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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.
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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.
