Activity

603-0.797] compared with an area under the curve of EF of 0.615 (95% CI 0.514-0.716) (P = 0.059 for difference). The total absolute deviation between predicted and observed outcome frequencies obtained by calibration plots were 13.8% for EF compared with only 6.4% for GLS. Best results of ROC analysis (area under the curve = 0.759; P = 0.06), calibration and goodness-of-fit (χ

 = 28.34, P ≤ 0.0001,

 = 0.25) were achieved for GLS added to a baseline model consisting of known cardiovascular risk factors in a multivariate regression analysis.

In summary, in chronic dialysis patients, GLS is a more precise predictor of cardiovascular mortality than conventional echocardiography-derived EF.

In summary, in chronic dialysis patients, GLS is a more precise predictor of cardiovascular mortality than conventional echocardiography-derived EF.Disorders of water and sodium homeostasis in the human body-or dysnatraemias-are frequently encountered in clinical practice, but their analysis is often complex and their management is often troublesome. For many clinicians, it remains challenging to correctly interpret all relevant biochemical parameters involved in the analysis of dysnatraemia, especially when a rapid ‘bedside’ evaluation is required to initiate treatment. By mathematically deriving the relationship between plasma osmolality and urine osmolality under physiological circumstances, we were able to propose a novel and clinically useful nomogram for the rapid evaluation of disorders of plasma osmolality. We believe that the presented osmolality nomogram could be a transparent and clinically useful tool for the quick evaluation of disorders of the water and sodium balance in patients.In this issue of Clinical Kidney Journal, Van der Willik et al. report findings from a pilot study where they introduced collection of patient-reported outcome measures (PROMs) into routine kidney care in Dutch dialysis centres. click here It is comparable to a registry-led PROMs initiative in Sweden, published in Clinical Kidney Journal in 2020. Both studies reported low average PROMs response rates with substantial between-centre variation, and both identified suboptimal patient and staff engagement as a key barrier to implementing PROMs in routine care for people with chronic kidney disease (CKD). This suggests that national kidney registries could be well placed to facilitate large-scale collection of PROMs data, but that they may require additional guidance on how to do this successfully. In this editorial, we discuss the current state-of-play of PROMs collection by kidney registries and provide an overview of what is (un)known about the feasibility and effectiveness of PROMs in CKD and other conditions. We anticipate that the fast-growing evidence base on whether, and how, PROMs can be of value in CKD settings will expedite registry-based PROMs collection, which will ultimately lead to more valuable and person-centred services and to enhanced health and well-being of people with CKD.Chronic kidney disease (CKD) has structural and functional repercussions for the cardiovascular system that facilitate the development of cardiovascular disease (CVD). In fact, cardiovascular complications are frequent in the CKD population and thus cause a great clinical, public health and economic burden. Despite this challenge, the prevention and management of cardiovascular complications is one among several aspects of CKD that meets the criteria of an unmet medical need. This probably has to do with the misperception by the nephrologist of the global relevance of CVD in the CKD patient which, in turn, may be due to insufficient cardiovascular training during nephrology specialization. Therefore a change in approach is necessary to understand CKD as a disease in which the manifestations and complications related to CVD become so frequent and important that they require dedicated multidisciplinary clinical management. From this perspective, it makes sense to consider training in the subspecialty of cardionephrology to provide adequate cardiovascular care for CKD patients by the nephrologist. In addition, the cardionephrology subspecialist would be better able to interact with other specialists in multidisciplinary care settings created to achieve a deeper understanding and more effective clinical handling of the interactions between CKD and CVD.An 86-year-old woman was admitted for a ruptured popliteal artery aneurysm (rPAA, 26 × 28 mm). Due to the patient’s age and comorbidities, emergency endovascular repair was performed. After the failed antegrade guidewire crossing, a retrograde approach from the anterior tibial artery and snaring was performed for lesion crossing, and stentgraft (5 × 50 mm) was deployed from antegrade fashion. At the 14-month follow-up, computed tomography angiogram demonstrated stentgraft patency and reduced aneurysmal size. Although open surgery remains the first-line treatment for infected rPAA, our approach adds to the evidence and can be applied to emergency cases or high-risk surgical patients.With the increasing prevalence of neurodegenerative diseases, including Parkinson’s disease, hand tremor detection has become a popular research topic because it helps with the diagnosis and tracking of disease progression. Conventional hand tremor detection algorithms involved wearable sensors. A non-invasive hand tremor detection algorithm using videos as input is desirable but the existing video-based algorithms are sensitive to environmental conditions. An algorithm, with the capability of detecting hand tremor from videos with a cluttered background, would allow the videos recorded in a non-research environment to be used. Clinicians and researchers could use videos collected from patients and participants in their own home environment or standard clinical settings. Neural network based machine learning architectures provide high accuracy classification results in related fields including hand gesture recognition and body movement detection systems. We thus investigated the accuracy of advanced neural network architectures to automatically detect hand tremor in videos with a cluttered background. We examined configurations with different sets of features and neural network based classification models. We compared the performance of different combinations of features and classification models and then selected the combination which provided the highest accuracy of hand tremor detection. We used cross validation to test the accuracy of the trained model predictions. The highest classification accuracy for automatically detecting tremor (vs non tremor) was 80.6% and this was obtained using Convolutional Neural Network-Long Short-Term Memory and features based on measures of frequency and amplitude change.