ArticleNPJ digital medicine2025
Streamlined machine learning model for early sepsis risk prediction in burn patients.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Artificial Intelligence in Burn and Wound Care: Image Analysis, Prediction, and Clinical Integration.Journal of burn care & research : official publication of the American Burn Association · 2026Review
- Infections in Burn Patients: Pathophysiology, Prevention, and Contemporary Therapeutic Strategies in the Era of Antimicrobial Resistance.Infectious diseases and therapy · 2026Review
- An Introduction to AI for Clinicians: Tutorial.Interactive journal of medical research · 2026Article
- A Causal and interpretable machine learning framework for postcranioplasty risk prediction and surgical decision support.NPJ digital medicine · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is the leading cause of mortality in burn patients, yet early identification remains difficult due to persistent hyperinflammatory responses and altered baseline physiology. We developed a streamlined machine learning model for early sepsis risk prediction in burn patients using data from 6629 patients across 11 centers participating in the German Burn Registry. The model was trained using only six admission-level variables (age, burned body surface area, deep partial-thickness burns, full-thickness burns, inhalation injury, and hypertension), selected through multiple feature selection methods and evaluated using cross-validated machine learning pipelines. The final Random Forest model achieved an AUROC of 0.91, sensitivity of 0.81, specificity of 0.85, and a negative predictive value of 0.98, enabling reliable early risk stratification immediately upon ICU admission. By relying solely on admission-level variables, this model offers a reliable and interpretable solution for early sepsis risk detection in burn patients, supporting timely interventions and potentially improving critical care outcomes.
Identifiers
What OpenQuestion holds
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.