European Nephrologists' Attitudes toward the Application of Artificial Intelligence in Clinical Practice: A Comprehensive Survey.

INTRODUCTION: The rapid advancement of artificial intelligence and big data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics, has the potential to revolutionize many areas of medicine, including nephrology and dialysis. Artificial intelligence and big data analytics can be used to analyze large amounts of patient medical records, including laboratory results and imaging studies, to improve the accuracy of diagnosis, enhance early detection, identify patterns and trends, and personalize treatment plans for patients with kidney disease. Additionally, artificial intelligence and big data analytics can be used to identify patients' treatment who are not receiving adequate care, highlighting care inefficiencies in the dialysis provider, optimizing patient outcomes, reducing healthcare costs, and consequently creating values for all the involved stakeholders. OBJECTIVES: We present the results of a comprehensive survey aimed at exploring the attitudes of European physicians from eight countries working within a major hemodialysis network (Fresenius Medical Care NephroCare) toward the application of artificial intelligence in clinical practice. METHODS: An electronic survey on the implementation of artificial intelligence in hemodialysis clinics was distributed to 1,067 physicians. Of the 1,067 individuals invited to participate in the study, 404 (37.9%) professionals agreed to participate in the survey. RESULTS: The survey showed that a substantial proportion of respondents believe that artificial intelligence has the potential to support physicians in reducing medical malpractice or mistakes. CONCLUSION: While artificial intelligence's potential benefits are recognized in reducing medical errors and improving decision-making, concerns about treatment plan consistency, personalization, privacy, and the human aspects of patient care persist. Addressing these concerns will be crucial for successfully integrating artificial intelligence solutions in nephrology practice.

Effectiveness of cold HD for the prevention of HD hypotension and mortality in the general HD population.

BACKGROUND: Cold hemodialysis (HD) prevented intradialysis hypotension (IDH) in small, short-term, randomized trials in selected patients with IDH. Whether this treatments prevents IDH and mortality in the HD population at large is unknown. METHODS: We investigated the relationship between dialysate temperature and the risk of IDH, i.e. nadir blood pressure <90 mmHg (generalized estimating equation model) and all-cause mortality (Cox's regression) in an incident cohort of HD patients (n = 8071). To control for confounding by bias by indication and other factors we applied instrumental variables adjusting for case mix at facility level. RESULTS: Twenty-seven percent of patients in the study cohort were systematically treated with a dialysate temperature ≤35.5°C. Over a median follow-up of 13.6 months (interquartile range 5.2-26.1 months), a 0.5°C reduction of the dialysate temperature was associated with a small (-2.4%) reduction of the risk of IDH [odds ratio (OR) 0.976, 95% confidence interval (CI) 0.957-0.995, P = .013]. In case-mix, facility-level adjusted analysis, the association became much stronger (OR 0.67, 95% CI 0.63-0.72, risk reduction = 33%, P < .001). In contrast, colder dialysate temperature had no effect on mortality both in the unadjusted [hazard ratio (HR) (0.5°C decrease) 1.074, 95% CI 0.972-1.187, P = .16] and case-mix-adjusted analysis at facility level (HR 1.01, 95% CI 0.88-1.16, P = .84). Similar results were registered in additional analyses by instrumental variables applying the median dialysate temperature or the facility percentage of patients prescribed a dialysate temperature <36°C. Further analyses restricted to patients with recurrent IDH fully confirmed these findings. CONCLUSIONS: Cold HD was associated with IDH in the HD population but had no association with all-cause mortality.

Local Weather Station Design and Development for Cost-Effective Environmental Monitoring and Real-Time Data Sharing.

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station's capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station's use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station's open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.

Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study.

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.

Use of Robotic Platforms as a Tool to Support STEM and Physical Education in Developed Countries: A Descriptive Analysis.

The lack of interest of children at school is one of the biggest problems that Mexican education faces. Two important factors causing this lack of interest are the predominant methodology used in Mexican schools and the technology as a barrier for attention. The methodology that institutions have followed has become an issue because of its very traditional approach, with the professor giving all the theoretical material to the students while they listen and memorize the contents, and, if we add the issue of the growing access to technological devices for students, children carrying a phone are more likely to be distracted. This study aims to integrate technology through assistive robots as a beneficial tool for educators, in order to improve the attention span of students by making the learning process in multiple areas of the Mexican curriculum more dynamic, therefore obtaining better results. To prove this, four different approaches were implemented; three in elementary schools and one in higher education: the LEGO® robotic kit and the NAO robot for STEM (science, technology, engineering, and mathematics) teaching, the NAO robot for physical education (PE), and the PhantomX Hexapod, respectively. Each of these technological approaches was applied by considering both control and experimental groups, in order to compare the data and provide conclusions. Finally, this study proves that the attention span is indeed improved as a result of implementing robotic platforms during the teaching process, allowing the children to become more motivated during their PE class and become more proactive and retain more information during their STEM classes.

Paclitaxel-Coated Balloons for the Treatment of Symptomatic Central Venous Stenosis in Vascular Access: Results From a European, Multicenter, Single-Arm Retrospective Analysis.

INTRODUCTION: This was a European, multicenter, investigator-initiated and run, single-arm retrospective analysis to assess the safety and the clinical benefit of the use of paclitaxel-coated balloon (PCB) for the treatment of symptomatic central venous stenosis (CVS). MATERIALS AND METHODS: Eleven centers from 7 countries across Europe, submitted 86 cases performed during the period between October 2015 and June 2018. Minimum follow-up was 6 months. Patient baseline demographics and procedural details were collected. Mean age was 62.6 years (SD 15.2 years). Median vascular access age was 3.0 years (IQR 1.2-4.8 years). A total of 55 were arteriovenous fistulas (64%) the rest arteriovenous grafts (31/86, 36%). Vessels treated were 43 subclavian veins, 42 brachiocephalic veins and 1 superior vena cava. Median drug-coated balloon diameter was 10 mm (IQR 8-12 mm). Primary outcome measures were clinically assessed intervention-free period (IFP) of the treated segment at 6 months and procedure-related minor and major complications. Secondary outcome measures included access circuit survival, patient survival, and the investigation of independent factors that influence the IFP. RESULTS: IFP was 62.7% at 6 months. Median patient follow-up time was 1.0 year (IQR 0.5-2.2 years). There was 1 minor complication (1/86; 1.2%) and no major complications. Access circuit survival was 87.7% at 6 months. Patient survival was 79.7% at 2 years according to Kaplan-Meier survival analysis. Higher balloon diameters significantly favored IFP [HR 0.71 (0.55-0.92), p=0.006; 5-7 mm group vs 8-12 mm group, p=0.025]. CONCLUSION: In this analysis, use of PCBs for the treatment of symptomatic CVS was safe. Efficacy was comparable to previous trials. Increased balloon size had a significant effect on patency rates.

A Multicenter Global Registry of Paclitaxel Drug-Coated Balloon in Dysfunctional Arteriovenous Fistulae and Grafts: 6-Month Results.

PURPOSE: To assess the safety and clinical benefit of the Lutonix drug-coated balloon (DCB) catheter for the treatment of dysfunctional arteriovenous fistulae (AVF) and grafts (AVG) in a heterogenous real-world population. MATERIALS AND METHODS: This multicenter, prospective study enrolled 320 subjects from 12 countries in 25 sites across Europe and Asia. A total of 392 lesions were treated with the Lutonix 035 DCB catheter. Lesions were de novo and restenotic, located in every part of the circuit from the cannulation zone to central venous outflow. In-stent restenotic lesions also were treated. The primary safety endpoint was freedom from serious adverse events involving the access circuit through 30 days. The primary effectiveness endpoint was target lesion primary patency (TLPP) through 6 months. Secondary endpoints included access circuit primary patency (ACPP) at 6 months and the investigation of factors that would independently influence the primary endpoints. RESULTS: The primary safety endpoint was 95.5%, while TLPP was 73.9% at 6 months, per Kaplan-Meier survival analysis. ACPP was 71% at 6 months. TLPP for stenosis of AVFs was 78.1%. Subgroup analysis showed significantly improved TLPP when DCB was dilated for ≥120 seconds (P = .007). TLPP was significantly better when predilation occurred compared with cases where only DCB angioplasty was performed (77% vs 48.6%, P = .0005). CONCLUSIONS: The Lutonix AV Global Registry confirms that the Lutonix DCB is a safe and effective treatment option in real-world patients with dysfunctional AVF or AVG. Procedural details had a significant role in TLPP. No significant difference in TLPP was observed among different treatment areas.

Automation Pyramid as Constructor for a Complete Digital Twin, Case Study: A Didactic Manufacturing System.

Nowadays, the concept of Industry 4.0 aims to improve factories' competitiveness. Usually, manufacturing production is guided by standards to segment and distribute its processes and implementations. However, industry 4.0 requires innovative proposals for disruptive technologies that engage the entire production process in factories, not just a partial improvement. One of these disruptive technologies is the Digital Twin (DT). This advanced virtual model runs in real-time and can predict, detect, and classify normal and abnormal operating conditions in factory processes. The Automation Pyramid (AP) is a conceptual element that enables the efficient distribution and connection of different actuators in enterprises, from the shop floor to the decision-making levels. When a DT is deployed into a manufacturing system, generally, the DT focuses on the low-level that is named field level, which includes the physical devices such as controllers, sensors, and so on. Thus, the partial automation based on the DT is accomplished, and the information between all manufacturing stages could be decremented. Hence, to achieve a complete improvement of the manufacturing system, all the automation pyramid levels must be included in the DT concept. An artificial intelligent management system could create an interconnection between them that can manage the information. As a result, this paper proposed a complete DT structure covering all automation pyramid stages using Artificial Intelligence (AI) to model each stage of the AP based on the Digital Twin concept. This work proposes a virtual model for each level of the traditional AP and the interactions among them to flow and control information efficiently. Therefore, the proposed model is a valuable tool in improving all levels of an industrial process. In addition, It is presented a case study where the DT concept for modular workstations underpins the development of technologies within the framework of the Automation Pyramid model is implemented into a didactic manufacturing system.

Driver's Personality and Behavior for Boosting Automobile Security and Sensing Health Problems Through Fuzzy Signal Detection Case Study: Mexico City.

Automobile security became an essential theme over the last years, and some automakers invested much money for collision avoidance systems, but personalization of their driving systems based on the user's behavior was not explored in detail. Furthermore, efficiency gains could be had with tailored systems. In Mexico, 80% of automobile accidents are caused by human beings; the remaining 20% are related to other issues such as mechanical problems. Thus, 80% represents a significant opportunity to improve safety and explore driving efficiency gains. Moreover, when driving aggressively, it could be connected with mental health as a post-traumatic stress disorder. This paper proposes a Tailored Collision Mitigation Braking System, which evaluates the driver's personality driving treats through signal detection theory to create a cognitive map that understands the driving personality of the driver. In this way, aggressive driving can be detected; the system is then trained to recognize the personality trait of the driver and select the appropriate stimuli to achieve the optimal driving output. As a result, when aggressive driving is detected continuously, an automatic alert could be sent to the health specialists regarding particular risky behavior linked with mental problems or drug consumption. Thus, the driving profile test could also be used as a detector for health problems.

Smart Homes as Enablers for Depression Pre-Diagnosis Using PHQ-9 on HMI through Fuzzy Logic Decision System.

Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders' health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral.

Clinical target achievement is associated with better quality of life among dialysis patients: results from a continuous quality improvement program in a Portuguese healthcare network.

BACKGROUND: Patients with kidney failure have multifaced clinical needs. Continuous quality improvement (CQI) programs initiated by large healthcare provider networks bear the promise of improving guideline adherence and improving patient-centered outcome, including health-related quality of life (HRQOL). We aimed at evaluating the association between key performance indicators (KPI) adopted for our CQI and HRQOL in a large network of dialysis providers. METHODS: We conducted a survey study in 39 centers belonging to the Portuguese Fresenius Medical Care (FME) network, in September 2017. For each participant, we retrospectively extracted clinical information during the 6-month period preceding survey administration. We used this information to calculate KPI as defined by the FME-CQI policy. Those KPI were selected in the FME-CQI policy as modifiable intermediate endpoints for which previous evidence suggested a causal relationship with patients' morbidity and mortality. HRQOL was assessed by the Kidney Disease Quality of Life Short Form 36 (KDQOL-36) questionnaire. RESULTS: Among 4691 eligible patients who were invited to participate in the survey, 2263 (48.2%) answered the self-administered survey. Based on KPI standards, patients had 1.5 (± 1.2) off-target clinical parameters on average. KDQOL-36 score were generally higher than those observed in European reference population. We found a significant linear association between KPI parameters and HRQOL. This pattern was robust to adjustment for satisfaction scores. CONCLUSIONS: Our data demonstrated a graded, monotonic, dose-response relationship between the number of off-target KPIs and HRQOL. Such relationship was not mediated by patients' satisfaction and may be attributed to amelioration of disease-specific symptoms and functional capacity.

Mobile Phone Usage Detection by ANN Trained with a Metaheuristic Algorithm †.

Artificial neural networks (ANN) are widely used to classify high non-linear systems by using a set of input/output data. Moreover, they are trained using several optimization methodologies and this paper presents a novel algorithm for training ANN through an earthquake optimization method. Usually, gradient optimization method is implemented for the training process, with perhaps the large number of iterations leading to slow convergence, and not always achieving the optimal solution. Since metaheuristic optimization methods deal with searching for weight values in a broad optimization space, the training computational effort is reduced and ensures an optimal solution. This work shows an efficient training process that is a suitable solution for detection of mobile phone usage while driving. The main advantage of training ANN using the Earthquake Algorithm (EA) lies in its versatility to search in a fine or aggressive way, which extends its field of application. Additionally, a basic example of a linear classification is illustrated using the proposal-training method, so the number of applications could be expanded to nano-sensors, such as reversible logic circuit synthesis in which a genetic algorithm had been implemented. The fine search is important for the studied logic gate emulation due to the small searching areas for the linear separation, also demonstrating the convergence capabilities of the algorithm. Experimental results validate the proposed method for smart mobile phone applications that also can be applied for optimization applications.

Predictive factors of recurrent endovascular intervention for cephalic arch stenosis after percutaneous transluminal angioplasty.

OBJECTIVE: Cephalic arch stenosis (CAS) is an important and recurring problem arising in hemodialysis patients because of the requirement for repeated interventions to maintain patency. The aim of this study was to determine predictive factors for recurrence of CAS after successful angioplasty. METHODS: A retrospective, case-control study was conducted at two ambulatory vascular access (VA) centers. All patients with a dysfunctional VA referred for an angiographic procedure and with a documented CAS as evidenced during the endovascular intervention (EI) between January 1, 2013, and December 31, 2015, were enrolled; 15 patients in whom an efficacious intervention was not possible were excluded. The study thus concerned 375 EIs using percutaneous transluminal angioplasty without stent placement on 241 VAs for CAS (9% of all procedures performed) during a 3-year period. Patients were compared regarding the absence (group 1; n = 181) or presence (group 2; n = 60) of recurrent CAS. We defined recurrence as that which occurred within 180 days of the previous successful EI for CAS. Any CAS diagnosed and treated >180 days after a previous one was considered a novel CAS and not a recurrence. Multivariate analysis was performed to determine variables independently associated with recurrence of CAS. Kaplan-Meier analysis was performed for determination of primary and assisted primary patency in this population. RESULTS: The recurrence rate of CAS was high (25%). Patients in both groups had similar demographic characteristics, time on hemodialysis, and mean dialysis dose and access flow rate at referral (P > .05). Multivariate analysis provided a significant discriminatory influence pertaining to diabetes (hazard ratio [HR], 2.054; 95% confidence interval [CI], 1.22-3.46; P = .007), residual stenosis even though it was <30% (HR, 1.86; 95% CI, 1.005-3.439; P = .048), and the finding of an isolated CAS lesion (HR, 0.445; 95% CI, 0.219-0.905; P = .025) in comparing group 1 and group 2. All other variables lost statistical significance on multivariate analysis. Primary patency at 6 months was 72%, increasing to an assisted primary patency of 89% at 6 months. The median durations of primary patency and assisted primary patency were 9.5 months and 15.6 months, respectively. CONCLUSIONS: Multivariate analysis showed that diabetes and residual stenosis (albeit <30%) were predictive of recurrence, whereas the finding of an isolated CAS lesion as opposed to stenoses in multiple locations was shown to be negatively associated with recurrent CAS, appearing to be "protective".

Pre-operative Assessment for Arteriovenous Fistula Placement for Dialysis.

Native AV fistulas are considered to be the best VA for most dialysis patients. A careful preoperative process of care is essential to maximize the proportion of fistulas that achieve adequacy for dialysis. An individualized and timely evaluation of patients starts early with the identification of risk factors, followed by a physical examination which should be complemented by ultrasound vascular mapping in most cases. Vascular mapping includes any technique that leads to information on patient's inflow and outflow anatomy (± hemodynamics) as they relate to arteriovenous access creation and may predict maturation. There is increasing evidence favoring the utilization of preoperative Doppler ultrasound which is recommended in all patients by NFK-KDOQI Guidelines. It allows noninvasive evaluation of both structural and functional aspects of vessels that play an important role in access maturation. Its major limitation is the relative inability to assess central vein patency. Although conventional venography is still the gold standard to evaluate central veins, it provides otherwise limited information and can incur serious adverse effects related to its invasive nature and contrast use. Alternatives to these two imaging techniques are rarely used, especially because of their higher costs and low availability.

An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients.

Managing anemia in hemodialysis patients can be challenging because of competing therapeutic targets and individual variability. Because therapy recommendations provided by a decision support system can benefit both patients and doctors, we evaluated the impact of an artificial intelligence decision support system, the Anemia Control Model (ACM), on anemia outcomes. Based on patient profiles, the ACM was built to recommend suitable erythropoietic-stimulating agent doses. Our retrospective study consisted of a 12-month control phase (standard anemia care), followed by a 12-month observation phase (ACM-guided care) encompassing 752 patients undergoing hemodialysis therapy in 3 NephroCare clinics located in separate countries. The percentage of hemoglobin values on target, the median darbepoetin dose, and individual hemoglobin fluctuation (estimated from the intrapatient hemoglobin standard deviation) were deemed primary outcomes. In the observation phase, median darbepoetin consumption significantly decreased from 0.63 to 0.46 µg/kg/month, whereas on-target hemoglobin values significantly increased from 70.6% to 76.6%, reaching 83.2% when the ACM suggestions were implemented. Moreover, ACM introduction led to a significant decrease in hemoglobin fluctuation (intrapatient standard deviation decreased from 0.95 g/dl to 0.83 g/dl). Thus, ACM support helped improve anemia outcomes of hemodialysis patients, minimizing erythropoietic-stimulating agent use with the potential to reduce the cost of treatment.

Dynamics of the erythropoiesis stimulating agent resistance index in incident hemodiafiltration and high-flux hemodialysis patients.

Hyporesponsiveness to erythropoiesis-stimulating agent therapy in dialysis patients is poorly understood. Some studies report an improvement in the erythropoiesis-stimulating agent resistance index (ERI) with hemodiafiltration (HDF) versus high-flux hemodialysis (HD). We explored ERI dynamics in 38,340 incident HDF and HD patients treated in 22 countries over a 7-year period. Groups were matched by propensity score at baseline (6 months after dialysis initiation). The follow-up period (mean of 1.31 years) was stratified into 1 month intervals with delta analyses performed for key ERI-related parameters. Dialysis modality, time interval, and polycystic kidney disease were included in a linear mixed model with the outcome ERI. Baseline ERI was nonsignificantly higher in HDF versus HD treatment. ERI decreased significantly faster in HDF-treated patients than in HD-treated patients, was decreased in both HD and HDF when patients were treated with intravenous darbepoetin alfa, but only in HDF when treated with intravenous recombinant human erythropoietin (rHuEPO). A clear difference between HD- and HDF-treated patients could only be found for patients with high baseline ERI and assigned to intravenous rHuEPO treatment. A significant advantage in terms of lower ERI for patients treated by HDF was found. Sensitivity analysis limited this advantage for HDF to those patients treated with intravenous rHuEPO (not darbepoetin alfa or subcutaneous rHuEPO) and to patients with a high baseline ERI. Thus, our results allow more accurate planning for future clinical trials addressing anemia management in dialysis patients.

Longitudinal Changes in Body Composition in Patients After Initiation of Hemodialysis Therapy: Results From an International Cohort.

OBJECTIVE: In patients with advanced kidney disease, metabolic and nutritional derangements induced by uremia interact and reinforce each other in a deleterious vicious circle. Literature addressing the effect of dialysis initiation on changes in body composition (BC) is limited and contradictory. The aim of this study was to evaluate changes in BC in a large international cohort of incident hemodialysis patients. METHODS: A total of 8,227 incident adult end-stage renal disease patients with BC evaluation within the initial first 6 months of baseline, defined as 6 months after renal replacement therapy initiation, were considered. BC, including fat tissue index (FTI) and lean tissue index (LTI), were evaluated by Body Composition Monitor (BCM, Fresenius Medical Care, Bad Homburg, Germany). Exclusion criteria at baseline were lack of a BCM measurement before or after baseline, body mass index (BMI) < 18.5 kg/m(2), presence of metastatic solid tumors, treatment with a catheter, and prescription of less or more than 3 treatments per week. Maximum follow-up was 2 years. Descriptive analysis was performed comparing current values with the baseline in each interval (delta analysis). Linear mixed models considering the correlation structure of the repeated measurements were used to evaluate factors associated with different trends in FTI and LTI. RESULTS: BMI increased about 0.6 kg/m(2) over 24 months from baseline. This was associated with increase in FTI of about 0.95 kg/m(2) and a decrease in LTI of about 0.4 kg/m(2). Female gender, diabetic status, and low baseline FTI were associated with a significant greater increase of FTI. Age > 67 years, diabetes, male gender, high baseline LTI, and low baseline FTI were associated with a significant greater decrease of LTI. CONCLUSIONS: With the transition to hemodialysis, end-stage renal disease patients presented with distinctive changes in BC. These were mainly associated with gender, older age, presence of diabetes, low baseline FTI, and high baseline LTI. BMI increases did not fully represent the changes in BC.

The development of an artificial organic networks toolkit for LabVIEW.

Two of the most challenging problems that scientists and researchers face when they want to experiment with new cutting-edge algorithms are the time-consuming for encoding and the difficulties for linking them with other technologies and devices. In that sense, this article introduces the artificial organic networks toolkit for LabVIEW™ (AON-TL) from the implementation point of view. The toolkit is based on the framework provided by the artificial organic networks technique, giving it the potential to add new algorithms in the future based on this technique. Moreover, the toolkit inherits both the rapid prototyping and the easy-to-use characteristics of the LabVIEW™ software (e.g., graphical programming, transparent usage of other softwares and devices, built-in programming event-driven for user interfaces), to make it simple for the end-user. In fact, the article describes the global architecture of the toolkit, with particular emphasis in the software implementation of the so-called artificial hydrocarbon networks algorithm. Lastly, the article includes two case studies for engineering purposes (i.e., sensor characterization) and chemistry applications (i.e., blood-brain barrier partitioning data model) to show the usage of the toolkit and the potential scalability of the artificial organic networks technique.

Doppler ultrasound: a powerful tool for vascular access surveillance.

National Kidney Foundation Kidney Disease Outcomes Quality Initiative (NKF KDOQI) guidelines recommend Doppler ultrasound (DU) for surveillance of vascular access (VA), but trials have not been unanimous about its benefit on VA patency. The aim of this study was to evaluate the accuracy of DU for patency, as well as to highlight additional data provided by this method. A transversal study was conducted to evaluate DU method in correlation with BTM using paired t-test and Pearson test. Ultrasonography evaluation was performed with a Siemens Acuson X150 Ultrasound device and BTM-Qa with the Blood Temperature Monitor BTM(®). Access blood flow (Qa) values were correlated with several factors by nonparametric tests. Fifty hemodialysis patients were included, with mean age of 64.5 ± 13.7 years; durations of hemodialysis and VA were 51.4 ± 47.3 and 47.6 ± 42.1 months, respectively. The mean difference between DU and BTM flows was 20.5 ml/minute (p 0.624). Pearson correlation was 0.851 (p < 0.001). DU-Qa values varied significantly with several factors: type of VA, reason for DU referral, the presence of artery stenosis, and the location and number of stenosis. BTM-Qa values only varied significantly with the presence and number of stenosis. Various silent abnormalities were detected with DU. DU provides accurate anatomic and hemodynamic data to further knowledge regarding the etiology of stenosis and other abnormalities that compromise VA well functioning.

High-volume postdilution hemodiafiltration is a feasible option in routine clinical practice.

Hemodiafiltration (HDF) with 20-22 L of substitution fluid is increasingly recognized as associated with significant benefits regarding patient outcome. However, some doubt exists as to whether these high volumes can be achieved in routine clinical practice. A total of 4176 sessions with 366 patients on postdilution HDF were analyzed in this 1-month observational cohort study with prospective data collection. All dialysis machines were equipped with AutoSub plus signal analysis software that automatically and continuously adapts the substitution fluid flow according to the blood flow, blood viscosity, and dialyzer characteristics. Percentages of sessions with different types of vascular access were compared regarding achievement of ≥21 L substitution fluid. Logistic regression analysis was conducted to study the independent relationship of selected variables with achievement of ≥21 L substitution volume. Patient- and dialysis-related variables that showed an association with the convection volume were entered in a multivariable model that included hematocrit up front. Respectively, 87%, 84%, and 33% of routine sessions conducted with fistulas, grafts, and catheters qualified as high-volume HDF. Serum albumin levels ≥4.2 g/dL were positively associated with the achievement of at least 21 L substitution volume. Positive associations were also observed for blood flows in the ranges 350-399 and ≥400 mL/min compared with the reference range (300-350 mL/min), for longer treatment time, for fistula versus catheter, for higher filtration fraction, and for dialysis conducted at the end of the week versus Monday. It can be concluded that implementation and sustainability of high-volume HDF is possible in routine clinical practice for almost all patients treated with fistulas and grafts.