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Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records

Objective. We propose 2 Medical Dictionary for Regulatory Activities–enabled pharmacovigilance algorithms, MetaLAB and MetaNurse, powered by a per-year meta-analysis technique and improved subject sampling strategy. Matrials and methods. This study developed 2 novel algorithms, MetaLAB for laborator...

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Autores principales: Lee, Suehyun, Choi, Jiyeob, Kim, Hun-Sung, Kim, Grace Juyun, Lee, Kye Hwa, Park, Chan Hee, Han, Jongsoo, Yoon, Dukyong, Park, Man Young, Park, Rae Woong, Kang, Hye-Ryun, Kim, Ju Han
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651894/
https://www.ncbi.nlm.nih.gov/pubmed/28087585
http://dx.doi.org/10.1093/jamia/ocw168
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author Lee, Suehyun
Choi, Jiyeob
Kim, Hun-Sung
Kim, Grace Juyun
Lee, Kye Hwa
Park, Chan Hee
Han, Jongsoo
Yoon, Dukyong
Park, Man Young
Park, Rae Woong
Kang, Hye-Ryun
Kim, Ju Han
author_facet Lee, Suehyun
Choi, Jiyeob
Kim, Hun-Sung
Kim, Grace Juyun
Lee, Kye Hwa
Park, Chan Hee
Han, Jongsoo
Yoon, Dukyong
Park, Man Young
Park, Rae Woong
Kang, Hye-Ryun
Kim, Ju Han
author_sort Lee, Suehyun
collection PubMed
description Objective. We propose 2 Medical Dictionary for Regulatory Activities–enabled pharmacovigilance algorithms, MetaLAB and MetaNurse, powered by a per-year meta-analysis technique and improved subject sampling strategy. Matrials and methods. This study developed 2 novel algorithms, MetaLAB for laboratory abnormalities and MetaNurse for standard nursing statements, as significantly improved versions of our previous electronic health record (EHR)–based pharmacovigilance method, called CLEAR. Adverse drug reaction (ADR) signals from 117 laboratory abnormalities and 1357 standard nursing statements for all precautionary drugs (n  = 101) were comprehensively detected and validated against SIDER (Side Effect Resource) by MetaLAB and MetaNurse against 11 817 and 76 457 drug-ADR pairs, respectively. Results. We demonstrate that MetaLAB (area under the curve, AUC = 0.61 ± 0.18) outperformed CLEAR (AUC = 0.55 ± 0.06) when we applied the same 470 drug-event pairs as the gold standard, as in our previous research. Receiver operating characteristic curves for 101 precautionary terms in the Medical Dictionary for Regulatory Activities Preferred Terms were obtained for MetaLAB and MetaNurse (0.69 ± 0.11; 0.62 ± 0.07), which complemented each other in terms of ADR signal coverage. Novel ADR signals discovered by MetaLAB and MetaNurse were successfully validated against spontaneous reports in the US Food and Drug Administration Adverse Event Reporting System database. Discussion. The present study demonstrates the symbiosis of laboratory test results and nursing statements for ADR signal detection in terms of their system organ class coverage and performance profiles. Conclusion. Systematic discovery and evaluation of the wide spectrum of ADR signals using standard-based observational electronic health record data across many institutions will affect drug development and use, as well as postmarketing surveillance and regulation.
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spelling pubmed-76518942020-11-30 Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records Lee, Suehyun Choi, Jiyeob Kim, Hun-Sung Kim, Grace Juyun Lee, Kye Hwa Park, Chan Hee Han, Jongsoo Yoon, Dukyong Park, Man Young Park, Rae Woong Kang, Hye-Ryun Kim, Ju Han J Am Med Inform Assoc Research and Applications Objective. We propose 2 Medical Dictionary for Regulatory Activities–enabled pharmacovigilance algorithms, MetaLAB and MetaNurse, powered by a per-year meta-analysis technique and improved subject sampling strategy. Matrials and methods. This study developed 2 novel algorithms, MetaLAB for laboratory abnormalities and MetaNurse for standard nursing statements, as significantly improved versions of our previous electronic health record (EHR)–based pharmacovigilance method, called CLEAR. Adverse drug reaction (ADR) signals from 117 laboratory abnormalities and 1357 standard nursing statements for all precautionary drugs (n  = 101) were comprehensively detected and validated against SIDER (Side Effect Resource) by MetaLAB and MetaNurse against 11 817 and 76 457 drug-ADR pairs, respectively. Results. We demonstrate that MetaLAB (area under the curve, AUC = 0.61 ± 0.18) outperformed CLEAR (AUC = 0.55 ± 0.06) when we applied the same 470 drug-event pairs as the gold standard, as in our previous research. Receiver operating characteristic curves for 101 precautionary terms in the Medical Dictionary for Regulatory Activities Preferred Terms were obtained for MetaLAB and MetaNurse (0.69 ± 0.11; 0.62 ± 0.07), which complemented each other in terms of ADR signal coverage. Novel ADR signals discovered by MetaLAB and MetaNurse were successfully validated against spontaneous reports in the US Food and Drug Administration Adverse Event Reporting System database. Discussion. The present study demonstrates the symbiosis of laboratory test results and nursing statements for ADR signal detection in terms of their system organ class coverage and performance profiles. Conclusion. Systematic discovery and evaluation of the wide spectrum of ADR signals using standard-based observational electronic health record data across many institutions will affect drug development and use, as well as postmarketing surveillance and regulation. Oxford University Press 2017-07 2017-01-13 /pmc/articles/PMC7651894/ /pubmed/28087585 http://dx.doi.org/10.1093/jamia/ocw168 Text en © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For Permissions, please email: journals.permissions@oup.com
spellingShingle Research and Applications
Lee, Suehyun
Choi, Jiyeob
Kim, Hun-Sung
Kim, Grace Juyun
Lee, Kye Hwa
Park, Chan Hee
Han, Jongsoo
Yoon, Dukyong
Park, Man Young
Park, Rae Woong
Kang, Hye-Ryun
Kim, Ju Han
Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records
title Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records
title_full Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records
title_fullStr Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records
title_full_unstemmed Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records
title_short Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records
title_sort standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651894/
https://www.ncbi.nlm.nih.gov/pubmed/28087585
http://dx.doi.org/10.1093/jamia/ocw168
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