Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP)

Abstract

Introduction: 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.

Methods: 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.

Ethics and dissemination: 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.

Trial registration number: DRKS00032970.

Keywords: Artificial Intelligence; INTENSIVE & CRITICAL CARE; Machine Learning.

Figure 1. Study flow of the trial. After screening and obtaining consent, blood samples are collected on days 1–8 and on day 30 (or at discharge if earlier). These samples undergo therapeutic drug monitoring and an extended assessment focusing on drug interactions and proteomics, along with an immunological characterisation. Two ML models will be developed. One model will be trained and validated using the prospectively recruited cohort in this study (n=200) and the existing Sepsis.DataNet cohort (n=500). The other model will incorporate additional variables determined exclusively in our cohort. ASC, antibiotic serum concentration; ML, machine learning.

Figure 2. Calculated sample size for a linear regression model with a significance level of 5%, power of 80% and effect size f2=0.35 for varying numbers of variables in the model.

Figure 3. Data from WPs 1–4 will be harmonised in WP 5. In WP 6, there will be the utilisation of the Sepsis.DataNet Cohort, integrative data analysis and AI model development. The focus lies on creating multiple AI models from both the main dataset and the extended dataset. During this process, variables will be selected through feature selection. These models are intended to be utilised in WP seven as prototypes for an Antibiotic Dosage Recommendation Tool. AI, artificial intelligence; TDM, therapeutic drug monitoring; WP, work package.