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Shock, Septic

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Developing and validating a machine learning model to predict multidrug-resistant -related septic shock.

Frontiers in immunology 39867898
BACKGROUND: Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine lea...
Adult Aged Drug Resistance, Multiple, Bacterial Female Humans Klebsiella Infections Klebsiella pneumoniae Machine Learning Male Middle Aged Retrospective Studies Risk Assessment Risk Factors Shock, Septic
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Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study.

Scientific reports 40328810
Sepsis, septic shock, and cardiogenic shock are life-threatening conditions associated with high mortality rates, but differentiating them is complex because they share certain symptoms. Using the Medical Information Mart for Intensive Care (MIMIC)-I...
Adult Aged Artificial Intelligence Bayes Theorem Female Humans Male Middle Aged Neural Networks, Computer Retrospective Studies Sepsis Shock, Cardiogenic Shock, Septic
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Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework.

Frontiers in immunology 39669564
BACKGROUND: Sepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcrip...
Biomarkers Computational Biology Female Gene Expression Profiling Humans Killer Cells, Natural Machine Learning Male Prognosis Sepsis Shock, Septic Transcriptome
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Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning.

Hereditas 39609718
PURPOSE: Mitochondria are involved in septic shock and inflammatory response syndrome, which severely affects the life security of patients. It is necessary to recognize and explore the immune-mitochondrial genes in septic shock.
Computational Biology Databases, Genetic Gene Expression Profiling Gene Ontology Gene Regulatory Networks Humans Machine Learning Metabolic Diseases Mitochondria Mitochondrial Diseases Protein Interaction Maps Shock, Septic
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Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.

Journal of pediatric hematology/oncology 39641618
OBJECTIVES: This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in pediatric patients with cancer who presenting febrile neutropenia (FN) at emergency department (ED) v...
Adolescent Bacteremia Child Child, Preschool Emergency Service, Hospital Febrile Neutropenia Female Humans Infant Machine Learning Male Neoplasms Prognosis Retrospective Studies Shock, Septic
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PROGNOSTIC ACCURACY OF MACHINE LEARNING MODELS FOR IN-HOSPITAL MORTALITY AMONG CHILDREN WITH PHOENIX SEPSIS ADMITTED TO THE PEDIATRIC INTENSIVE CARE UNIT.

Shock (Augusta, Ga.) 39671551
Objective: The Phoenix sepsis criteria define sepsis in children with suspected or confirmed infection who have ≥2 in the Phoenix Sepsis Score. The adoption of the Phoenix sepsis criteria eliminated the Systemic Inflammatory Response Syndrome criteri...
Adolescent Child Child, Preschool Female Hospital Mortality Humans Infant Intensive Care Units, Pediatric Machine Learning Male Prognosis Retrospective Studies Sepsis Shock, Septic
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Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study.

BMC medical research methodology 39987431
BACKGROUND: Classical approaches to subgroup analysis in randomised controlled trials (RCTs) to identify heterogeneous treatment effects (HTEs) involve testing the interaction between each pre-specified possible treatment effect modifier and the trea...
Female Humans Machine Learning Male Norepinephrine Potassium Randomized Controlled Trials as Topic Shock, Septic Treatment Effect Heterogeneity Treatment Outcome
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Scale to predict risk for refractory septic shock based on a hybrid approach using machine learning and regression modeling.

Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias 39898942
OBJECTIVE: To develop a scale to predict refractory septic shock (SS) based on clinical variables recorded during initial evaluations of patients.
Aged Emergency Service, Hospital Female Humans Intensive Care Units Machine Learning Male Middle Aged Norepinephrine Registries Regression Analysis Retrospective Studies Risk Assessment ROC Curve Shock, Septic
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Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock.

Scientific reports 40140612
This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. Three gene expression datasets were accessible from the Gene Expression Omnibus repository. The differentially expressed g...
Biomarkers Child Computational Biology Female Gene Expression Profiling Humans Machine Learning Male Matrix Metalloproteinase 8 ROC Curve Shock, Septic
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Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study.

Scientific reports 40021734
This study aimed to develop an interpretable machine learning model to predict methylene blue (MB) responsiveness in adult patients with refractory septic shock and to identify key factors influencing MB responsiveness using the SHapley Additive exPl...
Adult Aged Female Humans Machine Learning Male Methylene Blue Middle Aged Retrospective Studies Shock, Septic Support Vector Machine Treatment Outcome
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