ArticleJournal of critical care2024
Determining steady-state trough range in vancomycin drug dosing using machine learning.
Article in Journal of critical care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration.Kidney360 · 2026Review
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Nephrocast-V: A Deep Learning Model for the Prediction of Vancomycin Trough Concentration Using Electronic Health Record Data.Pharmacotherapy · 2026Article
- Prediction of Meropenem Target Attainment in Critically Ill Patients Using Routine Clinical Data from a Single Center: A Machine Learning Approach.Infection and drug resistance · 2026Article
- Hybrid Population Pharmacokinetic-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease.Clinical pharmacokinetics · 2025Article
- Hybrid Population PK-Machine Learning Modeling to Predict Infliximab Pharmacokinetics in Pediatric and Young Adult Patients with Crohn's Disease.bioRxiv : the preprint server for biology · 2025Article
- PK/PD-Guided Strategies for Appropriate Antibiotic Use in the Era of Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2025Review
- Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques.Pharmaceutical research · 2025Article
- Artificial intelligence in nephrology education: a multicenter survey of fellowship trainees at Mayo Clinic.Frontiers in nephrology · 2025Article
- Can we use artificial intelligence to better treat acute kidney injury?Intensive care medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundVancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow therapeutic window, widely used in intensive care units (ICU). We aimed to predict the risk of inappropriate vancomycin trough levels and appropriate dosing for each ICU patient.
methodsObserved vancomycin trough levels were categorized into sub-therapeutic, therapeutic, and supra-therapeutic levels to train and compare different classification models. We included adult ICU patients (≥ 18 years) with at least one vancomycin concentration measurement during hospitalization at Mayo Clinic, Rochester, MN, from January 2007 to December 2017.
resultThe final cohort consisted of 5337 vancomycin courses. The XGBoost models outperformed other machine learning models with the AUC-ROC of 0.85 and 0.83, specificity of 53% and 47%, and sensitivity of 94% and 94% for sub- and supra-therapeutic categories, respectively. Kinetic estimated glomerular filtration rate and other creatinine-based measurements, vancomycin regimen (dose and interval), comorbidities, body mass index, age, sex, and blood pressure were among the most important variables in the models.
conclusionWe developed models to assess the risk of sub- and supra-therapeutic vancomycin trough levels to improve the accuracy of drug dosing in critically ill patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.