关键词: Clinical decision support system Individual treatment Machine-learning Neonates β-lactam antibiotics

Mesh : Humans beta-Lactams / administration & dosage therapeutic use Infant, Newborn Machine Learning Decision Support Systems, Clinical Anti-Bacterial Agents / therapeutic use administration & dosage Neonatal Sepsis / drug therapy diagnosis Microbial Sensitivity Tests Algorithms

来  源:   DOI:10.1016/j.ebiom.2024.105221   PDF(Pubmed)

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.
RESULTS: 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.
CONCLUSIONS: An ML-based CDSS was successfully constructed to assist clinicians in selecting optimal β-lactam antibiotic doses.
BACKGROUND: 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.
摘要:
背景:准确预测新生儿败血症中β-内酰胺抗生素的最佳剂量具有挑战性。我们旨在评估基于机器学习(ML)的可靠临床决策支持系统(CDSS)是否可以帮助临床医生做出最佳剂量选择。
方法:五种β-内酰胺类抗生素(阿莫西林,头孢他啶,头孢噻肟,美罗培南和latamoxef),常用于治疗新生儿败血症,被选中。CDSS是通过掺入药物来构建的,病人,剂量,药效学,和微生物因素。CatBoostML算法用于构建CDSS。使用真实世界研究来评估CDSS性能。虚拟试验用于比较CDSS优化剂量与指南推荐剂量。
结果:对于特定药物,通过输入患者特征和药效学(PD)目标(游离药物浓度高于最小抑制浓度[fT>MIC]的时间的50%/70%/100%),CDSS可以确定计划的给药方案是否会达到PD目标,并建议最佳剂量.在现实世界验证中,所有五种药物的预测准确性均>80.0%。与PopPK模型相比,整体精度,精度,召回,F1-Score提高了10.7%,22.1%,64.2%,和43.1%,分别。使用CDSS优化的剂量,与指南推荐剂量相比,达到目标浓度的平均概率增加了58.2%.
结论:成功构建了基于ML的CDSS,以帮助临床医生选择最佳β-内酰胺抗生素剂量。
背景:这项工作得到了国家自然科学基金、山东大学杰出青年学者、国家重点研究发展计划的支持。
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