---
title: "Machine learning prediction of initial vancomycin serum concentrations in septic ICU patients"
id: "pubmed-42776357"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42776357"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42776357/"
doi: "10.1186/s40635-026-00973-7"
published_at: "2026-09-23T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Machine learning prediction of initial vancomycin serum concentrations in septic ICU patients
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42776357
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42776357/)
- **DOI:** [10.1186/s40635-026-00973-7](https://doi.org/10.1186%2Fs40635-026-00973-7)
- **Published At:** 2026-09-23T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Sepsis causes marked pharmacokinetic variability that complicates empiric dosing of **vancomycin**, risking underdosing (resistance) or overdosing (toxicity). Routine therapeutic drug monitoring can be delayed, so early individualized prediction is valuable. - This single-center retrospective study used electronic medical record data from adult ICU sepsis patients who received a vancomycin loading dose followed by continuous infusion and had at least one measured serum concentration. - Three machine learning algorithms were developed to predict the initial vancomycin serum concentration: **elastic net regression**, random forest, and XGBoost. Training and evaluation used stratified nested cross-validation to reduce bias and improve generalizability. - Model performance was compared to seven commonly used population pharmacokinetic (PK) models. The best-performing ML model (elastic net) achieved an average RMSE of 6.19 versus 7.83 for the top PK model, indicating improved accuracy. - Key variables that influenced the elastic net model included administration of **noradrenaline**, body weight, serum creatinine, and presence of chronic kidney disease (CKD), alongside classical pharmacokinetic parameters. - The ML approach enabled prediction of initial concentrations before therapeutic drug monitoring results were available, supporting earlier individualized dosing decisions in sepsis care. - The study used routinely collected clinical data from a single center and applied robust cross-validation but is retrospective; external validation and prospective testing were not reported in the abstract. - Ethical approval was granted by the Ruhr-University Bochum ethics committee (registry No. 18-6606-BR). The authors declared no competing interests. - Overall, the study suggests that **machine learning** models can outperform conventional PK models for early vancomycin concentration prediction and may support data-driven antimicrobial dosing in critically ill septic patients.
## Clinical Analysis & Structured Key Points
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Development and evaluation of machine learning-based prediction-modelling for initial vancomycin serum concentrations in septic ICU patients using clinical health record data [Robin Grugel](https://pubmed.ncbi.nlm.nih.gov/?term=Grugel+R&cauthor_id=42776357)[#](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-equal-contrib-explanation "Contributed equally")[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-1 "Medical Faculty, Medizinisches Proteom-Center \(MPC\), Ruhr University Bochum, 44801, Bochum, Germany.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-2 "Medical Faculty, Center for Protein Diagnostics \(ProDi\), Ruhr University Bochum, 44801, Bochum, Germany."), [Britta Westhus](https://pubmed.ncbi.nlm.nih.gov/?term=Westhus+B&cauthor_id=42776357)[#](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-equal-contrib-explanation "Contributed equally")[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-3 "Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany."), [Hartmuth Nowak](https://pubmed.ncbi.nlm.nih.gov/?term=Nowak+H&cauthor_id=42776357)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-3 "Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-4 "Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, Center for Artificial Intelligence, Medical Informatics and Data Science, 44892, Bochum, Germany."), [Michael Adamzik](https://pubmed.ncbi.nlm.nih.gov/?term=Adamzik+M&cauthor_id=42776357)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-3 "Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany."), [Tim Rahmel](https://pubmed.ncbi.nlm.nih.gov/?term=Rahmel+T&cauthor_id=42776357)[#](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-equal-contrib-explanation "Contributed equally")[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-5 "Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany. tim.rahmel@ruhr-uni-bochum.de."), [Martin Eisenacher](https://pubmed.ncbi.nlm.nih.gov/?term=Eisenacher+M&cauthor_id=42776357)[#](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-equal-contrib-explanation "Contributed equally")[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-1 "Medical Faculty, Medizinisches Proteom-Center \(MPC\), Ruhr University Bochum, 44801, Bochum, Germany.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-2 "Medical Faculty, Center for Protein Diagnostics \(ProDi\), Ruhr University Bochum, 44801, Bochum, Germany.")[ 6 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-6 "Medical Faculty, Core Unit Bioinformatics \(CUBiMed.RUB\), Ruhr University Bochum, Bochum, Germany.")[ 7 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#full-view-affiliation-7 "Medical Faculty, BioInfra.Prot, Deutsches Netzwerk für Bioinformatik-Infrastruktur \(de.NBI\), Ruhr University Bochum, Bochum, Germany.") Affiliations Expand ### Affiliations * 1 Medical Faculty, Medizinisches Proteom-Center (MPC), Ruhr University Bochum, 44801, Bochum, Germany. * 2 Medical Faculty, Center for Protein Diagnostics (ProDi), Ruhr University Bochum, 44801, Bochum, Germany. * 3 Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany. * 4 Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, Center for Artificial Intelligence, Medical Informatics and Data Science, 44892, Bochum, Germany. * 5 Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany. tim.rahmel@ruhr-uni-bochum.de. * 6 Medical Faculty, Core Unit Bioinformatics (CUBiMed.RUB), Ruhr University Bochum, Bochum, Germany. * 7 Medical Faculty, BioInfra.Prot, Deutsches Netzwerk für Bioinformatik-Infrastruktur (de.NBI), Ruhr University Bochum, Bochum, Germany. # Contributed equally. * PMID: **42776357** * DOI: [ 10.1186/s40635-026-00973-7 ](https://doi.org/10.1186/s40635-026-00973-7) Item in Clipboard # Development and evaluation of machine learning-based prediction-modelling for initial vancomycin serum concentrations in septic ICU patients using clinical health record data Robin Grugel et al. Intensive Care Med Exp. 2026. Show details Display options Display options Format Abstract PubMed PMID Intensive Care Med Exp Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Intensive+Care+Med+Exp%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Intensive+Care+Med+Exp%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42776357/) . 2026 Sep 23;14(1):125. doi: 10.1186/s40635-026-00973-7. ### Authors [Robin Grugel](https://pubmed.ncbi.nlm.nih.gov/?term=Grugel+R&cauthor_id=42776357)[#](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-equal-contrib-explanation "Contributed equally")[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-1 "Medical Faculty, Medizinisches Proteom-Center \(MPC\), Ruhr University Bochum, 44801, Bochum, Germany.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-2 "Medical Faculty, Center for Protein Diagnostics \(ProDi\), Ruhr University Bochum, 44801, Bochum, Germany."), [Britta Westhus](https://pubmed.ncbi.nlm.nih.gov/?term=Westhus+B&cauthor_id=42776357)[#](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-equal-contrib-explanation "Contributed equally")[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-3 "Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany."), [Hartmuth Nowak](https://pubmed.ncbi.nlm.nih.gov/?term=Nowak+H&cauthor_id=42776357)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-3 "Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-4 "Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, Center for Artificial Intelligence, Medical Informatics and Data Science, 44892, Bochum, Germany."), [Michael Adamzik](https://pubmed.ncbi.nlm.nih.gov/?term=Adamzik+M&cauthor_id=42776357)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-3 "Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany."), [Tim Rahmel](https://pubmed.ncbi.nlm.nih.gov/?term=Rahmel+T&cauthor_id=42776357)[#](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-equal-contrib-explanation "Contributed equally")[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-5 "Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany. tim.rahmel@ruhr-uni-bochum.de."), [Martin Eisenacher](https://pubmed.ncbi.nlm.nih.gov/?term=Eisenacher+M&cauthor_id=42776357)[#](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-equal-contrib-explanation "Contributed equally")[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-1 "Medical Faculty, Medizinisches Proteom-Center \(MPC\), Ruhr University Bochum, 44801, Bochum, Germany.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-2 "Medical Faculty, Center for Protein Diagnostics \(ProDi\), Ruhr University Bochum, 44801, Bochum, Germany.")[ 6 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-6 "Medical Faculty, Core Unit Bioinformatics \(CUBiMed.RUB\), Ruhr University Bochum, Bochum, Germany.")[ 7 ](https://pubmed.ncbi.nlm.nih.gov/42776357/#short-view-affiliation-7 "Medical Faculty, BioInfra.Prot, Deutsches Netzwerk für Bioinformatik-Infrastruktur \(de.NBI\), Ruhr University Bochum, Bochum, Germany.") ### Affiliations * 1 Medical Faculty, Medizinisches Proteom-Center (MPC), Ruhr University Bochum, 44801, Bochum, Germany. * 2 Medical Faculty, Center for Protein Diagnostics (ProDi), Ruhr University Bochum, 44801, Bochum, Germany. * 3 Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany. * 4 Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, Center for Artificial Intelligence, Medical Informatics and Data Science, 44892, Bochum, Germany. * 5 Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, 44892, Bochum, Germany. tim.rahmel@ruhr-uni-bochum.de. * 6 Medical Faculty, Core Unit Bioinformatics (CUBiMed.RUB), Ruhr University Bochum, Bochum, Germany. * 7 Medical Faculty, BioInfra.Prot, Deutsches Netzwerk für Bioinformatik-Infrastruktur (de.NBI), Ruhr University Bochum, Bochum, Germany. # Contributed equally. * PMID: **42776357** * DOI: [ 10.1186/s40635-026-00973-7 ](https://doi.org/10.1186/s40635-026-00973-7) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract **Background:** Sepsis remains a life-threatening condition with highly heterogeneous and dynamic pathophysiology, limiting the effectiveness of uniform therapeutic strategies. Beyond timely source control, antimicrobial therapy represents the only causal treatment option. Vancomycin is widely used for treatment of Gram-positive infections; however, optimal dosing in septic patients is challenging due to pronounced pharmacokinetic variability and substantial interindividual heterogeneity. Underdosing may promote antimicrobial resistance, whereas overdosing increases the risk of toxicity. This study aimed to develop and validate a machine learning-based prediction model to support individualized vancomycin dosing using routinely available clinical data. **Methods:** This single-center retrospective study included adult sepsis patients admitted to the intensive care unit, using routinely collected data from the hospital's electronic medical records. Patients were eligible if they received a vancomycin loading dose followed by continuous infusion and had at least one measured serum concentration. Three machine learning models-elastic net regression, random forest, and XGBoost-were developed to predict the initial vancomycin serum concentration. To minimize bias and enhance generalizability, model training, hyperparameter tuning, and performance evaluation were conducted using a stratified nested cross-validation approach. Model performance was compared with seven commonly used population pharmacokinetic models. **Results:** The developed best performing elastic net model achieved a notable improvement with an average RMSE of 6.19, compared to 7.83 for the best pharmacokinetic model highlighting the potential of early and individualized dosing supported by a machine learning model. Final model analysis revealed that noradrenaline administration, together with classical pharmacokinetic parameters including body weight, serum creatinine, and the presence of chronic kidney disease, significantly influenced predictive performance. **Conclusions:** This machine learning-based approach for predicting initial vancomycin serum concentrations outperforms conventional PK models and enables more precise, individualized dosing prior to the availability of therapeutic drug monitoring results. By integrating key clinical variables, the model facilitates data-driven decision-making in sepsis care and underscores the potential of machine learning to advance personalized antimicrobial therapy. **Keywords:** Machine learning; Prediction; Sepsis; Vancomycin. © 2026. The Author(s). [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declarations. Ethics approval and consent to participate: The SepsisDataNet.NRW study received approval from the ethics committee of the Ethics Committee of the Medical Faculty of Ruhr-University Bochum (registry No. 18-6606-BR) and was subsequently endorsed by the local ethics committees of each participating center. This work was supported by the Core Unit Bioinformatics at the Medical Faculty of the Ruhr University Bochum (CUBiMed.RUB). Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests. ## References 1. 1. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche J-D, Coopersmith CM et al (2016) The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315(8):801–810 - [PubMed](https://pubmed.ncbi.nlm.nih.gov/26903338/) - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/4968574/) - [DOI](https://doi.org/10.1001/jama.2016.0287) 2. 1. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, Colombara DV, Ikuta KS, Kissoon N, Finfer S, Fleischmann-Struzek C, Machado FR, Reinhart
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