58 research outputs found
External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients
Objectives: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients. Methods: The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterdam (the “AmsterdamUMC database”) admitted to their intensive care units. In this cohort, we analysed the accuracy of algorithms based on logistic regression and deep learning methods. The accuracy of investigated algorithms had previously been tested with electronic intensive care unit (eICU) and MIMIC-III patients. Results: The deep learning model had an area under the ROC curve (AUC) of 0,907 (± 0,007SE) with a sensitivity and specificity of 80% and 89%, respectively, for identifying oliguric AKI episodes. Logistic regression models had an AUC of 0,877 (± 0,005SE) with a sensitivity and specificity of 80% and 81%, respectively. These results were comparable to those obtained in the two US populations upon which the algorithms were previously developed and trained. Conclusion: External validation on the European sample confirmed the accuracy of the algorithms, previously investigated in the US population. The models show high accuracy in both the European and the American databases even though the two cohorts differ in a range of demographic and clinical characteristics, further underlining the validity and the generalizability of the two analytical approaches. Graphical abstract: [Figure not available: see fulltext.
A Complete Meteo/Hydro/Hydraulic Chain Application to Support Early Warning and Monitoring Systems: The Apollo Medicane Use Case
Because of the ongoing changing climate, extreme rainfall events’ frequency at the global scale
is expected to increase, thus resulting in high social and economic impacts. A Meteo/Hydro/Hydraulic
forecasting chain combining heterogeneous observational data sources is a crucial component for an
Early Warning System and is a fundamental asset for Civil Protection Authorities to correctly predict
these events, their effects, and put in place anticipatory actions. During the last week of October 2021
an intense Mediterranean hurricane (Apollo) affected many Mediterranean countries (Tunisia, Algeria,
Malta, and Italy) with a death toll of seven people. The CIMA Meteo/Hydro/Hydraulic forecasting chain,
including the WRF model, the hydrological model Continuum, the automatic system for water detection
(AUTOWADE), and the hydraulic model TELEMAC-2D, was operated in real-time to predict the Apollo
weather evolution as well as its hydrological and hydraulic impacts, in support of the early warning
activities of the Italian Civil Protection Department. The WRF model assimilating radar data and in situ
weather stations showed very good predictive capability for rainfall timing and location over eastern Sicily,
thus supporting accurate river flow peak forecasting with the hydrological model Continuum. Based on
WRF predictions, the daily automatic system for water detection (AUTOWADE) run using Sentinel 1 data
was anticipated with respect to the scheduled timing to quickly produce a flood monitoring map. Ad hoc
tasking of the COSMO-SkyMed satellite constellation was also performed to overcome the S1 data latency
in eastern Sicily. The resulting automated operational mapping of floods and inland waters was integrated
with the subsequent execution of the hydraulic model TELEMAC-2D to have a complete representation of
the flooded area with water depth and water velocity
Salt and Fluid Intake in the Development of Hypertension in Peritoneal Dialysis Patients
Cardiac and vascular structure and function parameters do not improve with alternate nightly home hemodialysis: An interventional cohort study
Background: Nightly extended hours hemodialysis may improve left ventricular hypertrophy and function and endothelial function but presents problems of sustainability and increased cost. The effect of alternate nightly home hemodialysis (NHD) on cardiovascular structure and function is not known
Diastolic function is a strong predictor of mortality in patients with chronic kidney disease
Short daily hemodialysis: Blood pressure control and left ventricular mass reduction in hypertensive hemodialysis patients.
- …