Imputation of adverse drug reactions: Causality assessment in hospitals

Detalhes bibliográficos
Autor(a) principal: Varallo, Fabiana Rossi [UNESP]
Data de Publicação: 2017
Outros Autores: Planeta, Cleopatra S. [UNESP], Herdeiro, Maria Teresa, De Mastroianni, Patricia Carvalho [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1371/journal.pone.0171470
http://hdl.handle.net/11449/169432
Resumo: Background & objectives 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 interrater 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 & 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.
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spelling Imputation of adverse drug reactions: Causality assessment in hospitalsBackground & objectives 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 interrater 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 & 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.Fundação para a Ciência e a TecnologiaCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)São Paulo State University (UNESP) School of Pharmaceutical SciencesCAPES Foundation Ministry of Education of BrazilDepartamento de Ciências Médicas Universidade de AveiroSão Paulo State University (UNESP) School of Pharmaceutical SciencesFundação para a Ciência e a Tecnologia: UID/BIM/04501/2013Universidade Estadual Paulista (Unesp)Ministry of Education of BrazilUniversidade de AveiroVarallo, Fabiana Rossi [UNESP]Planeta, Cleopatra S. [UNESP]Herdeiro, Maria TeresaDe Mastroianni, Patricia Carvalho [UNESP]2018-12-11T16:45:52Z2018-12-11T16:45:52Z2017-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1371/journal.pone.0171470PLoS ONE, v. 12, n. 2, 2017.1932-6203http://hdl.handle.net/11449/16943210.1371/journal.pone.01714702-s2.0-850116606952-s2.0-85011660695.pdf25147625452809420000-0002-1378-6327Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPLoS ONE1,164info:eu-repo/semantics/openAccess2023-11-10T06:14:30Zoai:repositorio.unesp.br:11449/169432Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:20:20.624607Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Imputation of adverse drug reactions: Causality assessment in hospitals
title Imputation of adverse drug reactions: Causality assessment in hospitals
spellingShingle Imputation of adverse drug reactions: Causality assessment in hospitals
Varallo, Fabiana Rossi [UNESP]
title_short Imputation of adverse drug reactions: Causality assessment in hospitals
title_full Imputation of adverse drug reactions: Causality assessment in hospitals
title_fullStr Imputation of adverse drug reactions: Causality assessment in hospitals
title_full_unstemmed Imputation of adverse drug reactions: Causality assessment in hospitals
title_sort Imputation of adverse drug reactions: Causality assessment in hospitals
author Varallo, Fabiana Rossi [UNESP]
author_facet Varallo, Fabiana Rossi [UNESP]
Planeta, Cleopatra S. [UNESP]
Herdeiro, Maria Teresa
De Mastroianni, Patricia Carvalho [UNESP]
author_role author
author2 Planeta, Cleopatra S. [UNESP]
Herdeiro, Maria Teresa
De Mastroianni, Patricia Carvalho [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Ministry of Education of Brazil
Universidade de Aveiro
dc.contributor.author.fl_str_mv Varallo, Fabiana Rossi [UNESP]
Planeta, Cleopatra S. [UNESP]
Herdeiro, Maria Teresa
De Mastroianni, Patricia Carvalho [UNESP]
description Background & objectives 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 interrater 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 & 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.
publishDate 2017
dc.date.none.fl_str_mv 2017-02-01
2018-12-11T16:45:52Z
2018-12-11T16:45:52Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1371/journal.pone.0171470
PLoS ONE, v. 12, n. 2, 2017.
1932-6203
http://hdl.handle.net/11449/169432
10.1371/journal.pone.0171470
2-s2.0-85011660695
2-s2.0-85011660695.pdf
2514762545280942
0000-0002-1378-6327
url http://dx.doi.org/10.1371/journal.pone.0171470
http://hdl.handle.net/11449/169432
identifier_str_mv PLoS ONE, v. 12, n. 2, 2017.
1932-6203
10.1371/journal.pone.0171470
2-s2.0-85011660695
2-s2.0-85011660695.pdf
2514762545280942
0000-0002-1378-6327
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instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
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reponame_str Repositório Institucional da UNESP
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repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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