AIMC Topic: Bacteremia
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High procalcitonin level is related to blood stream infections, gram-negative pathogens, and ICU admission in infections of adult febrile cancer patients.
Journal of the Egyptian National Cancer Institute
May 10, 2025
BACKGROUND: Blood stream infection (BSI) represent a life-threatening condition. Thus, we aimed to investigate the role of procalcitonin (PCT) and C-reactive protein (CRP) tests in adult febrile patients with BSI and other clinical infections in hosp...
Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.
BMC infectious diseases
Mar 26, 2025
OBJECTIVE: Bloodstream infection (BSI) is a significant cause of mortality in patients with hematologic malignancies(HMs), particularly amid rising antibiotic resistance. This study aimed to analyze pathogen distribution, drug-resistance patterns and...
AI Enhanced explainable early prediction of blood culture positivity in neutropenic patients using clinical and hematologic parameters.
Computers in biology and medicine
Mar 15, 2025
Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk of infections. Neutropenic sepsis is a life-threatening condition and a major cause of cancer-related mortality. Patients with neutropenic sepsis are...
Machine learning-based prediction model for patients with recurrent Staphylococcus aureus bacteremia.
BMC medical informatics and decision making
Feb 24, 2025
BACKGROUND: Staphylococcus aureus bacteremia (SAB) remains a significant contributor to both community-acquired and healthcare-associated bloodstream infections. SAB exhibits a high recurrence rate and mortality rate, leading to numerous clinical tre...
Rapid detection of carbapenem-resistant Escherichia coli and carbapenem-resistant Klebsiella pneumoniae in positive blood cultures via MALDI-TOF MS and tree-based machine learning models.
BMC microbiology
Jan 24, 2025
BACKGROUND: Bloodstream infection (BSI) is a systemic infection that predisposes individuals to sepsis and multiple organ dysfunction syndrome. Early identification of infectious agents and determination of drug-resistant phenotypes can help patients...
Blood Culture
Klebsiella Infections
Escherichia coli
Anti-Bacterial Agents
Decision Trees
Microbial Sensitivity Tests
Escherichia coli Infections
Klebsiella pneumoniae
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Bacteremia
Humans
Carbapenems
Machine Learning
Carbapenem-Resistant Enterobacteriaceae
Clinical evaluation of a multiplex droplet digital PCR for diagnosing suspected bloodstream infections: a prospective study.
Frontiers in cellular and infection microbiology
Jan 16, 2025
BACKGROUND: Though droplet digital PCR (ddPCR) has emerged as a promising tool for early pathogen detection in bloodstream infections (BSIs), more studies are needed to support its clinical application widely due to different ddPCR platforms with dis...
Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients.
International journal of medical informatics
Jan 10, 2025
BACKGROUND: Bacteremia is a critical condition with high mortality that requires prompt detection to prevent progression to life-threatening sepsis. Traditional diagnostic approaches, such as blood cultures, are time-consuming. This limitation has en...
Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.
The Journal of infection
Dec 30, 2024
BACKGROUND: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.
Mandatory surveillance of bacteremia conducted by automated monitoring.
Frontiers in public health
Dec 16, 2024
Except for a few countries, comprehensive all-cause surveillance for bacteremia is not part of mandatory routine public health surveillance. We argue that time has come to include automated surveillance for bacteremia in the national surveillance sys...
Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.
Journal of pediatric hematology/oncology
Dec 2, 2024
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...
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