Predicting Ketamine Exposure in Pediatric Status Epilepticus: A Pharmacokinetic-Machine Learning Approach

Status epilepticus is a life-threatening neurological emergency. Ketamine combined with levetiracetam is a promising therapy being investigated for established status epilepticus. Early drug exposure estimates could inform dosing, but intensive pharmacokinetic sampling is challenging in pediatric emergency settings due to logistical and ethical constraints. Existing population pharmacokinetic methods with Bayesian forecasting require multiple samples per individual, which is difficult in this context. The objective of this work was to develop a framework that utilizes population pharmacokinetic-simulated datasets as inputs for machine learning models to predict early ketamine exposure in pediatric status epilepticus.

We simulated 100,000 concentration-time profiles with covariates using a published population pharmacokinetic model. Additional predictor variables were simulated using distributions from a previously published status epilepticus trial. The simulated data were used to train machine learning algorithms to predict early exposure (AUC_0-2h) using two samples per individual. The machine learning models were validated internally.

We evaluated four machine learning algorithms, including LASSO, random forest (RF), K-nearest neighbor (KNN), and gradient boosted machine (GBM). The ensemble models (GBM and RF) achieved superior performance, with internal validation results indicating no overfitting. The root mean square errors for RF and GBM were 0.183 mg h/L and 0.185 mg h/L respectively, while mean absolute errors of RF and GBM were 0.103 mg h/L and 0.102 mg h/L, respectively CONCLUSIONS: We demonstrate the feasibility of developing population pharmacokinetic-informed machine learning models to accurately predict early ketamine exposures in pediatric status, where pharmacokinetic sampling is a challenge.