AIMC Topic: Sepsis

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Serial change of C1 inhibitor in patients with sepsis--a preliminary report.

The American journal of emergency medicine
OBJECTIVE: C1 inhibitor (C1INH) regulates not only the complement system but also the plasma kallikrein-kinin, fibrinolytic, and coagulation systems. The biologic activities of C1INH can be divided into the regulation of vascular permeability and ant...

Markers of endothelial damage and coagulation impairment in patients with severe sepsis resuscitated with hydroxyethyl starch 130/0.42 vs Ringer acetate.

Journal of critical care
PURPOSE: The Scandinavian Starch for Severe Sepsis/Septic Shock (6S) trial showed increased mortality in patients resuscitated with hydroxyethyl starch 130/0.42 (HES) vs Ringer acetate. Different effects of the fluids on the endothelium may have cont...

Learning a Severity Score for Sepsis: A Novel Approach based on Clinical Comparisons.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Sepsis is one of the leading causes of death in the United States. Early administration of treatment has been shown to decrease sepsis-related mortality and morbidity. Existing scoring systems such as the Acute Physiology and Chronic Health Evaluatio...

CLaI: Collaborative Learning and Inference for Low-Resolution Physiological Signals: Validation in Clinical Event Detection and Prediction.

IEEE transactions on bio-medical engineering
While machine learning (ML) techniques have been applied to detection and prediction tasks in clinical data, most methods rely on high-resolution data, which is not routinely available in most Intensive Care Units (ICUs), and perform poorly when face...

Impact of analytical bias on machine learning models for sepsis prediction using laboratory data.

Clinical chemistry and laboratory medicine
OBJECTIVES: Machine learning (ML) models, using laboratory data, support early sepsis prediction. However, analytical bias in laboratory measurements can compromise their performance and validity in real-world settings. We aimed to evaluate how analy...

SBC-SHAP: Increasing the Accessibility and Interpretability of Machine Learning Algorithms for Sepsis Prediction.

The journal of applied laboratory medicine
BACKGROUND: Sepsis is a life-threatening condition that is one of the major causes of death worldwide. Early detection of sepsis is required for fast initialization of an appropriate therapy. Complete blood count data containing information about whi...

[Pay attention to the application value of artificial intelligence in the diagnosis, treatment and analysis of sepsis].

Zhonghua yi xue za zhi
In critical care medicine, sepsis management represents a critical barrier to improving clinical outcomes, primarily due to the disease's profound heterogeneity and the current inability to optimally identify patient subgroups benefiting from persona...

Combination of machine learning and protein‑protein interaction network established one ATM‑DPP4‑TXN ferroptotic diagnostic model with experimental validation.

Molecular medicine reports
Ferroptosis and lethal sepsis are interlinked, although this association remains largely unknown to clinical panels. Sepsis is characterized by dysfunction of the inflammatory microenvironment. Most septic biomarkers lack independent validation, and ...

Sepsis criteria and kidney function: eliminating sex, age and economic status biases.

Nature reviews. Nephrology
The kidney is a target organ for the dysregulated host response to infection that defines sepsis, and acute kidney injury (AKI) is often an early manifestation of this response. Current sepsis criteria for adults (Sepsis-3) continue to include outmod...