The main goal of our study was to characterize the population pharmacokinetics of vancomycin in critically ill Chinese neonates to develop a pharmacokinetic model and investigate factors that have significant influences on the pharmacokinetics of vancomycin in this population. The study population consisted of 80 neonates in the neonatal intensive care unit (ICU) from which 165 trough and peak concentrations of vancomycin were obtained. Nonlinear mixed effect modeling was used to develop a population pharmacokinetic model for vancomycin. The stability and predictive ability of the final model were evaluated based on diagnostic plots, normalized prediction distribution errors and the bootstrap method. Serum creatinine (Scr) and body weight were significant covariates on the clearance of vancomycin. The average clearance was 0.309 L/h for a neonate with Scr of 23.3 μmol/L and body weight of 2.9 kg. No obvious ethnic differences in the clearance of vancomycin were found relative to the earlier studies of Caucasian neonates. Moreover, the established model indicated that in patients with a greater renal clearance status, especially Scr < 15 μmol/L, current guideline recommendations would likely not achieve therapeutic area under the concentration-time curve over 24 h/minimum inhibitory concentration (AUC_24h/MIC) ≥ 400. The exceptions to this are British National Formulary (2016–2017), Blue Book (2016) and Neofax (2017). Recommended dose regimens for neonates with different Scr levels and postmenstrual ages were estimated based on Monte Carlo simulations and the established model. These findings will be valuable for developing individualized dosage regimens in the neonatal ICU setting.

Cyclosporine (CsA) is an immunosuppressive agent that often causes acute kidney injury (AKI) in children. The specific mechanisms underlying CsA-induced AKI are currently unknown. This study used an integrated network analysis of microRNA (miRNA) and mRNA expression profiles, biochemical and pathological analyses to further investigate these potential mechanisms of CsA-induced AKI. Small RNA sequence analysis identified 25 differentially expressed miRNAs, RNA sequencing analysis identified 4,109 differentially expressed mRNAs. We obtained a total of 4,367 target genes from the 25 differentially expressed miRNAs based on three algorithms, including the Mirdb, Mirtarbase, and TargetScan. 971 target genes overlapped between the 4,367 target genes and 4,109 differentially expressed mRNAs were identified for further bioinformatics analysis. Finally, 30 hub genes and two main modules were recognized. Functional enrichment analysis of 30 hub genes indicated that inflammation and epithelial-mesenchymal transition (EMT) related genes were mainly concentrated together. Pathway analysis revealed that the PI3K-Akt signaling pathway plays an integral role in CsA-induced AKI. Network analysis identified 3 important miRNAs, mmu‐miR-17b‐5p, mmu-miR-19b-3p, and mmu-mir-423-5p that may further promote the development of inflammatory responses and EMT by mediating a complex network of factors. Our research provides a clearer understanding the molecular mechanism of this specific drug-induced AKI by CsA use, which is useful for discovering potential targets for gene therapies, and drug development in CsA-induced AKI.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Pattern mining utilizes multiple algorithms to explore objective and sometimes unexpected patterns in real world data. This technique could be applied to electronic medical records (EMRs) data mining; however, it first requires a careful clinical assessment and validation. Objective: Examine the use of pattern mining techniques on a large clinical dataset to detect treatment and medication use patterns for childhood pneumonia. Methods: We applied three pattern mining algorithms to 680,138 medication administration records from 30,512 pediatric inpatients with diagnosis of pneumonia during a six-year period at a children’s hospital in China. Patients’ ages ranged from 0-17 years; 37.5% were 0-3 months old, 86.5% were under 5 years old 60.3% were male, and 60.1% had a hospital stay of 9-15 days. We used the FP-Growth, PrefixSpan, and USpan pattern mining algorithms. The first two are more traditional methods of pattern mining and mine a complete set of frequent medication use patterns. PrefixSpan also incorporates an administration sequence. The newer USpan method considers medication utility, defined by the dose, frequency, and timing of use of the 652 individual medications in the dataset. Together, these three methods identified the top ten patterns from six age groups, forming a total of 180 distinct medication combinations. These medications encompassed the top 40 (72.0%) most frequently used medications. These patterns were then evaluated by subject matter experts to summarize five medication use and two treatment patterns. Results: We identified five medication use patterns: 1) anti-asthmatics AND expectorants AND corticosteroids; 2) antibiotics AND (anti-asthmatics OR expectorants OR corticosteroids); 3) third-generation cephalosporin antibiotics with (or followed by) traditional antibiotics; 4) antibiotics AND (medications for enteritis OR skin diseases); and 5) (anti-asthmatics OR expectorants OR corticosteroids) AND (medications for enteritis OR skin diseases). We also identified two frequent treatment patterns: 1) 42.1% of specific medication administration records were of intravenous therapy with antibiotics, diluents, and nutritional supplements; and 2) 13.1% were of various combinations of inhalation of anti-asthmatics, expectorants, and/or corticosteroids. Fleiss’ Kappa for the subject experts’ evaluation was 0.693, indicating moderate agreement. Conclusions: Utilizing a pattern mining approach, we summarized five medication use patterns and two treatment patterns. These warrant further investigation.

BACKGROUND Pattern mining utilizes multiple algorithms to explore objective and sometimes unexpected patterns in real-world data. This technique could be applied to electronic medical record data mining; however, it first requires a careful clinical assessment and validation. OBJECTIVE The aim of this study was to examine the use of pattern mining techniques on a large clinical dataset to detect treatment and medication use patterns for childhood pneumonia. METHODS We applied 3 pattern mining algorithms to 680,138 medication administration records from 30,512 childhood inpatients with diagnosis of pneumonia during a 6-year period at a children’s hospital in China. Patients’ ages ranged from 0 to 17 years, where 37.53% (11,453/30,512) were 0 to 3 months old, 86.55% (26,408/30,512) were under 5 years, 60.37% (18,419/30,512) were male, and 60.10% (18,338/30,512) had a hospital stay of 9 to 15 days. We used the FP-Growth, PrefixSpan, and USpan pattern mining algorithms. The first 2 are more traditional methods of pattern mining and mine a complete set of frequent medication use patterns. PrefixSpan also incorporates an administration sequence. The newer USpan method considers medication utility, defined by the dose, frequency, and timing of use of the 652 individual medications in the dataset. Together, these 3 methods identified the top 10 patterns from 6 age groups, forming a total of 180 distinct medication combinations. These medications encompassed the top 40 (73.66%, 500,982/680,138) most frequently used medications. These patterns were then evaluated by subject matter experts to summarize 5 medication use and 2 treatment patterns. RESULTS We identified 5 medication use patterns: (1) antiasthmatics and expectorants and corticosteroids, (2) antibiotics and (antiasthmatics or expectorants or corticosteroids), (3) third-generation cephalosporin antibiotics with (or followed by) traditional antibiotics, (4) antibiotics and (medications for enteritis or skin diseases), and (5) (antiasthmatics or expectorants or corticosteroids) and (medications for enteritis or skin diseases). We also identified 2 frequent treatment patterns: (1) 42.89% (291,701/680,138) of specific medication administration records were of intravenous therapy with antibiotics, diluents, and nutritional supplements and (2) 11.53% (78,390/680,138) were of various combinations of inhalation of antiasthmatics, expectorants, or corticosteroids. Fleiss kappa for the subject experts’ evaluation was 0.693, indicating moderate agreement. CONCLUSIONS Utilizing a pattern mining approach, we summarized 5 medication use patterns and 2 treatment patterns. These warrant further investigation.