Imputation of adverse drug reactions: Causality assessment in hospitals
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
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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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 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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PLoS ONE 1,164 |
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info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
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1808128793549733888 |