Previous studies have shown that the stroke volume variation (SVV), the pulse pressure variation (PPV) and the pleth variability index (PVI) could be successfully used for predicting fluid responsiveness (FR) in surgical patients. The aim of this study was to validate the ability of SVV, PPV and PVI to predict intraoperative FR in mechanically ventilated patients with obstructive jaundice (OJ). Thirty-two patients with OJ (mean serum total bilirubin 190.5 ± 95.3 µmol L(-1)) received intraoperative volume expansion (VE) with 250 ml colloids immediately after an exploratory laparotomy had been completed and after a 5 min period of hemodynamic stability. Hemodynamic variables were recorded before and after VE. FR was defined as an increase in stroke volume index > 10% after VE. The ability of SVV, PPV and PVI to predict FR was assessed by calculation of the area under the receiver operating characteristic curve. Eleven (34%) patients were responders and 21 patients were nonresponders to VE. The PPV was the unique dynamic index that had the moderate ability to predict FR during surgical procedures, the area under the curve was 0.71 (95% CI, 0.523 to 0.856; P = 0.039) and the threshold (sensitivity and specificity) discriminated responders was 7.5% (63.6%/71.4%). The present study concluded that SVV and PVI were not reliable predictors of FR, but PPV has some value predicting FR in patients with OJ intraoperatively.
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http://dx.doi.org/10.1088/0967-3334/35/3/369 | DOI Listing |
Alzheimers Dement
December 2024
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Background: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.
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December 2024
Keck School of Medicine of USC, Los Angeles, CA, USA.
Background: Research on biomarkers for Alzheimer's pathology has progressed rapidly. We summarize the evidence and make recommendations about biomarkers for future clinical use.
Method: Our interdisciplinary, international, multicultural group of experts in the Lancet Commission on dementia adopted a triangulation framework, prioritizing systematic reviews and meta-analyses and agreed on the best evidence for recommendations.
Alzheimers Dement
December 2024
Washington University in St. Louis, School of Medicine, St. Louis, MO, USA; Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer Disease Research Center, St. Louis, MO, USA.
Although amyloid b immunotherapies offer great potential for prevention or delay of symptoms in dominantly inherited AD (DIAD), the mechanism of action of this class of medications does not address the underlying mechanism of most DIAD mutations. Moreover, the need for repeated IV infusions or sub-cutaneous injections with Ab immunotherapies may prove challenging for long-term prevention approaches. The majority of DIAD mutations appear to affect the interaction of the gamma-secretase enzyme with the Amyloid Precursor Protein (APP) making this enzyme an attractive target for disease modification.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, CA, USA.
Background: Anti-amyloid therapy appears to have an increased effect on reducing cognitive decline in amyloid- and tau-positive individuals. However, clinical trials inclusion criteria require solely amyloid positivity. Herein, we developed a machine-learning prediction model to identify tau positivity in amyloid-positive individuals using clinical variables.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Laboratory of Neuro Imaging (LONI), University of Southern California, Los Angeles, CA, USA.
Background: Screen failure due to amyloid negativity is yet a problem in clinical trials for anti-amyloid drugs. In this context, clinical characteristics of patients presenting with cognitive decline may decrease the screen failure ratio by increasing the odds of selecting individuals with brain amyloid pathology. Herein, we aimed at estimating amyloid and tau positivity in individuals using clinical variables in a machine learning model of prediction.
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