Changes in the Perceived Epidemiology of Primary Hyperaldosteronism

Primary aldosteronism has been considered a rare disease in the past years, affecting 1% of the hypertensive population. Subsequently, growing evidence of its higher prevalence is present in literature, although the estimates of disease range from 5 up to 20%, as in type 2 diabetes and resistant hypertension. The main reasons for these variations are associated with the selection of patients and diagnostic procedures. If we consider that hypertension is present in about 20% of the adult population, primary aldosteronism can no longer be considered a rare disease. Patients with primary aldosteronism have a high incidence of cardiovascular, cerebrovascular and kidney complications. The identification of these patients has therefore a practical value on therapy, and to control morbidities derived from vascular damage. The ability to identify the prevalence of a disease depends on the number of subjects studied and the methods of investigation. Epidemiological studies are affected by these two problems: there is not consensus on patients who need to be investigated, although testing is recommended in subjects with resistant hypertension and diabetes. The question of how to determine aldosterone and renin levels is open, particularly if pharmacological wash-out is difficult to perform because of inadequate blood pressure control

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 deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients

BACKGROUND: Acute Kidney Injury (AKI), a frequentcomplication of pateints in theIntensive Care Unit (ICU), is associated with a high mortality rate. Early prediction of AKI is essential in order to trigger the use of preventive careactions.METHODS: The aim of this study was to ascertain the accuracy of two mathematical analysis models in obtaining a predictive score for AKI development. A deep learning model based on a urine output trends was compared with a logistic regression analysis for AKI prediction in stages 2 and 3 (defined as the simultaneous increase of serum creatinine and decrease of urine output, according to the Acute Kidney Injury Network (AKIN) guidelines). Two retrospective datasets including 35,573 ICU patients were analyzed. Urine output data were used to train and test the logistic regression and the deep learning model.RESULTS: The deep learning model definedan area under the curve (AUC) of 0.89 (±0.01), sensitivity=0.8 and specificity=0.84, which was higher than the logistic regression analysis. The deep learning model was able to predict 88% of AKI cases more than 12h before their onset: for every 6 patients identified as being at risk of AKI by the deep learning model, 5 experienced the event. On the contrary, for every 12 patients not considered to be at risk by the model, 2 developed AKI.CONCLUSION: In conclusion, by using urine output trends, deep learning analysis was able to predict AKI episodes more than 12h in advance, and with a higher accuracy than the classical urine output thresholds. We suggest that this algorithm could be integrated inthe ICU setting to better manage, and potentially prevent, AKI episodes

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

Genomic Damage in Endstage Renal Disease—Contribution of Uremic Toxins

Patients with end-stage renal disease (ESRD), whether on conservative, peritoneal or hemodialysis therapy, have elevated genomic damage in peripheral blood lymphocytes and an increased cancer incidence, especially of the kidney. The damage is possibly due to accumulation of uremic toxins like advanced glycation endproducts or homocysteine. However, other endogenous substances with genotoxic properties, which are increased in ESRD, could be involved, such as the blood pressure regulating hormones angiotensin II and aldosterone or the inflammatory cytokine TNF-α. This review provides an overview of genomic damage observed in ESRD patients, focuses on possible underlying causes and shows modulations of the damage by modern dialysis strategies and vitamin supplementation

Measuring Residual Renal Function in Hemodialysis Patients without Urine Collection

This is the peer reviewed version of the following article: Wong, J., Kaja Kamal, R. M., Vilar, E. and Farrington, K. (2017), 'Measuring Residual Renal Function in Hemodialysis Patients without Urine Collection', Seminars in Dialysis, Vol. 30 (1): 39–49, which has been published in final form at doi: 10.1111/sdi.12557. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. © 2016 Wiley Periodicals, Inc.Many patients on hemodialysis retain significant residual renal function (RRF) but currently measurement of RRF in routine clinical practice can only be achieved using inter-dialytic urine collections to measure urea and creatinine clearances. Urine collections are difficult and inconvenient for patients and staff, and therefore RRF is not universally measured. Methods to assess RRF without reliance on urine collections are needed since RRF provides useful clinical and prognostic information and also permits the application of incremental hemodialysis techniques. Significant efforts have been made to explore the use of serum based biomarkers such as cystatin C, β-trace protein and β2 -microglobulin to estimate RRF. This article reviews blood-based biomarkers and novel methods using exogenous filtration markers which show potential in estimating RRF in hemodialysis patients without the need for urine collection.Peer reviewedFinal Accepted Versio

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