Background: Through venous contraction, norepinephrine (NE) increases stressed blood volume and mean systemic pressure (Pms) and exerts a "fluid-like" effect. When both fluid and NE are administered, Pms may not only result from the sum of the effects of both drugs. Indeed, norepinephrine may enhance the effects of volume expansion: because fluid dilutes into a more constricted, smaller, venous network, fluid may increase Pms to a larger extent at a higher than at a lower dose of NE. We tested this hypothesis, by mimicking the effects of fluid by passive leg raising (PLR).
Methods: In 30 septic shock patients, norepinephrine was decreased to reach a predefined target of mean arterial pressure (65-70 mmHg by default, 80-85 mmHg in previously hypertensive patients). We measured the PLR-induced increase in Pms (heart-lung interactions method) under high and low doses of norepinephrine. Preload responsiveness was defined by a PLR-induced increase in cardiac index ≥ 10%.
Results: Norepinephrine was decreased from 0.32 [0.18-0.62] to 0.26 [0.13-0.50] µg/kg/min (p < 0.0001). This significantly decreased the mean arterial pressure by 10 [7-20]% and Pms by 9 [4-19]%. The increase in Pms (∆Pms) induced by PLR was 13 [9-19]% at the higher dose of norepinephrine and 11 [6-16]% at the lower dose (p < 0.0001). Pms reached during PLR at the high dose of NE was higher than expected by the sum of Pms at baseline at low dose, ∆Pms induced by changing the norepinephrine dose and ∆Pms induced by PLR at low dose of NE (35.6 [11.2] mmHg vs. 33.6 [10.9] mmHg, respectively, p < 0.01). The number of preload responders was 8 (27%) at the high dose of NE and 15 (50%) at the low dose.
Conclusions: Norepinephrine enhances the Pms increase induced by PLR. These results suggest that a bolus of fluid of the same volume has a greater haemodynamic effect at a high dose than at a low dose of norepinephrine during septic shock.
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http://dx.doi.org/10.1186/s13054-021-03711-5 | DOI Listing |
Background: Structural and functional heterogeneity in the brains of patients with Alzheimer's disease (AD) leads to diagnostic and prognostic uncertainty and confounds clinical treatment planning. Normative modelling, where individual-level deviations in brain measures from a reference sample are computed to infer personalized effects of disease, allows parsing of disease heterogeneity. In this study, GAN based normative modelling technique quantifies individual level neuroanatomical abnormality thereby facilitating measurement of personalized disease related effects in AD patients.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA, USA.
Background: Amyloid-β (Aβ) plaques and tau pathogenesis in the brain precede cognitive decline in the progression of Alzheimer's dementia, yet the extent to which these measures can predict localized brain tissue atrophy has not been studied in a large, diverse population. Multisite studies offer robust statistical power with larger sample sizes but are confounded by variations in biomarker quantification across studies, including variations in MRI scanners, PET tracers, and CSF assays. Longitudinal data from N=1223 individuals from four independent AD studies were harmonized to assess localized brain tissue atrophy over 2 to 5 years.
View Article and Find Full Text PDFBackground: Cortical brain atrophy is an excellent marker of clinical decline and can support future clinical course prediction in cognitive impairment. We used a U-Net image-generation deep learning network to predict future cortical atrophy rates in elderly populations, with initial T1-weighted (T1w-) MRI or baseline amyloid-PET serving as inputs to the model.
Method: MRI and PET data were retrospectively collected from Alzheimer's Disease Imaging Initiative (ADNI) and all participants had two serial T1w-MRI scans (Figure 1A,B).
Alzheimers Dement
December 2024
University of Pennsylvania, Philadelphia, PA, USA.
Background: Assessment of longitudinal hippocampal atrophy is a well-studied biomarker for Alzheimer's disease (AD). However, most state-of-the-art measurements calculate changes directly from MRI images using image registration/segmentation, which may misreport head motion or MRI artifacts as neurodegeneration. We present a deep learning method Regional Deep Atrophy (RDA) that (1) estimates atrophy sensitive to progression by quantifying time-associated changes in images, especially in preclinical AD stage (as in DeepAtrophy (Dong et al.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of California, Irvine, Irvine, CA, USA.
Background: Perivascular spaces (PVS), the small spaces surrounding vasculature in the brain which are critical for glymphatic clearance, are an emerging marker of cerebrovascular dysfunction. Abnormal expansion of PVS has been associated with Alzheimer's disease (AD), and, therefore, may provide a measure of disease risk. Here, we investigate the relationship between PVS burden, memory performance, neurodegeneration, and biomarkers of AD pathology in community-dwelling older adults without cognitive impairment.
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