Optimal use of β-lactams in neonates: machine learning-based clinical decision support system

Abstract

Background: Accurate prediction of the optimal dose for β-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliable clinical decision support system (CDSS) based on machine learning (ML) can assist clinicians in making optimal dose selections.

Methods: Five β-lactam antibiotics (amoxicillin, ceftazidime, cefotaxime, meropenem and latamoxef), commonly used to treat neonatal sepsis, were selected. The CDSS was constructed by incorporating the drug, patient, dosage, pharmacodynamic, and microbiological factors. The CatBoost ML algorithm was used to build the CDSS. Real-world studies were used to evaluate the CDSS performance. Virtual trials were used to compare the CDSS-optimized doses with guideline-recommended doses.

Findings: For a specific drug, by entering the patient characteristics and pharmacodynamic (PD) target (50%/70%/100% fraction of time that the free drug concentration is above the minimal inhibitory concentration [fT > MIC]), the CDSS can determine whether the planned dosing regimen will achieve the PD target and suggest an optimal dose. The prediction accuracy of all five drugs was >80.0% in the real-world validation. Compared with the PopPK model, the overall accuracy, precision, recall, and F1-Score improved by 10.7%, 22.1%, 64.2%, and 43.1%, respectively. Using the CDSS-optimized doses, the average probability of target concentration attainment increased by 58.2% compared to the guideline-recommended doses.

Interpretation: An ML-based CDSS was successfully constructed to assist clinicians in selecting optimal β-lactam antibiotic doses.

Funding: This work was supported by the National Natural Science Foundation of China; Distinguished Young and Middle-aged Scholar of Shandong University; National Key Research and Development Program of China.

Keywords: Clinical decision support system; Individual treatment; Machine-learning; Neonates; β-lactam antibiotics.

Fig. 1

Diagram illustrating the construction, evaluation, optimization, and application of the clinical decision support system. CDSS: Clinical Decision Support System; PopPK: population pharmacokinetic; ML: machine learning; CL: clearance; V1: central volume of distribution; V2: peripheral volume of distribution; Q: inter-compartment clearance.

Fig. 2

Nested cross-validation method used in this study.

Fig. 3

SHAP results of model covariates. Each point represents a sample. A higher intensity of the red color indicates a more relevant covariate whereas a higher intensity of the blue color indicates a less relevant covariate. If most SHAP values of the red points are located in the area greater than 0, and most SHAP values of the blue points are located in the area less than 0, it means that the covariate has a positive correlation with the target variable and vice versa. BW: birth weight; CW: current weight; GA: gestational age at birth; PMA: postmenstrual age; PNA: postnatal age; CREA: Serum creatinine concentration; ALB: albumin. (A) Ceftazidime; (B) cefotaxime; (C) meropenem; (D) latamoxef; (E) amoxicillin.

Fig. 4

Schematic diagram to illustrate how the clinical decision support system would be applied in clinical practice. CDSS: Clinical Decision Support System; PD: pharmacodynamics; CW: current weight; BW: birth weight; GA: gestational age; PNA: postnatal age; PMA: postmenstrual age; CREA: serum creatinine.