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Validation of CORE-MD PMS Support Tool: A Novel Strategy for Aggregating Information from Notices of Failures to Support Medical Devices’ Post-Market Surveillance
INTRODUCTION: The EU Medical Device Regulation 2017/745 defines new rules for the certification and post-market surveillance of medical devices (MD), including an additional review by Expert Panels of clinical evaluation data for high-risk MD if reports and alerts suggest possibly associated increas...
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/PMC10133046/ https://www.ncbi.nlm.nih.gov/pubmed/36652105 http://dx.doi.org/10.1007/s43441-022-00493-y |
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author | Ren, Yijun Bertoldi, Michele Fraser, Alan G. Caiani, Enrico Gianluca |
author_facet | Ren, Yijun Bertoldi, Michele Fraser, Alan G. Caiani, Enrico Gianluca |
author_sort | Ren, Yijun |
collection | PubMed |
description | INTRODUCTION: The EU Medical Device Regulation 2017/745 defines new rules for the certification and post-market surveillance of medical devices (MD), including an additional review by Expert Panels of clinical evaluation data for high-risk MD if reports and alerts suggest possibly associated increased risks. Within the EU-funded CORE-MD project, our aim was to develop a tool to support such process in which web-accessible safety notices (SN) are automatically retrieved and aggregated based on their specific MD categories and the European Medical Device Nomenclature (EMDN) classification by applying an Entity Resolution (ER) approach to enrich data integrating different sources. The performance of such approach was tested through a pilot study on the Italian data. METHODS: Information relevant to 7622 SN from 2009 to 2021 was retrieved from the Italian Ministry of Health website by Web scraping. For incomplete EMDN data (68%), the MD best match was searched within a list of about 1.5 M MD on the Italian market, using Natural Language Processing techniques and pairwise ER. The performance of this approach was tested on the 2440 SN (32%) already provided with the EMDN code as reference standard. RESULTS: The implemented ER method was able to correctly assign the correct manufacturer to the MD in each SN in 99% of the cases. Moreover, the correct EMDN code at level 1 was assigned in 2382 SN (97.62%), at level 2 in 2366 SN (96.97%) and at level 3 in 2329 SN (95.45%). CONCLUSION: The proposed approach was able to cope with the incompleteness of the publicly available data in the SN. In this way, grouping of SN relevant to a specific MD category/group/type could be used as possible sentinel for increased rates in reported serious incidents in high-risk MD. |
format | Online Article Text |
id | pubmed-10133046 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-101330462023-04-28 Validation of CORE-MD PMS Support Tool: A Novel Strategy for Aggregating Information from Notices of Failures to Support Medical Devices’ Post-Market Surveillance Ren, Yijun Bertoldi, Michele Fraser, Alan G. Caiani, Enrico Gianluca Ther Innov Regul Sci Original Research INTRODUCTION: The EU Medical Device Regulation 2017/745 defines new rules for the certification and post-market surveillance of medical devices (MD), including an additional review by Expert Panels of clinical evaluation data for high-risk MD if reports and alerts suggest possibly associated increased risks. Within the EU-funded CORE-MD project, our aim was to develop a tool to support such process in which web-accessible safety notices (SN) are automatically retrieved and aggregated based on their specific MD categories and the European Medical Device Nomenclature (EMDN) classification by applying an Entity Resolution (ER) approach to enrich data integrating different sources. The performance of such approach was tested through a pilot study on the Italian data. METHODS: Information relevant to 7622 SN from 2009 to 2021 was retrieved from the Italian Ministry of Health website by Web scraping. For incomplete EMDN data (68%), the MD best match was searched within a list of about 1.5 M MD on the Italian market, using Natural Language Processing techniques and pairwise ER. The performance of this approach was tested on the 2440 SN (32%) already provided with the EMDN code as reference standard. RESULTS: The implemented ER method was able to correctly assign the correct manufacturer to the MD in each SN in 99% of the cases. Moreover, the correct EMDN code at level 1 was assigned in 2382 SN (97.62%), at level 2 in 2366 SN (96.97%) and at level 3 in 2329 SN (95.45%). CONCLUSION: The proposed approach was able to cope with the incompleteness of the publicly available data in the SN. In this way, grouping of SN relevant to a specific MD category/group/type could be used as possible sentinel for increased rates in reported serious incidents in high-risk MD. Springer International Publishing 2023-01-18 2023 /pmc/articles/PMC10133046/ /pubmed/36652105 http://dx.doi.org/10.1007/s43441-022-00493-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits 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/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Research Ren, Yijun Bertoldi, Michele Fraser, Alan G. Caiani, Enrico Gianluca Validation of CORE-MD PMS Support Tool: A Novel Strategy for Aggregating Information from Notices of Failures to Support Medical Devices’ Post-Market Surveillance |
title | Validation of CORE-MD PMS Support Tool: A Novel Strategy for Aggregating Information from Notices of Failures to Support Medical Devices’ Post-Market Surveillance |
title_full | Validation of CORE-MD PMS Support Tool: A Novel Strategy for Aggregating Information from Notices of Failures to Support Medical Devices’ Post-Market Surveillance |
title_fullStr | Validation of CORE-MD PMS Support Tool: A Novel Strategy for Aggregating Information from Notices of Failures to Support Medical Devices’ Post-Market Surveillance |
title_full_unstemmed | Validation of CORE-MD PMS Support Tool: A Novel Strategy for Aggregating Information from Notices of Failures to Support Medical Devices’ Post-Market Surveillance |
title_short | Validation of CORE-MD PMS Support Tool: A Novel Strategy for Aggregating Information from Notices of Failures to Support Medical Devices’ Post-Market Surveillance |
title_sort | validation of core-md pms support tool: a novel strategy for aggregating information from notices of failures to support medical devices’ post-market surveillance |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10133046/ https://www.ncbi.nlm.nih.gov/pubmed/36652105 http://dx.doi.org/10.1007/s43441-022-00493-y |
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