Vascular access is the key part of haemodialysis (HD) treatment, as this is not possible without a functioning access. The use of the arteriovenous fistula (AVF) has fewer complications, lower mortality and fewer hospital admissions compared to central venous catheter (CVC). However, although guidelines recommend AVF as the access of choice, access-related cannulation complications may lead to greater morbidity. Most guidelines recommend using Doppler ultrasound (DU) to surveil the AVF for HD, but its use must not only be limited to surveillance as it can also be used for needling. Therefore, among those techniques at our disposal today, one of the best tools for AVF needling is Doppler ultrasound (DU). Despite the lack of evidence regarding ultrasound-guided needling of AVF, it is becoming part of our usual practice arsenal in many HD centres. Its use has allowed needling results to improve and the number of complications to be reduced versus traditional ‘blind’ needling. It should be remembered that even though it is very useful for the daily work of dialysis nurses, as in the case of other techniques, it requires adequate, specialised and long-term training to acquire competence in using it. For example, it is important to learn some concepts and terminology that should be known and, at the same time, be highly familiar with different techniques available. Two types of needling techniques are described using US assistance: US-guided needling, where DU is used to make a map of the vessels which can be utilised and to mark the best site to insert the needles once the mapping is done; and real-time US-guided needling, the simultaneous manipulation of the probe and the insertion of the puncture needle through the slice plane of the ultrasound device. Regarding the real-time technique, there are two approaches: out of plane (the probe takes a transversal image of the needle) and in plane (vessel axis aligned with the probe and the needle in the same plane) To ensure successful needling and to maximise reproducibility, especially with tight deadlines and staff resources, nursing staff need to follow some important recommendations that include safety and the use of the method, both for them and the patient. In this way, ultrasound-guided needling becomes a tool with enormous potential utility, but practical training is as important as knowing the technique.
Background and Aims The native arteriovenous fistula is considered the vascular access of preference, since it is directly related to major survival, reduced complications, mortality, and costs. Still, its proper maintenance remains a challenge for nephrologists. A previous study from our group, which used the data of 117 arteriovenous fistulas, led to identify in the multivariable analysis age and vein diameters as predictive factors for early failure. On the other hand, Artificial Intelligence has been established as a tool to identify relationships between variables at deep levels, which might be unseen with more conservative methods like classic statistics. Therefore, through a Machine Learning technique known as Random Forest, the aim is to evaluate the same comorbidity, biological and Doppler ultrasound variables data to identify those with a major relation with the early failure of the native arteriovenous fistula. Method Retrospective cohort study, gathering the same data of the previous study (from 2011 to 2015): survival, ultrasound mapping (morphology and hemodynamics), comorbidities (blood pressure, severe arteriopathy, diabetes, Charlson’s Index), and laboratory (haemoglobin, calcium, phosphorus, PTH, ferritin, PCR). Different Artificial Intelligence algorithms were tested, but the most suited one for the study's aim turned out to be Random Forest. A model was trained, dividing the data in two sets, training and validation, with an 80/20 ratio. The algorithm used 100 decision trees, with a maximum individual depth of 3 levels. The training was made with the variables that represented the 100%, 95%, 90% and 85% of impact in the fistula's maturation from a theresold according to Gini’s Index. Results Age 65.7 (32-88) years, male 59.8%. Hypertension 86.7%, diabetes 50.7% and vascular disease 41.3%. The trained model obtained provided the following results in the evaluation: accuracy 0.82, precision 0.86, AUROC 0.85, F1 0.86 (balance between precision and predictive value). The most relevant variables by decision order were age, phosphorous, PTH, ferritin and calcium. Morphologic and hemodynamic variables such as the vessels diameter, Peak Systolic Velocity, Charlson’s Index or PCR, were also found to be relevant, but in a minor level. Conclusion In comparison with classic statistics, Machine Learning techniques might create a change of paradigm in predictive models for the patency of the vascular access for Haemodialysis. Even though the Artificial Intelligence-based model provided the same relevance for age in maturation failure as traditional models, other findings stand out, like the major participation of variables related with mineral metabolism or inflammation, rather than ultrasound based ones.
Background and Aims Patients with Acute Renal Failure (ARF) have a high risk of mortality, especially those who enter the Intensive Care Unit (ICU). In this population, predictive models of mortality on prognostic scales, such as SAPS-II (Simplified Acute Physiology Score II), linearly relate risk factors without taking into account the complex relationship's variables can have. There are models where Machine Learning (ML) techniques have been used, but there is still room for improvement. The implementation of deep artificial neural networks (DANN) can be challenging. The literature models, using SAPS-II report an accuracy, f1 and ROC area (receiver operating curve) in ranges of 0.538-0.621, 0.333-0.377 and 0.781-0.809 respectively. The best results with ML are improved in neural networks of a hidden layer or random forest, being the best performance in the latter: accuracy 0.715-0.741, F1 0.449-0.470 and ROC between 0.862-0.870. The aim is to evaluate and improve the predictive capacity of ML techniques for the prediction of mortality in patients with ARF admitted to the ICU, through the use of the open database MIMIC-III (Medical Information Martfor Intensive Care III). Method Design: Retrospective analysis of historical cooperation of 20,928 patients with ARF from Beth Israel Deaconess (Boston), from 2001 to 2012. Method: ML algorithm based on DANN. Creation of a model to predict in-hospital mortality after discharge from the ICU with the variables of the first 24 hours after admission to the ICU. To evaluate the robustness of the model has been performed cross-validation by separating the samples into different combinations of training and test data (k-folds). The unavailable variables haematological with means extracted from the training set of the respective fold. The DANN trained with the complex relationship's variables folds, two hidden layers of 75 and 40 neurons respectively. Inclusion criteria: > 16 years, AKI according to KDIGO criteria and predictive variables based on SAPS-II. Variables: Age, sex, type of admission, number of admissions in ICU, heart rate, blood pressure, temperature, PaO2, FiO2, sodium, potassium, bilirubin, bicarbonate, urea, leukocytes, diuresis and diagnosis of ICD-9 (metastatic cancer, haematological malignancies). Results Accuracy: 76.7% 0 ± 1.7%, f1: 86.6% ± 1.9%, RO area: 0.859 ± 0.006, sensitivity: 75.7% ± 2.8%, specificity: 80.5% ± 2.9%. Conclusion The use of Machine Learning techniques based on deep artificial neural networks can improve the predictive ability of mortality in acute renal failure of the critically ill patient of traditional clinical risk scales and even current Machine Learning models.
Background and Aims Tunnelled catheter-related bacteraemia (BRC) of the patient in chronic hemodialysis program results in high morbidity and mortality. Antibiotic catheter lock has been suggested to reduce the incidence of BRC in clinical trials. Aim: To demonstrate the effectiveness of universal aseptic measures in obtaining an optimal BRC rate in a long-term single-centre study. Method Design: Prospective cohort study, single-centre. Follow-up time: 11 years (2008-2018) Tunneled catheters: Optiflow, Hemostar, Hemosplit, Equistream, Hemoglide (Bard Access Systems, New Jersey, USA) and Palindrome (Covidien, - Medtronic, Mansfield, Massachusets, USA). Days / catheter analyzed: 207,320. Catheter placement: Ultrasound-guided interventional radiology equipment, with fluoroscopy and universal aseptic measures + 1 single dose of Vancomycin / cefazolin. Follow-up: Nursing and Nephrology and Infectious physicians. Universal aseptic measures. Heparin lock. BRC is considered to be the presence of a positive blood culture after discarding another focus and related to the negative catheter or blood culture with no other focus than the catheter. All available samples at the time of bacteraemia are analyzed: blood, sputum, urine, faeces, pleural fluid, peritoneal fluid, cerebrospinal fluid, exudates, smears etc, and their results. The BRC rate x 1000 days / catheter is evaluated Results Results BRC 2008 to 2018: 0.57, 0.47, 0.31, 0.1, 0.43, 0.37, 0.42, 0.16, 0.2, 0.18 and 0.04 respectively. The evaluated catheters, follow-up days, catheter-related bacteraemias, germ typing and treatment are analyzed: antibiotic vs. catheter removal in Table 1. Conclusion 1. The use of universal measures only, without the use in the antibiotics lock or anticoagulants other than heparin, can achieve a bacteraemia rate related to the optimal catheter, being cost / effective and avoiding possible resistance to antibiotics and side effects of other anticoagulant drugs. 2. Antibiotic sealing should be reserved for cases of difficult epidemiological control of BRC.
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