Home haemodialysis : trends in technology

Self management and home-based dialysis therapies offer the prospect of improved patient experience and outcomes. To allow more patients to realize these benefits requires changes in technology which focus on maximizing the ease and minimizing the burdens of undertaking home dialysis. These developments are underway.Peer reviewedFinal Published versio

Measurements of entropy-layer instabilities over cone-ogive-cylinders at Mach 6

Predicting the onset of boundary layer transition is critical in hypersonic flight. To improve transition prediction methods, it is necessary to understand the underlying instability mechanisms that cause transition. Entropy-layer instabilities are of particular interest in the design of blunt reentry vehicles and other blunt supersonic and hypersonic vehicles. Entropy-layer instabilities from outside the boundary layer may enter the boundary layer and have a significant effect on transition. There is little experimental data for entropy-layer instabilities. ^ Experimental measurements of what appear to be entropy-layer instabilities have been made in the Boeing/AFOSR Mach-6 Quiet Tunnel (BAM6QT) using surface pressure transducers and hot-wire anemometry. A long cone-ogive-cylinder model with interchangeable cone-ogive noses was used to generate the shock curvature that resulted in an entropy layer conducive to instability growth. The nosetip angles of the cone-ogive range from 25 to 40 degrees, with a majority of the measurements taken with the sharp 30 to 35-degree nosetips. ^ Surface measurements of the entropy-layer instabilities using the 30 to 35-degree configurations show disturbances between 15 and 50 kHz. As the nosetip angle increases, the frequency of the instability decreases slightly. Results also show that the instability magnitude as measured on the model surface increases with downstream distance, then decreases, before starting to increase again. The decrease is likely due to stabilization that occurs during the entropy-layer swallowing process. ^ Off-surface measurements using hot wires have also been made for each of the cone-ogive-cylinder configurations. These measurements show the location, frequency, and relative magnitude of the entropy-layer instability. As the instability progresses downstream, it grows inside the entropy layer, then at a certain distance downstream, the instability approaches the model surface and enters the boundary layer. Results show a smooth variation of the location of this instability descent with nosetip angle. As the angle increases, the instability approaches the model further upstream. ^ Cross-correlations between the surface transducer and hot-wire anemometry measurements confirm that the same instability is being measured at both locations. Cross-correlations between axially-displaced surface sensors were used to calculate an instability convection velocity that is approximately equal to the numerically-calculated flow velocity. And cross-correlations between azimuthally-displaced sensors show that the instability is primarily axisymmetric. The model angle of attack for all measurements was nominally zero. However, the actual angle of attack may vary by up to 0.1 degrees. The experimental results were also compared with mean-flow computations for several of the model configurations

Numerical Analysis and Optimization of the Ultra Compact Combustors

In an effort to increase thrust per weight ratio and decrease pollutant emissions of aero-turbine jet engines, a circumferentially burning Ultra Compact Combustor (UCC) with a Cavity-in-a-Cavity design has been developed. A numerical analysis of this design has been conducted and compared with experimental results. The CFD model has been validated through a wide range of conditions and four alternative physical configurations of the UCC have been modeled. Emissions, combustor efficiencies, temperature and velocity profiles, and pressure drop values were used as comparison parameters. Numerical results indicate that increasing the outflow area will increase the pressure drop over the combustor and decrease the combustor efficiency. A significant decrease (250%) in the cavity circumferential velocity effectively decreased the fuel-air mixing in the cavity resulting in decreased combustion efficiencies. A decreased cavity length reduced combustor pressure drop significantly with only minimal increases in pollutant emissions. The addition of a curved vane to the decreased cavity length configuration further decreased the pressure drop

The post-hemodialysis rebound: Predicting and quantifying its effect on Kt/V

The post-hemodialysis rebound: Predicting and quantifying its effect on Kt/V. Immediately after hemodialysis, the urea concentration rebounds upwards as urea continues to be transferred into the arterial circulation from peripheral body compartments. This rebound takes at least 30 minutes to complete. Hemodialysis is quantified as the Kt/V, calculated prom pre- and post-dialysis urea samples. Unless the post-dialysis sample is taken at least 30 minutes after dialysis, the Kt/V will be overestimated. This overestimation will be relatively greater in short high-efficiency dialyses, which have greater post-dialysis rebounds. We propose a method of correction that uses only the conventional pre- and immediate post-dialysis samples and is based on the physiologically-appropriate patient clearance time (tp). This is the time needed to clear all body compartments when the dialyzer clearance is infinite. The tp can be calculated from the pre-, immediate post- and 30-minute post-dialysis urea concentrations and was 35 minutes (SD 16) in 29 patients undergoing short (149 min) hemodiafiltration and standard (243 min) hemodialysis the following week. There was no significant difference between tp values calculated during the two treatments. Standard Kt/V can be corrected by multiplying by t/(t + tp) and dialysis time should be increased by tp × Kt/V minutes to compensate for the rebound. Despite individual variations in tp, a value of tp = 35 was sufficient to correct Kt/V in all patients. Kt/V corrected in this way agreed with Kt/V calculated using a 60-minute post-dialysis sample (r = 0.856, P < 0.001). The method predicted the 60-minute post-rebound concentration (SE 0.5mM, r = 0.983, P < 0.001) and the addition of 35 minutes to the treatment time corrected for the rebound in both conventional and short treatments. Similar simple equations corrected the error in V caused by rebound effects

Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012

Acuity scores, such as APACHE, SAPS, MPM, and SOFA, are widely used to account for population differ ences in studies aiming to compare how medications, care guidelines, surgery, and other interventions impact mortality in Intensive Care Unit (ICU) patients. By contrast, the focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of in-hospital mortality. The data used for the challenge consisted of 5 general descriptors and 36 time series (measurements of vital signs and laboratory results) from the first 48 hours of the first available ICU stay of 12,000 adult patients from the MIMIC II database. The challenge was organized as two events: event 1 measured performance of a binary classifier, and event 2 measured performance of a risk estimator. The score of event 1 was the lower of sensitivity and positive predictive value. The score for event 2 was a range-normalized Hosmer-Lemeshow statistic. A baseline algorithm (using SAPS-1) obtained event 1 and 2 scores of 0.3125 and 68.58 respectively. Most participants submitted entries that outperformed the baseline algorithm. The top final scores for events 1 and 2 were 0.5353 and 17.88 respectively.National Institute for Biomedical Imaging and Bioengineering (U.S.)National Institute of General Medical Sciences (U.S.) (NIH cooperative agreement U01-EB-008577)National Institute of General Medical Sciences (U.S.) (NIH grant R01-EB-001659

Empirical relationships among oliguria, creatinine, mortality, and renal replacement therapy in the critically ill

Purpose: The observation periods and thresholds of serum creatinine and urine output defined in the Acute Kidney Injury Network (AKIN) criteria were not empirically derived. By continuously varying creatinine/urine output thresholds as well as the observation period, we sought to investigate the empirical relationships among creatinine, oliguria, in-hospital mortality, and receipt of renal replacement therapy (RRT). Methods: Using a high-resolution database (Multiparameter Intelligent Monitoring in Intensive Care II), we extracted data from 17,227 critically ill patients with an in-hospital mortality rate of 10.9 %. The 14,526 patients had urine output measurements. Various combinations of creatinine/urine output thresholds and observation periods were investigated by building multivariate logistic regression models for in-hospital mortality and RRT predictions. For creatinine, both absolute and percentage increases were analyzed. To visualize the dependence of adjusted mortality and RRT rate on creatinine, the urine output, and the observation period, we generated contour plots. Results: Mortality risk was high when absolute creatinine increase was high regardless of the observation period, when percentage creatinine increase was high and the observation period was long, and when oliguria was sustained for a long period of time. Similar contour patterns emerged for RRT. The variability in predictive accuracy was small across different combinations of thresholds and observation periods. Conclusions: The contour plots presented in this article complement the AKIN definition. A multi-center study should confirm the universal validity of the results presented in this articleNational Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant R01 EB001659

Effect of dialysate composition on intercompartmental fluid shift

Effect of dialysate composition on intercompartmental fluid shift. Effect of dialysate composition on intercompartmental fluid shift and hemodynamics was studied in 12 patients during 1.5 or 2 hours of hemodialysis without net ultrafiltration, using high (H;Na 154 mmol/liter), normal (N;Na 140 mmol/liter) or low (L:Na 126 mmol/liter) concentration dialysate. H dialysate was associated with a small (0.9%) increase in blood volume, a larger increase in plasma volume and a decrease in erythrocyte volume. L dialysate resulted in a 2.3% decrease in blood volume, a larger decrease in plasma volume and an increase in erythrocyte volume. N dialysate gave results which were intermediately between the other two dialysis conditions. There was no difference in the post-dialysis mean arterial pressure between the groups, although heart rate increased more during H dialysis than during the other two conditions. Change in blood and erythrocyte volume correlated significantly with change in plasma Na concentration and osmolality, but not with change in plasma urea concentration. We conclude that dialysate composition affects the movement of water into and out of the plasma and erythrocytes in a manner that can be accounted for by altered plasma concentrations of osmotically active substances

Investigation into diagnostic accuracy of common strategies for automated perfusion motion correction

Respiratory motion is a significant obstacle to the use of quantitative perfusion in clinical practice. Increasingly complex motion correction algorithms are being developed to correct for respiratory motion. However, the impact of these improvements on the final diagnosis of ischemic heart disease has not been evaluated. The aim of this study was to compare the performance of four automated correction methods in terms of their impact on diagnostic accuracy. Three strategies for motion correction were used: (1) independent translation correction for all slices, (2) translation correction for the basal slice with transform propagation to the remaining two slices assuming identical motion in the remaining slices, and (3) rigid correction (translation and rotation) for the basal slice. There were no significant differences in diagnostic accuracy between the manual and automatic motion-corrected datasets (p=0.88). The area under the curve values for manual motion correction and automatic motion correction were 0.93 and 0.92, respectively. All of the automated motion correction methods achieved a comparable diagnostic accuracy to manual correction. This suggests that the simplest automated motion correction method (method 2 with translation transform for basal location and transform propagation to the remaining slices) is a sufficiently complex motion correction method for use in quantitative myocardial perfusion