AIMC Topic: Fluid Therapy

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Machine Learning-Guided Fluid Resuscitation for Acute Pancreatitis Improves Outcomes.

Clinical and translational gastroenterology
INTRODUCTION: Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity and manage fluid resuscitation needs based on the physiologic parameters of individual patients. Our a...

Effect of different targets of goal-directed fluid therapy on intraoperative hypotension and fluid infusion in robot-assisted laparoscopic gynecological surgery: a randomized non-inferiority trial.

Journal of robotic surgery
Carotid corrected flow time (FTc) and tidal volume challenge pulse pressure variation (VtPPV) are useful clinical parameters for assessing volume status and fluid responsiveness in robot-assisted surgery, but their usefulness as goal-directed fluid t...

Perioperative Fluid and Vasopressor Therapy in 2050: From Experimental Medicine to Personalization Through Automation.

Anesthesia and analgesia
Intravenous (IV) fluids and vasopressor agents are key components of hemodynamic management. Since their introduction, their use in the perioperative setting has continued to evolve, and we are now on the brink of automated administration. IV fluid t...

Real-time and accurate estimation of surgical hemoglobin loss using deep learning-based medical sponges image analysis.

Scientific reports
Real-time and accurate estimation of surgical hemoglobin (Hb) loss is essential for fluid resuscitation management and evaluation of surgical techniques. In this study, we aimed to explore a novel surgical Hb loss estimation method using deep learnin...

Novel parameters for predicting fluid responsiveness during the mini fluid challenge and ability of the cardiac power index: an observational cohort study.

Turkish journal of medical sciences
BACKGROUND/AIM: The percentage change in the stroke volume index (SVI) due to the mini fluid challenge (MFC) (MFC-ΔSVI%) is used commonly in daily practice. However, up to 20% of patients remain in the gray zone of this variable. Thus, it was aimed t...

Clinical outcomes of hospitalised individuals with spin-induced exertional rhabdomyolysis.

Annals of the Academy of Medicine, Singapore
INTRODUCTION: Exertional rhabdomyolysis (ER) is caused by myocyte breakdown after strenuous physical activity. In recent years, the incidence of spin-induced ER (SER) has been increasing. We describe the clinical characteristics, management and outco...

Application of multiple deep learning models for automatic burn wound assessment.

Burns : journal of the International Society for Burn Injuries
PURPOSE: Accurate assessment of the percentage of total body surface area (%TBSA) burned is crucial in managing burn injuries. It is difficult to estimate the size of an irregular shape by inspection. Many articles reported the discrepancy of estimat...

Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms.

AMIA ... Annual Symposium proceedings. AMIA Symposium
The efficacy of early fluid treatment in patients with sepsis is unclear and may contribute to serious adverse events due to fluid non-responsiveness. The current method of deciding if patients are responsive to fluid administration is often subjecti...

Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: an observational study.

British journal of anaesthesia
BACKGROUND: Passive leg raising (PLR) predicts fluid responsiveness in critical illness, although restrictions in mobilising patients often preclude this haemodynamic challenge being used. We investigated whether machine learning applied on transthor...