Marko Britta, Palmowski Lars, Nowak Hartmuth, Witowski Andrea, Koos Björn, Rump Katharina, Bergmann Lars, Bandow Julia, Eisenacher Martin, Günther Patrick, Adamzik Michael, Sitek Barbara, Rahmel Tim
Klinik für Anästhesiologie, Intensivmedizin und Schmerztherapie, Universitätsklinikum Knappschaftskrankenhaus Bochum GmbH, Bochum, Germany.
Zentrum für Künstliche Intelligenz, Medizininformatik und Datenwissenschaften, Universitätsklinikum Knappschaftskrankenhaus Bochum GmbH, Bochum, Germany.
BMJ Open. 2024 Dec 12;14(12):e086094. doi: 10.1136/bmjopen-2024-086094.
In sepsis treatment, achieving and maintaining effective antibiotic therapy is crucial. However, optimal antibiotic dosing faces challenges due to significant variability among patients with sepsis. Therapeutic drug monitoring (TDM), the current gold standard, lacks initial dosage adjustments and global availability. Even with daily TDM, antibiotic serum concentrations (ASCs) often deviate from the therapeutic range. This study addresses these challenges by developing machine learning (ML)-based ASC prediction models capable of handling variable data input and encompassing diverse clinical, laboratory, microbiological and proteomic parameters without the need for daily TDM.
This prospective observational study is conducted in a German university hospital intensive care unit. Eligible sepsis patients receive continuous antibiotic therapy with piperacillin/tazobactam (n=100) or meropenem (n=100) within 24 hours. Exclusion criteria include refusal, pregnancy, lactation and severe anaemia (haemoglobin <8 g/dL). Blood samples for TDM are collected from patients, along with clinical and laboratory parameters on days 1-8 and day 30 or on discharge. ML models predicting ASC between day 1 and day 8 serve as primary and key secondary endpoints. We will use the collected data to develop multifaceted ML-based algorithms aimed at optimising antibiotic dosing in sepsis. Our two-way approach involves creating two distinct algorithms: the first focuses on predictive accuracy and generalisability using routine clinical parameters, while the second leverages an extended dataset including a plethora of factors currently insufficiently explored and not available in standard clinical practice but may help to enhance precision. Ultimately, these models are envisioned for integration into clinical decision support systems within patient data management systems, facilitating automated, personalised treatment recommendations for sepsis.
The study received approval from the Ethics Committee of the Medical Faculty of Ruhr-University Bochum (No. 23-7905). Findings will be disseminated through open-access publication in a peer-reviewed journal and social media channels.
DRKS00032970.
在脓毒症治疗中，实现并维持有效的抗生素治疗至关重要。然而，由于脓毒症患者之间存在显著差异，最佳抗生素剂量面临挑战。治疗药物监测（TDM）作为当前的金标准，缺乏初始剂量调整且全球可用性有限。即使每日进行TDM，抗生素血清浓度（ASC）仍常偏离治疗范围。本研究通过开发基于机器学习（ML）的ASC预测模型来应对这些挑战，该模型能够处理可变数据输入，并涵盖各种临床、实验室、微生物学和蛋白质组学参数，而无需每日进行TDM。
本前瞻性观察性研究在德国一家大学医院的重症监护病房进行。符合条件的脓毒症患者在24小时内接受哌拉西林/他唑巴坦（n = 100）或美罗培南（n = 100）的持续抗生素治疗。排除标准包括拒绝、妊娠、哺乳和严重贫血（血红蛋白<8 g/dL）。在第1 - 8天、第30天或出院时采集患者的TDM血样以及临床和实验室参数。预测第1天至第8天ASC的ML模型作为主要和关键次要终点。我们将使用收集的数据开发多方面的基于ML的算法，旨在优化脓毒症中的抗生素剂量。我们的双向方法包括创建两种不同的算法：第一种使用常规临床参数关注预测准确性和可推广性，而第二种利用扩展数据集，该数据集包括大量目前未充分探索且在标准临床实践中不可用但可能有助于提高精度的因素。最终，这些模型设想整合到患者数据管理系统中的临床决策支持系统中，为脓毒症提供自动化、个性化的治疗建议。
本研究获得了鲁尔大学波鸿医学院伦理委员会的批准（编号23 - 7905）。研究结果将通过在同行评审期刊上的开放获取出版物以及社交媒体渠道进行传播。
DRKS00032970。
