Abstract：
Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients are hindered by small patient groups, variability between studies, patient heterogeneity, and inadequate use of TDM. Accordingly, definitive conclusions regarding the efficacy of TDM remain elusive. To address these challenges, we propose an innovative approach that leverages data-driven methods to unveil the concealed connections between therapy effectiveness and patient data, collected through a randomized controlled trial (DRKS00011159; 10th October 2016). Our findings reveal that machine learning algorithms can successfully identify informative features that distinguish between healthy and sick states. These hold promise as potential markers for disease classification and severity stratification, as well as offering a continuous and data-driven \"multidimensional\" Sequential Organ Failure Assessment (SOFA) score. The positive impact of TDM on patient recovery rates is demonstrated by unraveling the intricate connections between therapy effectiveness and clinically relevant data via machine learning.
摘要：
研究β-内酰胺治疗药物监测（TDM）在危重患者中的益处的临床研究受到小患者群体的阻碍，研究之间的差异，患者异质性，以及TDM的使用不足。因此，关于TDM疗效的确切结论仍然难以捉摸。为了应对这些挑战，我们提出了一种创新的方法，利用数据驱动的方法来揭示治疗有效性和患者数据之间的隐藏联系，通过一项随机对照试验（DRKS00011159；2016年10月10日）收集。我们的发现表明，机器学习算法可以成功地识别出区分健康和生病状态的信息特征。这些有望成为疾病分类和严重程度分层的潜在标志，以及提供连续和数据驱动的“多维”序贯器官衰竭评估（SOFA）评分。通过机器学习揭示治疗有效性和临床相关数据之间的复杂联系，证明了TDM对患者恢复率的积极影响。
