Dataset Information

J-CKD-DB: a nationwide multicentre electronic health record-based chronic kidney disease database in Japan.

ABSTRACT: The Japan Chronic Kidney Disease (CKD) Database (J-CKD-DB) is a large-scale, nation-wide registry based on electronic health record (EHR) data from participating university hospitals. Using a standardized exchangeable information storage, the J-CKD-DB succeeded to efficiently collect clinical data of CKD patients across hospitals despite their different EHR systems. CKD was defined as dipstick proteinuria ≥1+ and/or estimated glomerular filtration rate <60 mL/min/1.73 m² base on both out- and inpatient laboratory data. As an initial analysis, we analyzed 39,121 CKD outpatients (median age was 71 years, 54.7% were men, median eGFR was 51.3 mL/min/1.73 m²) and observed that the number of patients with a CKD stage G1, G2, G3a, G3b, G4 and G5 were 1,001 (2.6%), 2,612 (6.7%), 23,333 (59.6%), 8,357 (21.4%), 2,710 (6.9%) and 1,108 (2.8%), respectively. According to the KDIGO risk classification, there were 30.1% and 25.5% of male and female patients with CKD at very high-risk, respectively. As the information from every clinical encounter from those participating hospitals will be continuously updated with an anonymized patient ID, the J-CKD-DB will be a dynamic registry of Japanese CKD patients by expanding and linking with other existing databases and a platform for a number of cross-sectional and prospective analyses to answer important clinical questions in CKD care.

SUBMITTER: Nakagawa N

PROVIDER: S-EPMC7192920 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

J-CKD-DB: a nationwide multicentre electronic health record-based chronic kidney disease database in Japan.

Nakagawa Naoki N Sofue Tadashi T Kanda Eiichiro E Nagasu Hajime H Matsushita Kunihiro K Nangaku Masaomi M Maruyama Shoichi S Wada Takashi T Terada Yoshio Y Yamagata Kunihiro K Narita Ichiei I Yanagita Motoko M Sugiyama Hitoshi H Shigematsu Takashi T Ito Takafumi T Tamura Kouichi K Isaka Yoshitaka Y Okada Hirokazu H Tsuruya Kazuhiko K Yokoyama Hitoshi H Nakashima Naoki N Kataoka Hiromi H Ohe Kazuhiko K Okada Mihoko M Kashihara Naoki N

Scientific reports 20200430 1

The Japan Chronic Kidney Disease (CKD) Database (J-CKD-DB) is a large-scale, nation-wide registry based on electronic health record (EHR) data from participating university hospitals. Using a standardized exchangeable information storage, the J-CKD-DB succeeded to efficiently collect clinical data of CKD patients across hospitals despite their different EHR systems. CKD was defined as dipstick proteinuria ≥1+ and/or estimated glomerular filtration rate <60 mL/min/1.73 m<sup>2</sup> base on both ...[more]

PMID: 32355258

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Project 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.

Dataset Information

J-CKD-DB: a nationwide multicentre electronic health record-based chronic kidney disease database in Japan.

Publications

J-CKD-DB: a nationwide multicentre electronic health record-based chronic kidney disease database in Japan.

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