Critical care : the official journal of the Critical Care Forum
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Meta Analysis
Variation in central venous oxygen saturation to evaluate fluid responsiveness: a systematic review and meta-analysis.
Since oxygen content and oxygen consumption typically remain unchanged within a short period, variation in central venous oxygen saturation (ΔScvO2) during fluid challenge can theoretically track the changes in cardiac output (CO). We conducted this meta-analysis to systematically assess the diagnostic performance of ΔScvO2 during a fluid challenge for fluid responsiveness in mechanically ventilated patients receiving volume expansion. ⋯ In mechanically ventilated patients receiving volume expansion, the ΔScvO2 during the fluid challenge is a reliable indicator of fluid responsiveness. Clinical trial registration PROSPERO, https://www.crd.york.ac.uk/prospero/ , registry number: CRD42022370192.
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Observational Study
Correlation between the VExUS score and right atrial pressure: a pilot prospective observational study.
Venous congestion is an under-recognized contributor to mortality in critically ill patients. Unfortunately, venous congestion is difficult to measure, and right heart catheterization (RHC) has been considered the most readily available means for measuring venous filling pressure. Recently, a novel "Venous Excess Ultrasound (VExUS)" score was developed to noninvasively quantify venous congestion using inferior vena cava (IVC) diameter and Doppler flow through the hepatic, portal, and renal veins. ⋯ After controlling for age, sex, and common comorbidities, we observed a significant positive association between RAP and VExUS grade (P < 0.001, R2 = .68). VExUS had a favorable AUC for prediction of a RAP ≥ 12 mmHg (0.99, 95% CI 0.96-1) compared to IVC diameter (0.79, 95% CI 0.65-0.92). These results suggest a strong correlation between VExUS and RAP in a diverse patient population, and support future studies of VExUS as a tool to assess venous congestion and guide management in a spectrum of critical illnesses.
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A quantitative assessment of pulmonary edema is important because the clinical severity can range from mild impairment to life threatening. A quantitative surrogate measure, although invasive, for pulmonary edema is the extravascular lung water index (EVLWI) extracted from the transpulmonary thermodilution (TPTD). Severity of edema from chest X-rays, to date is based on the subjective classification of radiologists. In this work, we use machine learning to quantitatively predict the severity of pulmonary edema from chest radiography. ⋯ Deep learning can quantify pulmonary edema as measured by EVLWI with high accuracy.