Optimizing Meropenem in Highly Resistant Klebsiella pneumoniae Environments: Population Pharmacokinetics and Dosing Simulations in Critically Ill Patients.

Critically ill patients are characterized by substantial pathophysiological changes that alter the pharmacokinetics (PK) of hydrophilic antibiotics, including carbapenems. Meropenem is a key antibiotic for multidrug-resistant Gram-negative bacilli, and such pathophysiological alterations can worsen treatment outcomes. This study aimed to determine the population PK of meropenem and to propose optimized dosing regimens for the treatment of multidrug-resistant Klebsiella pneumoniae in critically ill patients. Two plasma samples were collected from eligible patients over a dosing interval. Nonparametric population PK modeling was performed using Pmetrics. Monte Carlo simulations were applied to different dosing regimens to determine the probability of target attainment and the cumulative fraction of response, taking into account the local MIC distribution for K. pneumoniae. The targets of 40% and 100% for the fraction of time that free drug concentrations remained above the MIC (ƒT>MIC) were tested, as suggested for critically ill patients. A one-compartment PK model using data from 27 patients showed high interindividual variability. Significant PK covariates were the 8-h creatinine clearance for meropenem and the presence of an indwelling catheter for pleural, abdominal, or cerebrospinal fluid drainage for the meropenem volume of distribution. The target 100% ƒT>MIC for K. pneumoniae, with a MIC of ≤2 mg/liter, could be attained by the use of a continuous infusion of 2.0 g/day. Meropenem therapy in critically ill patients could be optimized for K. pneumoniae isolates with an MIC of ≤2 mg/liter by using a continuous infusion in settings with more than 50% isolates have a MIC of ≥32mg/L.

Predicting mortality in intensive care unit patients infected with Klebsiella pneumoniae: A retrospective cohort study.

INTRODUCTION: Although several models to predict intensive care unit (ICU) mortality are available, their performance decreases in certain subpopulations because specific factors are not included. Moreover, these models often involve complex techniques and are not applicable in low-resource settings. We developed a prediction model and simplified risk score to predict 14-day mortality in ICU patients infected with Klebsiella pneumoniae. METHODOLOGY: A retrospective cohort study was conducted using data of ICU patients infected with Klebsiella pneumoniae at the largest tertiary hospital in Northern Vietnam during 2016-2018. Logistic regression was used to develop our prediction model. Model performance was assessed by calibration (area under the receiver operating characteristic curve-AUC) and discrimination (Hosmer-Lemeshow goodness-of-fit test). A simplified risk score was also constructed. RESULTS: Two hundred forty-nine patients were included, with an overall 14-day mortality of 28.9%. The final prediction model comprised six predictors: age, referral route, SOFA score, central venous catheter, intracerebral haemorrhage surgery and absence of adjunctive therapy. The model showed high predictive accuracy (AUC = 0.83; p-value Hosmer-Lemeshow test = 0.92). The risk score has a range of 0-12 corresponding to mortality risk 0-100%, which produced similar predictive performance as the original model. CONCLUSIONS: The developed prediction model and risk score provide an objective quantitative estimation of individual 14-day mortality in ICU patients infected with Klebsiella pneumoniae. The tool is highly applicable in practice to help facilitate patient stratification and management, evaluation of further interventions and allocation of resources and care, especially in low-resource settings where electronic systems to support complex models are missing.