DataSheet1_Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database.docx

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.</p

Association of KCNB1 polymorphisms with lipid metabolisms and insulin resistance: a case-control design of population-based cross-sectional study in Chinese Han population

Abstract Background In our previous study, we had assessed in the Chinese Han population the association of KCNB1 rs1051295 with metabolic traits indicating metabolic syndrome, and showed that KCNB1 rs1051295 genotype TT was associated with increase of waist to hip ratio (WHR), fasting insulin (FINS), triglycerides (TG) and decreased insulin sensitivity at basal condition. Here, we aimed at detecting whether there were associations between other tag SNPs of KCNB1 and favorable or unfavorable metabolic traits. Methods We conducted a case–control design of population-based cross-sectional study to investigate the association between each of the 22 candidates tag SNPs of KCNB1 and metabolic traits in a population of 733 Chinese Han individuals. The association was assessed by multiple linear regression analysis or unconditional logistic regression analysis. Results We found that among the 22 selected tag SNPs, four were associated with an increase (rs3331, rs16994565) or decrease (rs237460, rs802950) in serum cholesterol levels; two of these (rs237460, rs802590) further associated or were associated with reduced serum LDL-cholesterol. Two novel tag SNPs (rs926672, rs1051295) were associated with increased serum TG levels. We also showed that KCNB1 rs926672 associated with insulin resistance by a case–control study, and two tag SNPs (rs2057077and rs4810952) of KCNB1 were associated with the measure of insulin resistance (HOMA-IR) in a cross-section study. Conclusion These results indicate that KCNB1 is likely associated with metabolic traits that may either predispose or protect from progression to metabolic syndrome. This study provides initial evidence that the gene variants of KCNB1, encoding Kv2.1 channel, is associated with perturbation of lipid metabolism and insulin resistance in Chinese Han population