID28166274
PMCPMC5293251

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.