A cohort study of the risk factors and the target AUC to avoid vancomycin-associated acute kidney injury in pediatric patients

Machine learning and population pharmacokinetics: a hybrid approach for optimizing vancomycin therapy in sepsis patients

Predicting vancomycin exposure is essential for optimizing dosing regimens in sepsis patients. While population pharmacokinetic (PPK) models are commonly used, their performance is limited. Machine learning (ML) models offer advantages over PPK models, but it remains unclear which model-PPK, Bayesian, ML, or hybrid PPK-ML-is best for predicting vancomycin exposure across different clinical scenarios in sepsis patients. This study compares the performance of these models in predicting the 24 hour area under the blood concentration curve (AUC24) to support precision dosing in sepsis care. Data from sepsis patients treated with intravenous vancomycin were sourced from the MIMIC-IV database. The data set was split into training and testing sets, and four models-PPK, Bayesian, ML, and hybrid-were developed. In the testing set, AUC24 was predicted using all models, and performance was evaluated using mean absolute error, mean squared error, root mean squared error, mean absolute percentage error (MAPE), and R². A total of 4,059 patients were included. In the absence of vancomycin concentration data, the hybrid model outperformed both PPK and Bayesian models, with MAPE improvements of 58% and 17%, respectively. When vancomycin concentration data were available, the Bayesian model demonstrated the best performance (MAPE: 13.37% vs 68.17%, 34.17%, and 28.52% for PPK, Random Forest, and hybrid models). The hybrid model is recommended to predict AUC24 when concentration data were unavailable, while the Bayesian model should be used when concentrations were available, offering robust strategies for precise vancomycin dosing in sepsis patients.

IMPORTANCE

This study evaluates and compares the performance of four models-PPK, Bayesian, ML, and hybrid PPK-ML-in predicting vancomycin exposure (AUC24) in sepsis patients using real-world data from the MIMIC-IV database. These results underscore the importance of selecting appropriate models based on the availability of concentration data, providing valuable guidance for precision dosing strategies in sepsis care. This work contributes to advancing personalized vancomycin therapy, optimizing dosing regimens, and improving clinical outcomes in sepsis patients.

Vancomycin AUC0-24 estimation using first-order pharmacokinetic methods in pediatric patients

INTRODUCTION

The optimal dosing and monitoring of vancomycin in pediatrics is still unknown but has evolved to emphasize area under the curve over 24 h (AUC0-24) over minimum concentration (Cmin) monitoring. Real-world data supporting the feasibility of two-concentration kinetics with first-order equations for the estimation of vancomycin AUC0-24 in pediatric patients are lacking.

OBJECTIVES

To describe the interplay of vancomycin dose, AUC0-24, and Cmin using first-order equations within four pediatric age groups.

METHODS

This is a single-center, retrospective cohort study analyzing pediatric patients (<18 years) receiving intravenous vancomycin between 2020 and 2022. Included patients received at least 24 h of intravenous vancomycin with two concentrations obtained within 96 h of therapy initiation. Patients with baseline renal dysfunction were excluded. Patients were divided into four age categories: neonates (≤28 days), infants (29 days to <1 year), children (1-12 years), and adolescents (13-17 years). First-order equations were utilized to estimate pharmacokinetic parameters and AUC0-24.

RESULTS

Overall, 219 patients (median age of 6 years [IQR 1-12]) met inclusion criteria. The median vancomycin daily dose was 30 mg/kg in neonates, 70 mg/kg in infants and children, and 52 mg/kg in adolescents. Median Cmin and AUC0-24 values among all age groups were 8.68 mg/L and 505 mg * h/L, respectively. For AUC0-24 values outside of the therapeutic range (400-600 mg * h/L), more values were SUPRAtherapeutic (>600 mg * h/L) than SUBtherapeutic (<400 mg * h/L). The overall trend within our data showed suboptimal correlation between Cmin and AUC0-24. However, 71% of patients with Cmin values of 5-10 mg/L had an AUC0-24 within the therapeutic range of 400-600 mg * h/L, whereas 23 patients (92%) with a SUPRAtherapeutic AUC0-24 had a Cmin value ≥15 mg/L. Approximately 10% of patients experienced acute kidney injury.

CONCLUSIONS

Our data describe the relationship between vancomycin dose, Cmin, and AUC0-24 in pediatric patients. We demonstrated the feasibility of using first-order equations to estimate AUC0-24, using two concentrations obtained at steady state to monitor efficacy and safety in pediatric patients receiving intravenous vancomycin. Our data showed suboptimal correlation between AUC0-24 and Cmin, which indicates that Cmin should not be used as a surrogate marker for a therapeutic AUC0-24 in pediatric patients. In alignment with the 2020 vancomycin consensus guidelines, we suggest utilizing AUC0-24 for efficacy and safety monitoring.