ArticleJournal of electrocardiology
Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics.
Article in Journal of electrocardiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
66 citing papers in PubMed, 6 syntheses or guidelines pooled it, 158 citations in OpenAlex.
- 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
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review.PloS one · 2021Pooled it
- Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.Frontiers in medicine · 2021Pooled it
- Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy.Intensive care medicine · 2020Pooled it
- Machine Learning-Based Prediction of Culture-Confirmed Neonatal Sepsis in a Tertiary Neonatal Intensive Care Unit: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- Early Sepsis Detection Using Heterogeneous Structured ICU Data with Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Lysosome-related biomarkers in peripheral blood immune cells discriminate sepsis from SIRS.Scientific reports · 2026Article
- Machine learning predicts sepsis deterioration trajectories.NPJ digital medicine · 2026Article
- Machine learning model for daily prediction of pediatric sepsis using Phoenix criteria.Pediatric research · 2026Article
- Chloremia Disturbances in Critical Care: A Narrative Review of Pathophysiology, Clinical Impact and Management Strategies.Life (Basel, Switzerland) · 2026Review
- Interpretable machine learning models using early dynamic clinical features to predict early hemodynamic stabilization after vasopressor initiation in patients with septic shock: development in MIMIC-IV and external validation in eICU.Frontiers in medicine · 2026Article
- Smart Imitator: Learning from Imperfect Clinical Decisions.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Prognostic Value of Tryptophanyl-tRNA Synthetase in Sepsis Combined with Kidney Dysfunction or Urinary Tract Infection: A Prospective Observational Study.Diagnostics (Basel, Switzerland) · 2025Article
- First Signs at Admission: Prognostic Value of Initial Proinflammatory Markers for Sepsis and Mortality in ICU Patients.Pathogens (Basel, Switzerland) · 2025Article
- ChronoSynthNet: a dual-task deep learning model development and validation study for predicting real-time norepinephrine dosage and the early detection of hypotension in patients with septic shock.Cardiovascular diagnosis and therapy · 2025Article
- The Potential of Artificial Intelligence in the Diagnosis and Prognosis of Sepsis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Trends and management of acute respiratory failure in hospitalized patients: a multicenter retrospective study in South Korea.Acute and critical care · 2025Article
- Exploring the Potentials of Artificial Intelligence in Sepsis Management in the Intensive Care Unit.Critical care research and practice · 2025Review
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Sepsis remains a leading cause of morbidity and mortality among intensive care unit (ICU) patients. For each hour treatment initiation is delayed after diagnosis, sepsis-related mortality increases by approximately 8%. Therefore, maximizing effective care requires early recognition and initiation of treatment protocols. Antecedent signs and symptoms of sepsis can be subtle and unrecognizable (e.g., loss of autonomic regulation of vital signs), causing treatment delays and harm to the patient. In this work we investigated the utility of high-resolution blood pressure (BP) and heart rate (HR) times series dynamics for the early prediction of sepsis in patients from an urban, academic hospital, meeting the third international consensus definition of sepsis (sepsis-III) during their ICU admission. Using a multivariate modeling approach we found that HR and BP dynamics at multiple time-scales are independent predictors of sepsis, even after adjusting for commonly measured clinical values and patient demographics and comorbidities. Earlier recognition and diagnosis of sepsis has the potential to decrease sepsis-related morbidity and mortality through earlier initiation of treatment protocols.
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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.