AIMC Topic: Shock, Septic

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Clinical Decision Support for Septic Shock in the Emergency Department: A Cluster Randomized Trial.

Pediatrics Jul 1, 2025
BACKGROUND AND OBJECTIVES: Delays in septic shock diagnosis cause preventable mortality in children. Evidence is limited around early recognition strategies. The hypothesis was that clinical decision support (CDS) based on machine-learning predictive...
Humans Machine Learning Anti-Bacterial Agents Prospective Studies Female Male Infant Adolescent Child, Preschool Child Decision Support Systems, Clinical Shock, Septic Emergency Service, Hospital Time-to-Treatment
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Early Prediction of Septic Shock in Emergency Department Using Serum Metabolites.

Journal of the American Society for Mass Spectrometry Jun 4, 2025
Early recognition of septic shock is crucial for improving clinical management and patient outcomes, especially in the emergency department (ED). This study conducted serum metabolomic profiling on ED patients diagnosed with septic shock (n = 32) and...
Humans Machine Learning Algorithms Biomarkers Middle Aged Shock, Septic Mass Spectrometry Metabolomics Metabolome Female Aged Adult Male Emergency Service, Hospital Early Diagnosis
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Exploring treatment effects and fluid resuscitation strategies in septic shock: a deep learning-based causal inference approach.

Scientific reports May 25, 2025
Septic shock exhibits diverse etiologies and patient characteristics, necessitating tailored fluid management. We aimed to compare resuscitation strategies using normal saline, Ringer's lactate, and albumin, and to determine which patient factors are...
Acute Kidney Injury Female Aged Male Middle Aged Humans Fluid Therapy Albumins Resuscitation Ringer's Lactate Deep Learning Hospital Mortality Shock, Septic Treatment Outcome
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Optimal Vasopressin Initiation in Septic Shock: The OVISS Reinforcement Learning Study.

JAMA May 20, 2025
IMPORTANCE: Norepinephrine is the first-line vasopressor for patients with septic shock. When and whether a second agent, such as vasopressin, should be added is unknown.
Observational Studies as Topic Norepinephrine Vasoconstrictor Agents Datasets as Topic Practice Guidelines as Topic Male Middle Aged Hospital Mortality Shock, Septic Organ Dysfunction Scores Vasopressins Reinforcement Machine Learning Humans Critical Illness Electronic Health Records Clinical Decision-Making Female Aged
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Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory.

Nature communications May 13, 2025
Corticosteroids decrease the duration of organ dysfunction in sepsis and a range of overlapping and complementary infectious critical illnesses, including septic shock, pneumonia and the acute respiratory distress syndrome (ARDS). The risk and benefi...
Pneumonia Multiple Organ Failure Organ Dysfunction Scores Adrenal Cortex Hormones Machine Learning Humans Male Retrospective Studies Middle Aged Female Sepsis Aged Shock, Septic Respiratory Distress Syndrome
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Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study.

Scientific reports May 6, 2025
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...
Female Humans Middle Aged Shock, Cardiogenic Aged Neural Networks, Computer Sepsis Adult Artificial Intelligence Shock, Septic Male Retrospective Studies Bayes Theorem
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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 Feb 1, 2025
OBJECTIVE: To develop a scale to predict refractory septic shock (SS) based on clinical variables recorded during initial evaluations of patients.
Emergency Service, Hospital Registries Humans Norepinephrine Risk Assessment Machine Learning ROC Curve Female Retrospective Studies Shock, Septic Aged Intensive Care Units Regression Analysis Male Middle Aged
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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.) Jan 1, 2025
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...
Child, Preschool Child Hospital Mortality Intensive Care Units, Pediatric Shock, Septic Female Humans Male Machine Learning Infant Prognosis Sepsis Retrospective Studies Adolescent
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[Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue Apr 1, 2024
OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.
Prognosis Intensive Care Units Female Male Middle Aged Shock, Septic Support Vector Machine Decision Trees Supervised Machine Learning Humans Machine Learning Algorithms
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A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

Critical care medicine Nov 1, 2019
OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.
Algorithms Retrospective Studies Text Messaging Electronic Health Records Hospitals, Teaching Sepsis Sensitivity and Specificity Cohort Studies Diagnosis, Computer-Assisted Decision Support Systems, Clinical Humans Shock, Septic Machine Learning
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