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Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility
INTRODUCTION: Spontaneous reporting of adverse events has increased steadily over the past decades, and although this trend has contributed to improving post-marketing surveillance pharmacovigilance activities, the consequent amount of data generated is challenging to manually review during assessme...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442257/ https://www.ncbi.nlm.nih.gov/pubmed/37535258 http://dx.doi.org/10.1007/s40264-023-01327-y |
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author | Kara, Vijay Powell, Greg Mahaux, Olivia Jayachandra, Aparna Nyako, Naashika Golds, Christopher Bate, Andrew |
author_facet | Kara, Vijay Powell, Greg Mahaux, Olivia Jayachandra, Aparna Nyako, Naashika Golds, Christopher Bate, Andrew |
author_sort | Kara, Vijay |
collection | PubMed |
description | INTRODUCTION: Spontaneous reporting of adverse events has increased steadily over the past decades, and although this trend has contributed to improving post-marketing surveillance pharmacovigilance activities, the consequent amount of data generated is challenging to manually review during assessment, with each individual report requiring review by pharmacovigilance experts. This highlights a clear need for alternative or complementary methodologies to help prioritise review. OBJECTIVE: Here, we aimed to develop and test an automated methodology, the Clinical Utility Score for Prioritisation (CUSP), to assist pharmacovigilance experts in prioritising clinical assessment of safety data to improve the rapidity of case series review when case volumes are large. METHODS: The CUSP method was tested on a reference dataset of individual case safety reports (ICSRs) associated to five drug-event pairs that led to labelling changes. The selected drug-event pairs were of varying characteristics across the portfolio of GSK’s products. RESULTS: The mean CUSP score for ‘key cases’ and ‘cases of low utility’ was 19.7 (median: 21; range: 7–27) and 17.3 (median: 19; range: 4–27), respectively. CUSP distribution for ‘key cases’ were skewed toward the higher range of scores compared with ‘all cases’. The overall performance across each individual drug-event pair varied considerably, showing higher predictive power for ‘key cases’ for three of the drug-event pairs (average CUSP between these three: 22.8; range: 22.5–23.0) and lesser power for the remaining two (average CUSP between these two: 17.6; range: 14.5–20.7). CONCLUSION: Although several tools have been developed to assess ICSR completeness and regulatory utility, this is the first attempt to successfully develop an automated clinical utility scoring system that can support the prioritisation of ICSRs for clinical review. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40264-023-01327-y. |
format | Online Article Text |
id | pubmed-10442257 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-104422572023-08-23 Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility Kara, Vijay Powell, Greg Mahaux, Olivia Jayachandra, Aparna Nyako, Naashika Golds, Christopher Bate, Andrew Drug Saf Original Research Article INTRODUCTION: Spontaneous reporting of adverse events has increased steadily over the past decades, and although this trend has contributed to improving post-marketing surveillance pharmacovigilance activities, the consequent amount of data generated is challenging to manually review during assessment, with each individual report requiring review by pharmacovigilance experts. This highlights a clear need for alternative or complementary methodologies to help prioritise review. OBJECTIVE: Here, we aimed to develop and test an automated methodology, the Clinical Utility Score for Prioritisation (CUSP), to assist pharmacovigilance experts in prioritising clinical assessment of safety data to improve the rapidity of case series review when case volumes are large. METHODS: The CUSP method was tested on a reference dataset of individual case safety reports (ICSRs) associated to five drug-event pairs that led to labelling changes. The selected drug-event pairs were of varying characteristics across the portfolio of GSK’s products. RESULTS: The mean CUSP score for ‘key cases’ and ‘cases of low utility’ was 19.7 (median: 21; range: 7–27) and 17.3 (median: 19; range: 4–27), respectively. CUSP distribution for ‘key cases’ were skewed toward the higher range of scores compared with ‘all cases’. The overall performance across each individual drug-event pair varied considerably, showing higher predictive power for ‘key cases’ for three of the drug-event pairs (average CUSP between these three: 22.8; range: 22.5–23.0) and lesser power for the remaining two (average CUSP between these two: 17.6; range: 14.5–20.7). CONCLUSION: Although several tools have been developed to assess ICSR completeness and regulatory utility, this is the first attempt to successfully develop an automated clinical utility scoring system that can support the prioritisation of ICSRs for clinical review. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40264-023-01327-y. Springer International Publishing 2023-08-03 2023 /pmc/articles/PMC10442257/ /pubmed/37535258 http://dx.doi.org/10.1007/s40264-023-01327-y Text en © GlaxoSmithKline Biologicals S.A 2023, corrected publication 2023 https://creativecommons.org/licenses/by-nc/4.0/Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Original Research Article Kara, Vijay Powell, Greg Mahaux, Olivia Jayachandra, Aparna Nyako, Naashika Golds, Christopher Bate, Andrew Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility |
title | Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility |
title_full | Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility |
title_fullStr | Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility |
title_full_unstemmed | Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility |
title_short | Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility |
title_sort | finding needles in the haystack: clinical utility score for prioritisation (cusp), an automated approach for identifying spontaneous reports with the highest clinical utility |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442257/ https://www.ncbi.nlm.nih.gov/pubmed/37535258 http://dx.doi.org/10.1007/s40264-023-01327-y |
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