Observational studyClinical and translational science2021
Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.
Observational study in Clinical and translational science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 6 of them syntheses that pooled it.
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
26 citing papers in PubMed, 6 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- AI-Based Sepsis Prediction in Hospitalized Adults: Systematic Review, Subgroup Meta-Analysis, and Contextual Analysis of Clinical Burden.Journal of medical Internet research · 2026Pooled it
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled it
- Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis.Critical care explorations · 2025Pooled it
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Cerebrospinal Fluid Biomarkers for Diagnosis and the Prognostication of Acute Ischemic Stroke: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Recent advances in the biosensing platforms for sepsis diagnosis.RSC advances · 2026Review
- Challenges in early detection and prognostication of sepsis: new approaches from the emergency department and intensive care unit.EClinicalMedicine · 2026Review
- Association of antibiotic type and timing with sepsis mortality using target trial emulation.Scientific reports · 2026Article
- Identification of Monocyte CD11c as a Potential Biomarker for Distinguishing Sepsis From Non-Infectious Inflammation and Predicting the Outcome of Sepsis.Journal of inflammation research · 2026Article
- Development and Validation of a Predictive Model Using Logistic Regression and Machine Learning for Carotid Artery Atherosclerosis Risk in Postmenopausal Han Women from Northern China: A Retrospective Case-Control Study.International journal of women's health · 2026Article
- Hyper-inflammation and immunosuppression: redefining sepsis therapy using modern approaches.Frontiers in pharmacology · 2026Review
- Machine-learning based calculator for personalized risk assessment following ST-elevation myocardial infarction.BMC cardiovascular disorders · 2025Article
- Review
- Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management.Diagnostics (Basel, Switzerland) · 2025Review
- The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.Annals of translational medicine · 2025Review
- Development and Validation of a Machine Learning Model for Early Prediction of Sepsis Onset in Hospital Inpatients from All Departments.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Sepsis Management: An Overview for Clinicians.Journal of clinical medicine · 2025Review
- Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model.Frontiers in physiology · 2025Article
- Personalized machine learning-based prognostic model for ICU-acquired bloodstream infections.Frontiers in cellular and infection microbiology · 2025Article
Corrections and comments
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Authors and funding
30 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30-day mortality, and 3-day inpatient re-admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high-risk groups showed significant differences in LOS (p < 0.0001), 30-day mortality (p < 0.0001), and 30-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.
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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.