AIMC Topic: Catheter-Related Infections
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Early detection of vascular catheter-associated infections employing supervised machine learning - a case study in Lleida region.
BMC medical informatics and decision making
Aug 11, 2025
Healthcare-associated infections (HAIs), particularly Vascular Catheter-Associated Infections (VCAIs), are a significant concern, accounting for over 7% of all infections and are often linked to medical devices. Early detection of VCAIs before invasi...
The future of healthcare-associated infection surveillance: Automated surveillance and using the potential of artificial intelligence.
Journal of internal medicine
Jun 5, 2025
Healthcare-associated infections (HAIs) are common adverse events, and surveillance is considered a core component of effective HAI reduction programmes. Recently, efforts have focused on automating the traditional manual surveillance process by util...
Point-of-Care Sensors and Medical Internet-of-Things Technologies to Manage Catheter-Associated Urinary Tract Infections in the Intensive Care Unit.
Critical care nursing clinics of North America
Mar 24, 2025
This article examines the current technologies used with indwelling urinary catheters to monitor potential catheter-associated urinary tract infections (CAUTIs) in the intensive care unit. Advancements in medical internet-of-things, artificial intell...
Estimating individual risk of catheter-associated urinary tract infections using explainable artificial intelligence on clinical data.
American journal of infection control
Oct 29, 2024
BACKGROUND: Catheter-associated urinary tract infections (CAUTIs) increase clinical burdens. Identifying the high-risk patients is crucial. We aimed to develop and externally validate an explainable, prognostic prediction model of CAUTIs among hospit...
Safety and Efficacy of Acute Central Venous Catheters for Hemodialysis with Sodium Bicarbonate versus an Antibiotic Catheter Locking Solution.
Saudi journal of kidney diseases and transplantation : an official publication of the Saudi Center for Organ Transplantation, Saudi Arabia
May 9, 2024
This study was conducted to determine the safety and efficacy of acute central venous catheters (CVC) using a sodium bicarbonate catheter locking solution (SBCLS) versus an antibiotic catheter locking solution (ACLS). Our study included patients aged...
Assisting the infection preventionist: Use of artificial intelligence for health care-associated infection surveillance.
American journal of infection control
Mar 14, 2024
BACKGROUND: Health care-associated infection (HAI) surveillance is vital for safety in health care settings. It helps identify infection risk factors, enhancing patient safety and quality improvement. However, HAI surveillance is complex, demanding s...
Magnetic Soft Robot for Minimally Invasive Urethral Catheter Biofilm Eradication.
ACS nano
Oct 23, 2023
Catheter-related biofilm infection remains the main problem for millions of people annually, affecting morbidity, mortality, and quality of life. Despite the recent advances in the prevention of biofilm formation, alternative methods for biofilm prev...
Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
Catheter-associated urinary tract infection (CAUTI) is a common and costly healthcare-associated infection, yet measuring it accurately is challenging and resource-intensive. Electronic surveillance promises to make this task more objective and effic...
Assessing patient risk of central line-associated bacteremia via machine learning.
American journal of infection control
Apr 13, 2018
BACKGROUND: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLAB...
Academic Medical Centers
Epidemiologic Methods
Bacteremia
Catheterization, Central Venous
Catheter-Related Infections
Humans
Male
Middle Aged
Risk Assessment
Aged
Infant
Machine Learning
Case-Control Studies
Aged, 80 and over
Retrospective Studies
Young Adult
Adolescent
Female
Adult
Child, Preschool
Infant, Newborn
Child
Predicting central line-associated bloodstream infections and mortality using supervised machine learning.
Journal of critical care
Feb 21, 2018
PURPOSE: The purpose of this study was to compare machine learning techniques for predicting central line-associated bloodstream infection (CLABSI).
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