Challenges of Vancomycin Dosing and Therapeutic Monitoring in Neonates

Epidemiology of neonatal sepsis in two neonatal intensive care units in Krakow, Poland in 2016-2017 years

BACKGROUND

Sepsis in low-birth-weight neonates remains one of the most significant causes of neonatal morbidity and mortality. Approximately 3 million newborns suffer from sepsis globally every year. The aim of this study was to compare demographic and clinical features, as well as etiology and antibiotic susceptibility, of the main pathogens related to neonatal sepsis in two neonatal intensive units during a two-year period.

METHODS

We observed early-onset (EO-BSI) and late-onset bloodstream infections (LO-BSI) cases in two high-reference neonatal intensive care units (NICU) over a 24-month period (2016-2017). Samples of patients' blood were tested for the presence of the microorganisms. All bacterial isolates were tested for susceptibility to antibiotics.

RESULTS

The majority of sepsis cases weighed above 1000 g and were born by cesarean section. About 10% of the EO-BSI group died. There were differences in the EO-BSI /LO-BSI ratio in the compared wards due to differences among the admitted children. The most common pathogens isolated from blood were coagulase-negative staphylococci (CoNS) were represented by two dominating species: S. epidermidis and S. haemolyticus, followed by Klebsiella spp. strains and E.coli, which were mostly found in EO-BSI cases. No single S. agalactiae (GBS) strain was isolated. The majority of CoNS strains were resistant to methicillin, half were resistant to aminoglycosides, and one-third were resistant to macrolides and lincosamides. Half of the Gram-negative rods were resistant to beta-lactams.

CONCLUSIONS

The epidemiology of sepsis in two observed NICUs is comparable to data obtained from other studies with a predominance of methicillin-resistant CoNS in LO-BSI and beta-lactam resistant E. coli in EO-BSI. It is of importance that the campaign for controlling GBS carriage in pregnant women in Poland resulted in the disappearance of GBS as a cause of sepsis. Unfortunately, there are no such measures to control E.coli related sepsis.

Evaluation of an Empiric Vancomycin Dosing Protocol on Goal Troughs and Acute Kidney Injury in a Neonatal Intensive Care Unit

OBJECTIVE

Review the efficacy and safety of an updated empiric vancomycin dosing protocol in a neonatal intensive care unit (NICU).

METHODS

Retrospective chart review including neonates with postmenstrual age (PMA) less than 40 weeks without renal dysfunction who received vancomycin per protocol at a single institution's NICU before and after implementation of an updated dosing protocol. The primary outcome is the proportion of initial therapeutic troughs. Secondary outcomes include average trough, achievement of a therapeutic trough, number of days before attainment of a therapeutic trough, and proportion of acute kidney injury (AKI) during therapy.

RESULTS

The 2 groups were similar in gestational age, race, birth weight, PMA, and weight at time of vancomycin initiation. The post-implementation group had a higher proportion of initial therapeutic troughs (33.0% vs 55.1%) and a lower proportion of a subtherapeutic (58.7% vs 43.8%) and supratherapeutic (8.3% vs 1.1%) initial troughs (p = 0.002). The median trough was not different (9.20 vs 10.50 mg/L; p = 0.092). There was no difference in the proportions of achieving a therapeutic trough throughout therapy (69% vs 76%; p = 0.235); however, the post-implementation group achieved a therapeutic trough 1 day earlier (3 vs 2 days; p < 0.001). There was no difference in proportions of AKI developing between the pre-implementation vs post-implementation groups (10.1% vs 5.6%; p = 0.251).

CONCLUSIONS

Implementation of an updated vancomycin dosing protocol yielded a higher percentage of initial therapeutic vancomycin troughs and patients reached the therapeutic range 1 day earlier without increasing the proportion of AKI.

Use of Machine Learning for Dosage Individualization of Vancomycin in Neonates

BACKGROUND AND OBJECTIVE

High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C0) and steady-state area-under-curve (AUC0-24) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions.

METHODS

C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation.

RESULTS

Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%.

CONCLUSION

C0-based and AUC0-24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.

Association between Vancomycin Pharmacokinetic Parameters and Clinical and Microbiological Efficacy in a Cohort of Neonatal Patients

Vancomycin pharmacokinetic/pharmacodynamic (PK/PD) targets have not been validated in the neonatal population as no specifically designed studies are available. The main goal of this study was to analyze the therapeutic vancomycin regimen, the 24-h area under the curve (AUC₂₄), and the trough plasma concentration (C_t) obtained that achieved clinical and microbiological effectiveness in a cohort of neonates. This was an observational, prospective, single-center study covering a period of 2 years. Eligible patients were neonates and young infants who were undergoing treatment with intravenous vancomycin for ≥72 h with ≥1 C_t available. The primary outcome was the association of C_t and AUC₂₄ with clinical and microbiological efficacy at the beginning (early clinical evolution [ECE]) and the end (late clinical evolution [LCE]) of treatment with vancomycin. A total of 43 patients were included, 88.4% of whom were cured. In ECE, the cutoff points of the receiver operating characteristic (ROC) curve were 238 mg · h/L (sensitivity of 61% and specificity of 88%) for AUC₂₄ and 6.8 μg/mL (sensitivity of 61% and specificity of 92%) for C_t. In LCE, the C_t value was 11 μg/mL, with a sensitivity of 80% and a specificity of 92%. In this analysis, AUC₂₄ was not considered a good predictor. Logistic regression showed that a vancomycin C_t of ≤6.8 μg/mL was associated with an unfavorable ECE (P = 0.001), being 18 times more likely to progress poorly compared to those with higher levels. AUC₂₄ and C_t are good predictors of ECE in this population. Concentrations close to 7 μg/mL and an AUC₂₄ of around 240 mg · h/L 48 h after antibiotic initiation seem to be sufficient to achieve clinical cure in most cases.

Optimization of Vancomycin Initial Dose in Term and Preterm Neonates by Machine Learning

INTRODUCTION

Vancomycin is one of the antibiotics most used in neonates. Continuous infusion has many advantages over intermittent infusions, but no consensus has been achieved regarding the optimal initial dose. The objectives of this study were: to develop a Machine learning (ML) algorithm based on pharmacokinetic profiles obtained by Monte Carlo simulations using a population pharmacokinetic model (POPPK) from the literature, in order to derive the best vancomycin initial dose in preterm and term neonates, and to compare ML performances with those of an literature equation (LE) derived from a POPPK previously published.

MATERIALS AND METHODS

The parameters of a previously published POPPK model of vancomycin in children and neonates were used in the mrgsolve R package to simulate 1900 PK profiles. ML algorithms were developed from these simulations using Xgboost, GLMNET and MARS in parallel, benchmarked and used to calculate the ML first dose. Performances were evaluated in a second simulation set and in an external set of 82 real patients and compared to those of a LE.

RESULTS

The Xgboost algorithm yielded numerically best performances and target attainment rates: 46.9% in the second simulation set of 400-600 AUC/MIC ratio vs. 41.4% for the LE model (p = 0.0018); and 35.3% vs. 28% in real patients (p = 0.401), respectively). The Xgboost model resulted in less AUC/MIC > 600, thus decreasing the risk of nephrotoxicity.

CONCLUSION

The Xgboost algorithm developed to estimate the initial dose of vancomycin in term or preterm infants has better performances than a previous validated LE and should be evaluated prospectively.

Role of fluid status markers as risk factors for suboptimal vancomycin concentration during continuous infusion in neonates: an observational study

Vancomycin is widely used in neonatal sepsis but proportion of newborn reaching recommended concentration is variable. Fluid status impact on vancomycin level remains understudied. We aimed to study fluid factors impacting vancomycin concentration at 24 h of treatment. We performed a prospective and retrospective observational monocentric study of NICU patients requiring a vancomycin treatment. We used a continuous infusion protocol, with age-appropriate loading and maintenance doses. Vancomycin target serum concentration after 24 h (C_24h) was above 20 mg/L. Demographic, infections, and organ failure variables were analyzed as potential predictors of C_24h. Over the study period, 70 infective episodes in 52 patients were included. At treatment initiation, the median post-natal age was 12.5 days (IQR 7-23), post menstrual age 30 weeks (IQR 28-35), and median weight 1140 g (IQR 835-1722). Germs isolated were mainly gram-positive with 73.5% being coagulase-negative Staphylococci. Median C_24h was 18.7 mg/L (IQR 15.4-22.4). Overall, 41 (58.6%) treatments had a C_24h < 20 mg/L. After multivariate analysis, higher creatinine level (OR 1.03 (95% CI 1.002-1.06)) was associated with C_24h ≥ 20 mg/L; weight gain the day before infection (OR 0.21 (95% CI 0.05-0.79)) and positive biomarkers of inflammation (OR 0.22 (0.05-0.94)) were associated with C_24h < 20 mg/L.

CONCLUSION

Vancomycin C_24h was underdosed in 60% of patients and factors linked to changes in vancomycin pharmacokinetic such as volume of distribution and clearance, linked to creatinine level, inflammation, or weight gain, were identified.

WHAT IS KNOWN

• Adjustment of vancomycin regimen remains difficult due to inter- and intra-individual variability of vancomycin pharmacokinetics. • Impact of fluid status on vancomycin concentration in critically ill neonates is incompletely studied.

WHAT IS NEW

• Proportion of patients with adequate vancomycin concentration using a target adapted to nosocomial gram-positive bacteria MIC is low. • We confirmed the role of creatinine level and report two new factors associated with low vancomycin concentration: presence of systemic inflammation and weight gain.

Variation in vancomycin dosing and therapeutic drug monitoring practices in neonatal intensive care units

Background Vancomycin is a frequently used antibiotic in neonates. However, there is no consensus guideline on the optimal dosing regimen and therapeutic drug monitoring (TDM) practices in this patient population. Objective To document the variability in the current dosing and TDM practices in neonatal intensive care units (NICU). Setting Belgian and Dutch NICUs. Method An online questionnaire was disseminated by e-mail to potential respondents. Main outcome measure Differences in vancomycin dosing and TDM practices in comparison with a reference source, the Dutch Paediatric Formulary. Results Eighteen NICUs (response rate 62%) participated. Eleven different dosing regimens are applied, with 83% using intermittent dosing regimens. Stratifying covariates used to determine the (initial) dosage include gestational age, postnatal age, serum creatinine, concurrent use of non-steroidal anti-inflammatory drugs, birth weight and current weight. Large variability is observed with regard to TDM practice as well, both for the concentration target range and the times of (re)sampling. Dosing calculators are more commonly used in the Netherlands than Belgium. Conclusion Significant inter-centre variability in dosing and TDM practices was found. The development of international consensus guidelines is required to optimize therapy. Dosing calculators to guide dosing are not yet considered as part of standard-of-care.

Vancomycin dosing and therapeutic drug monitoring practices: guidelines versus real-life

Background Correct dosing and therapeutic drug monitoring (TDM) practices are essential when aiming for optimal vancomycin treatment. Objective To assess target attainment after initial dosing and dose adjustments, and to determine compliance to dosing and TDM guidelines. Setting Tertiary care university hospital in Belgium. Method A chart review was performed in 150 patients, ranging from preterm infants to adults, treated intravenously with vancomycin. Patient characteristics, dosing and TDM data were compared to evidence-based hospital guidelines. Main outcome measures Target attainment of vancomycin after initial dosing and dose adjustments. Results Subtherapeutic concentrations were measured in 68% of adults, in 76% of children and in 52% of neonates after treatment initiation. Multiple dose adaptations (median 2, Q1 1-Q3 2) were required for target attainment, whilst more than 20% of children and neonates never reached targeted concentrations. Regarding compliance to the hospital guideline, some points of improvement were identified: omitted dose adjustment in adults with decreased renal function (53%), delayed sampling (16% in adults, 31% in children) and redundant sampling (34% of all samples in adults, 12% in children, 13% in neonates). Conclusion Target attainment for vancomycin with current dosing regimens and TDM is poor in all age groups. Besides, human factors should not be ignored when aiming for optimal treatment. This study reflects an ongoing challenge in clinical practice and highlights the need for optimization of vancomycin dosing strategies and improvement of awareness of all health care professionals involved.

Population pharmacokinetics and dose optimization of vancomycin in neonates

The pharmacokinetics of vancomycin vary among neonates, and we aimed to conduct population pharmacokinetic analysis to determine the optimal dosage of vancomycin in Korean neonates. From a retrospective chart review, neonates treated with vancomycin from 2008 to 2017 in a neonatal intensive care unit (NICU) were included. Vancomycin concentrations were collected based on therapeutic drug monitoring, and other patient characteristics were gathered through electronic medical records. We applied nonlinear mixed-effect modeling to build the population pharmacokinetic model. One- and two-compartment models with first-order elimination were evaluated as potential structural pharmacokinetic models. Allometric and isometric scaling was applied to standardize pharmacokinetic parameters for clearance and volume of distribution, respectively, using fixed powers (0.75 and 1, respectively, for clearance and volume). The predictive performance of the final model was developed, and dosing strategies were explored using Monte Carlo simulations with AUC_0–24 targets 400–600. The patient cohort included 207 neonates, and 900 vancomycin concentrations were analyzed. Only 37.4% of the analyzed concentrations were within trough concentrations 5–15 µg/mL. A one-compartment model with first-order elimination best described the vancomycin pharmacokinetics in neonates. Postmenstrual age (PMA) and creatinine clearance (CLcr) affected the clearance of vancomycin, and model evaluation confirmed the robustness of the final model. Population pharmacokinetic modeling and dose optimization of vancomycin in Korean neonates showed that vancomycin clearance was related to PMA and CLcr, as well as body weight. A higher dosage regimen than the typical recommendation is suggested.