Does Monitoring Total and Free Polymyxin B1 Plasma Concentrations Predict Polymyxin B-Induced Nephrotoxicity? A Retrospective Study in Critically Ill Patients.

INTRODUCTION: The correlation between total and free polymyxin B (PMB including PMB1 and PMB2) exposure in vivo and acute kidney injury (AKI) remains obscure. This study explores the relationships between plasma exposure of PMB1 and PMB2 and nephrotoxicity, and investigates the risk factors for PMB-induced acute kidney injury (AKI) in critically ill patients. METHODS: Critically ill patients who used PMB and met the criteria were enrolled. The total plasma concentration and plasma binding of PMB1 and PMB2 were analysed by liquid chromatography-tandem mass spectrometry and equilibrium dialysis. RESULTS: A total of 89 patients were finally included, and AKI developed in 28.1% of them. The peak concentration of PMB1 (Cmax (B1)) (adjusted odds ratio (AOR) = 1.68, 95% CI 1.08-2.62, p = 0.023), baseline BUN level (AOR = 1.08, 95% CI 1.01-1.16, p = 0.039) and hypertension (AOR = 3.73, 95% CI 1.21-11.54, p = 0.022) were independent risk factors for PMB-induced AKI. The area under the ROC curve of the model was 0.799. When Cmax (B1) was 5.23 µg/ml or more, the probability of AKI was higher than 50%. The ratio of PMB1/PMB2 decreased after PMB preparation entered into the body. The protein binding rate in critically ill patients indicated significant individual differences. Free Cmax (B) and free Cmax (B1) levels in the AKI group were significantly (p < 0.05) higher than those in the non-AKI group. Total and free concentrations of PMB in patients showed a positive correlation. CONCLUSIONS: Both the ROC curve and logistic regression model showed that Cmax (B1) was a good predictor for the probability of PMB-induced AKI. Early therapeutic drug monitoring (TDM) of PMB should be considered in critically ill patients. Compared with Cmin (B), Cmax (B) and Cmax (B1) may be helpful for the early prediction of PMB-induced AKI in critically ill patients.

Nemonoxacin Enhances Antibacterial Activity and Anti-Resistance Mutation Ability of Vancomycin against Methicillin-Resistant Staphylococcus aureus in an In Vitro Dynamic Pharmacokinetic/Pharmacodynamic Model.

Reduced susceptibility and emergence of resistance to vancomycin in methicillin-resistant Staphylococcus aureus (MRSA) have led to the development of various vancomycin-based combinations. Nemonoxacin is a novel nonfluorinated quinolone with antibacterial activity against MRSA. The present study aimed to investigate the effects of nemonoxacin on antibacterial activity and the anti-resistant mutation ability of vancomycin for MRSA and explore whether quinolone resistance genes are associated with a reduction in the vancomycin MIC and mutant prevention concentration (MPC) when combined with nemonoxacin. Four isolates, all with vancomycin MICs of 2 µg/mL, were used in a modified in vitro dynamic pharmacokinetic/pharmacodynamic model to investigate the effects of nemonoxacin on antibacterial activity (isolates M04, M23, and M24) and anti-resistant mutation ability (isolates M04, M23, and M25, all with MPCs of ≥19.2 µg/mL) of vancomycin. The mutation sites of gyrA, gyrB, parC, and parE of 55 clinical MRSA isolates were sequenced. We observed that in M04 and M23, the combination of vancomycin (1 g given every 12 h [q12h]) and nemonoxacin (0.5 g once daily [qd]) showed a synergistic bactericidal activity and resistance enrichment suppression. All clinical isolates resistant to nemonoxacin harbored gyrA (S84→L) mutation; gyrA (S84→L) and parC (E84→K) mutations were the two independent risk factors for the unchanged vancomycin MPC in combination. Nemonoxacin enhances the bactericidal activity and suppresses resistance enrichment ability of vancomycin against MRSA, with an MIC of 2 µg/mL. Our in vitro data support the combination of nemonoxacin and vancomycin for the treatment of MRSA infection with a high MIC.

Anxiety disturbs the blood plasma metabolome in acute coronary syndrome patients.

Coronary heart disease (CHD) is the result of a complex metabolic disorder caused by various environmental and genetic factors, and often has anxiety as a comorbidity. Rupture of atherosclerotic plaque in CHD patients can lead to acute coronary syndrome (ACS). Anxiety is a known independent risk factor for the adverse cardiovascular events and mortality in ACS, but it remains unclear how stress-induced anxiety behavior impacts their blood plasma metabolome and contributes to worsening of CHD. The present study aimed to determine the effect of anxiety on the plasma metabolome in ACS patients. After receiving ethical approval 26 ACS patients comorbid anxiety were recruited and matched 26 ACS patients. Blood plasma samples were collected from the patients and stored at - 80 °C until metabolome profiling. Metabolome analysis was performed by liquid chromatography mass spectrometry (LC-MS), and the data were subjected to multivariate analysis. Disturbance of 39 plasma metabolites was noted in the ACS with comorbid anxiety group compared to the ACS group. These disturbed metabolites were mainly involved in tryptophan metabolism, pyrimidine metabolism, glycerophospholipid metabolism, pentose phosphate pathway, and pentose and glucuronate interconversions. The most significantly affected pathway was tryptophan metabolism including the down-regulation of tryptophan and serotonin. Glycerophospholipids metabolism, pentose and glucuronate interconversions, and pentose phosphate pathway were also greatly affected. These results suggest that anxiety can disturb three translation of material in ACS patients. Besides the above metabolism pathways pyrimidine metabolism was significantly disturbed. Based on the present findings the plasma metabolites monitoring can be recommended and may be conducive to early biomarkers detection for personalized treatment anxiety in CHD patients in future.

Electronic medical record-based model to predict the risk of 90-day readmission for patients with heart failure.

BACKGROUND: Several heart failure (HF) risk models exist, however, most of them perform poorly when applied to real-world situations. This study aimed to develop a convenient and efficient risk model to identify patients with high readmission risk within 90 days of HF. METHODS: A multivariate logistic regression model was used to predict the risk of 90-day readmission. Data were extracted from electronic medical records from January 1, 2017 to December 31, 2017 and follow-up records of patients with HF within 3 months after discharge. Model performance was evaluated using a receiver operating characteristic curve. All statistical analysis was done using R version 3.5.0. RESULTS: A total of 350 patients met the inclusion criterion of being readmitted within in 90 days. All data sets were randomly divided into derivation and validation cohorts at a 7/3 ratio. The baseline data were fairly consistent among the derivation and validation cohorts. The variables most clearly related to readmission were logarithm of serum N-terminal pro b-type natriuretic peptide (NT-proBNP) level, red cell volume distribution width (RDW-CV), and Charlson comorbidity index (CCI). The model had good discriminatory ability (C-statistic = 0.73). CONCLUSIONS: We developed and validated a multivariate logistic regression model to predict the 90-day readmission risk for Chinese patients with HF. The predictors included in the model are derived from electronic medical record (EMR) admission data, making it easier for physicians and pharmacists to identify high-risk patients and tailor more intensive precautionary strategies.

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network.

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.