Cargando…

Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database

Background: Drug-induced kidney injury (DIKI) is one of the most common complications in clinical practice. Detection signals through post-marketing approaches are of great value in preventing DIKI in pediatric patients. This study aimed to propose a quantitative algorithm to detect DIKI signals in...

Descripción completa

Detalles Bibliográficos
Autores principales: Yu, Yuncui, Nie, Xiaolu, Zhao, Yiming, Cao, Wang, Xie, Yuefeng, Peng, Xiaoxia, Wang, Xiaoling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9543451/
https://www.ncbi.nlm.nih.gov/pubmed/36210853
http://dx.doi.org/10.3389/fphar.2022.957980
_version_ 1784804376314904576
author Yu, Yuncui
Nie, Xiaolu
Zhao, Yiming
Cao, Wang
Xie, Yuefeng
Peng, Xiaoxia
Wang, Xiaoling
author_facet Yu, Yuncui
Nie, Xiaolu
Zhao, Yiming
Cao, Wang
Xie, Yuefeng
Peng, Xiaoxia
Wang, Xiaoling
author_sort Yu, Yuncui
collection PubMed
description Background: Drug-induced kidney injury (DIKI) is one of the most common complications in clinical practice. Detection signals through post-marketing approaches are of great value in preventing DIKI in pediatric patients. This study aimed to propose a quantitative algorithm to detect DIKI signals in children using an electronic health record (EHR) database. Methods: In this study, 12 years of medical data collected from a constructed data warehouse were analyzed, which contained 575,965 records of inpatients from 1 January 2009 to 31 December 2020. Eligible participants included inpatients aged 28 days to 18 years old. A two-stage procedure was adopted to detect DIKI signals: 1) stage 1: the suspected drugs potentially associated with DIKI were screened by calculating the crude incidence of DIKI events; and 2) stage 2: the associations between suspected drugs and DIKI were identified in the propensity score-matched retrospective cohorts. Unconditional logistic regression was used to analyze the difference in the incidence of DIKI events and to estimate the odds ratio (OR) and 95% confidence interval (CI). Potentially new signals were distinguished from already known associations concerning DIKI by manually reviewing the published literature and drug instructions. Results: Nine suspected drugs were initially screened from a total of 652 drugs. Six drugs, including diazepam (OR = 1.61, 95%CI: 1.43–1.80), omeprazole (OR = 1.35, 95%CI: 1.17–1.54), ondansetron (OR = 1.49, 95%CI: 1.36–1.63), methotrexate (OR = 1.36, 95%CI: 1.25–1.47), creatine phosphate sodium (OR = 1.13, 95%CI: 1.05–1.22), and cytarabine (OR = 1.17, 95%CI: 1.06–1.28), were demonstrated to be associated with DIKI as positive signals. The remaining three drugs, including vitamin K1 (OR = 1.06, 95%CI: 0.89–1.27), cefamandole (OR = 1.07, 95%CI: 0.94–1.21), and ibuprofen (OR = 1.01, 95%CI: 0.94–1.09), were found not to be associated with DIKI. Of these, creatine phosphate sodium was considered to be a possible new DIKI signal as it had not been reported in both adults and children previously. Moreover, three other drugs, namely, diazepam, omeprazole, and ondansetron, were shown to be new potential signals in pediatrics. Conclusion: A two-step quantitative procedure to actively explore DIKI signals using real-world data (RWD) was developed. Our findings highlight the potential of EHRs to complement traditional spontaneous reporting systems (SRS) for drug safety signal detection in a pediatric setting.
format Online
Article
Text
id pubmed-9543451
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-95434512022-10-08 Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database Yu, Yuncui Nie, Xiaolu Zhao, Yiming Cao, Wang Xie, Yuefeng Peng, Xiaoxia Wang, Xiaoling Front Pharmacol Pharmacology Background: Drug-induced kidney injury (DIKI) is one of the most common complications in clinical practice. Detection signals through post-marketing approaches are of great value in preventing DIKI in pediatric patients. This study aimed to propose a quantitative algorithm to detect DIKI signals in children using an electronic health record (EHR) database. Methods: In this study, 12 years of medical data collected from a constructed data warehouse were analyzed, which contained 575,965 records of inpatients from 1 January 2009 to 31 December 2020. Eligible participants included inpatients aged 28 days to 18 years old. A two-stage procedure was adopted to detect DIKI signals: 1) stage 1: the suspected drugs potentially associated with DIKI were screened by calculating the crude incidence of DIKI events; and 2) stage 2: the associations between suspected drugs and DIKI were identified in the propensity score-matched retrospective cohorts. Unconditional logistic regression was used to analyze the difference in the incidence of DIKI events and to estimate the odds ratio (OR) and 95% confidence interval (CI). Potentially new signals were distinguished from already known associations concerning DIKI by manually reviewing the published literature and drug instructions. Results: Nine suspected drugs were initially screened from a total of 652 drugs. Six drugs, including diazepam (OR = 1.61, 95%CI: 1.43–1.80), omeprazole (OR = 1.35, 95%CI: 1.17–1.54), ondansetron (OR = 1.49, 95%CI: 1.36–1.63), methotrexate (OR = 1.36, 95%CI: 1.25–1.47), creatine phosphate sodium (OR = 1.13, 95%CI: 1.05–1.22), and cytarabine (OR = 1.17, 95%CI: 1.06–1.28), were demonstrated to be associated with DIKI as positive signals. The remaining three drugs, including vitamin K1 (OR = 1.06, 95%CI: 0.89–1.27), cefamandole (OR = 1.07, 95%CI: 0.94–1.21), and ibuprofen (OR = 1.01, 95%CI: 0.94–1.09), were found not to be associated with DIKI. Of these, creatine phosphate sodium was considered to be a possible new DIKI signal as it had not been reported in both adults and children previously. Moreover, three other drugs, namely, diazepam, omeprazole, and ondansetron, were shown to be new potential signals in pediatrics. Conclusion: A two-step quantitative procedure to actively explore DIKI signals using real-world data (RWD) was developed. Our findings highlight the potential of EHRs to complement traditional spontaneous reporting systems (SRS) for drug safety signal detection in a pediatric setting. Frontiers Media S.A. 2022-09-23 /pmc/articles/PMC9543451/ /pubmed/36210853 http://dx.doi.org/10.3389/fphar.2022.957980 Text en Copyright © 2022 Yu, Nie, Zhao, Cao, Xie, Peng and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Yu, Yuncui
Nie, Xiaolu
Zhao, Yiming
Cao, Wang
Xie, Yuefeng
Peng, Xiaoxia
Wang, Xiaoling
Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database
title Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database
title_full Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database
title_fullStr Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database
title_full_unstemmed Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database
title_short Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database
title_sort detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9543451/
https://www.ncbi.nlm.nih.gov/pubmed/36210853
http://dx.doi.org/10.3389/fphar.2022.957980
work_keys_str_mv AT yuyuncui detectionofpediatricdruginducedkidneyinjurysignalsusingahospitalelectronicmedicalrecorddatabase
AT niexiaolu detectionofpediatricdruginducedkidneyinjurysignalsusingahospitalelectronicmedicalrecorddatabase
AT zhaoyiming detectionofpediatricdruginducedkidneyinjurysignalsusingahospitalelectronicmedicalrecorddatabase
AT caowang detectionofpediatricdruginducedkidneyinjurysignalsusingahospitalelectronicmedicalrecorddatabase
AT xieyuefeng detectionofpediatricdruginducedkidneyinjurysignalsusingahospitalelectronicmedicalrecorddatabase
AT pengxiaoxia detectionofpediatricdruginducedkidneyinjurysignalsusingahospitalelectronicmedicalrecorddatabase
AT wangxiaoling detectionofpediatricdruginducedkidneyinjurysignalsusingahospitalelectronicmedicalrecorddatabase