Effect of dynamic random leaks on the monitoring accuracy of home mechanical ventilators: a bench study

BACKGROUND: So far, the accuracy of tidal volume (VT) and leak measures provided by the built-in software of commercial home ventilators has only been tested using bench linear models with fixed calibrated and continuous leaks. The objective was to assess the reliability of the estimation of tidal volume (VT) and unintentional leaks in a single tubing bench model which introduces random dynamic leaks during inspiratory or expiratory phases. METHODS: The built-in software of four commercial home ventilators and a fifth ventilator-independent ad hoc designed external software tool were tested with two levels of leaks and two different models with excess leaks (inspiration or expiration). The external software analyzed separately the inspiratory and expiratory unintentional leaks. RESULTS: In basal condition, all ventilators but one underestimated tidal volume with values ranging between -1.5 ± 3.3% to -8.7% ± 3.27%. In the model with excess of inspiratory leaks, VT was overestimated by all four commercial software tools, with values ranging from 18.27 ± 7.05% to 35.92 ± 17.7%, whereas the ventilator independent-software gave a smaller difference (3.03 ± 2.6%). Leaks were underestimated by two applications with values of -11.47 ± 6.32 and -5.9 ± 0.52 L/min. With expiratory leaks, VT was overestimated by the software of one ventilator and the ventilator-independent software and significantly underestimated by the other three, with deviations ranging from +10.94 ± 7.1 to -48 ± 23.08%. The four commercial tools tested overestimated unintentional leaks, with values between 2.19 ± 0.85 to 3.08 ± 0.43 L/min. CONCLUSIONS: In a bench model, the presence of unintentional random leaks may be a source of error in the measurement of VT and leaks provided by the software of home ventilators. Analyzing leaks during inspiration and expiration separately may reduce this source of error

Brain natriuretic peptide precursor (NT-pro-BNP) levels predict for clinical benefit to sunitinib treatment in patients with metastatic renal cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Sunitinib is an oral, multitargeted tyrosine kinase inhibitor that has been approved for the treatment of metastatic renal cell carcinoma. Although the majority of sunitinib-treated patients receive a clinical benefit, almost a third of the patients will not respond. Currently there is no available marker that can predict for response in these patients.</p> <p>Methods</p> <p>We estimated the plasma levels of NT-pro-BNP (the N-terminal precursor of brain natriuretic peptide) in 36 patients that were treated with sunitinib for metastatic clear-cell renal carcinoma.</p> <p>Results</p> <p>From the 36 patients, 9 had progressive disease and 27 obtained a clinical benefit (objective response or disease stabilization). Increases in plasma NT-pro-BNP were strongly correlated to clinical outcome. Patients with disease progression increased plasma BNP at statistically significant higher levels than patients that obtained a clinical benefit, and this was evident from the first 15 days of treatment (a three-fold increase in patients with progressive disease compared to stable NT-pro-BNP levels in patients with clinical benefit, p < 0.0001). Median progression-free survival was 12.0 months in patients with less than 1.5 fold increases (n = 22) and 3.9 months in patients with more than 1.5 fold increases in plasma NT-pro-BNP (n = 13) (log-rank test, p = 0.001).</p> <p>Conclusions</p> <p>This is the first time that a potential "surrogate marker" has been reported with such a clear correlation to clinical benefit at an early time of treatment. Due to the relative small number of accessed patients, this observation needs to be further addressed on larger cohorts. More analyses, including multivariate analyses are needed before such an observation can be used in clinical practice.</p

Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications

Versailles project on advanced materials and standards (VAMAS) interlaboratory study on measuring the number concentration of colloidal gold nanoparticles

We describe the outcome of a large international interlaboratory study of the measurement of particle number concentration of colloidal nanoparticles, project 10 of the technical working area 34, "Nanoparticle Populations" of the Versailles Project on Advanced Materials and Standards (VAMAS). A total of 50 laboratories delivered results for the number concentration of 30 nm gold colloidal nanoparticles measured using particle tracking analysis (PTA), single particle inductively coupled plasma mass spectrometry (spICP-MS), ultraviolet-visible (UV-Vis) light spectroscopy, centrifugal liquid sedimentation (CLS) and small angle X-ray scattering (SAXS). The study provides quantitative data to evaluate the repeatability of these methods and their reproducibility in the measurement of number concentration of model nanoparticle systems following a common measurement protocol. We find that the population-averaging methods of SAXS, CLS and UV-Vis have high measurement repeatability and reproducibility, with between-labs variability of 2.6%, 11% and 1.4% respectively. However, results may be significantly biased for reasons including inaccurate material properties whose values are used to compute the number concentration. Particle-counting method results are less reproducibile than population-averaging methods, with measured between-labs variability of 68% and 46% for PTA and spICP-MS respectively. This study provides the stakeholder community with important comparative data to underpin measurement reproducibility and method validation for number concentration of nanoparticles

3D Muscle modelling from CT-Scan

info:eu-repo/semantics/publishe

Monitoring of noninvasive ventilation by built-in software of home bilevel ventilators: a bench study.

BACKGROUND: Current bilevel positive-pressure ventilators for home noninvasive ventilation (NIV) provide physicians with software that records items important for patient monitoring, such as compliance, tidal volume (Vt), and leaks. However, to our knowledge, the validity of this information has not yet been independently assessed. METHODS: Testing was done for seven home ventilators on a bench model adapted to simulate NIV and generate unintentional leaks (ie, other than of the mask exhalation valve). Five levels of leaks were simulated using a computer-driven solenoid valve (0-60 L/min) at different levels of inspiratory pressure (15 and 25 cm H(2)O) and at a fixed expiratory pressure (5 cm H(2)O), for a total of 10 conditions. Bench data were compared with results retrieved from ventilator software for leaks and Vt. RESULTS: For assessing leaks, three of the devices tested were highly reliable, with a small bias (0.3-0.9 L/min), narrow limits of agreement (LA), and high correlations (R(2), 0.993-0.997) when comparing ventilator software and bench results; conversely, for four ventilators, bias ranged from -6.0 L/min to -25.9 L/min, exceeding -10 L/min for two devices, with wide LA and lower correlations (R(2), 0.70-0.98). Bias for leaks increased markedly with the importance of leaks in three devices. Vt was underestimated by all devices, and bias (range, 66-236 mL) increased with higher insufflation pressures. Only two devices had a bias &lt; 100 mL, with all testing conditions considered. CONCLUSIONS: Physicians monitoring patients who use home ventilation must be aware of differences in the estimation of leaks and Vt by ventilator software. Also, leaks are reported in different ways according to the device used