Effect of subsurface pressure on the interface pressure measurement in an in vitro experiment

2019 ◽  
Vol 35 (2) ◽  
pp. 134-138
Author(s):  
Yung-Wei Chi ◽  
Ray Lin ◽  
Kuo-Hao Tseng ◽  
Blythe Durbin-Johnson

Introduction It was hypothesized that subsurface pressure (mimicking subcutaneous pressure) variation may affect interface pressure measurement. Method BISCO® (Rogers, CT) foam was placed on a cylinder cuff model for the experiment. Picopress® and a piezoresistive sensor were used for interface pressure measurement. External pressure was applied using an automated pressure cuff at 40 mmHg. Subsurface pressure mimicking subcutaneous pressure from 3 mmHg to 12 mmHg was generated by a pressure pump underneath the foam. Interface pressure was compared between the true pressure, 40 mmHg, versus Picopress® and the piezoresistive sensor using linear mixed effect model (SAS software, version 9.4, SAS Institute, Cary, NC). Result Interface pressure measurement using Picopress® did not differ between the incremental subsurface pressures (mean 45.4 ± 0.4) ( P = 0.54), in contrast to piezoresistive sensor, which demonstrated a difference (mean 42.65 ± 2.7) ( P < 0.001). This difference appeared to be linearly related. Conclusion Subsurface pressure mimicking subcutaneous pressure may affect the overall interface pressure measurement according to the piezoresistive sensor but not Picopress®.

2010 ◽  
Vol 93 (1) ◽  
pp. 234-241 ◽  
Author(s):  
J.J. Lievaart ◽  
H.W. Barkema ◽  
J. van den Broek ◽  
J.A.P. Heesterbeek ◽  
W.D.J. Kremer

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0212512 ◽  
Author(s):  
Eloá Moreira-Marconi ◽  
Marcia Cristina Moura-Fernandes ◽  
Patrícia Lopes-Souza ◽  
Ygor Teixeira-Silva ◽  
Aline Reis-Silva ◽  
...  

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 700.2-701
Author(s):  
G. Alex ◽  
S. K C ◽  
D. Reachel Varghese ◽  
S. Babu A S ◽  
R. Reji ◽  
...  

Background:Mycophenolate mofetil (MMF) is an effective treatment option for systemic sclerosis (SSC). However, many patients require co administration of proton pump inhibitors (PPI) or H2 receptor blockers (HRB) because of significant gastrointestinal manifestations in SSC. Co-treatment with PPI or HRB have shown to be associated with reduced drug exposure in post-transplant patients.1, 2 There is scarcity of data among patients with SSC. We evaluated the drug concentration of MMF over 12 hours of exposure and assessed the impact of ranitidine and PPI in twenty patients with SSC.Objectives:To assess the effect of esomeprazole or ranitidine on the bioavailability of MMF in SSC patients who are on a stable dose of MMF.Methods:Twenty SSC patients, who were on a stable dose of MMF (1.5-3 g) for the past 3 months were selected for the study after obtaining informed written consent. All patients were given either MMF (without PPI or HRB), MMF + esomeprazole, MMF + ranitidine for one month each. At the end of each month, EDTA plasma samples were collected at various time points including 0, 1/2, 1, 1½, 2, 2½, 3, 4, 5, 6, 8 and 12 hours following drug administration to determine the 12-hour area under curve (AUC) of mycophenolic acid (MPA) levels. Estimation of MPA levels was carried out using reverse phase high performance liquid chromatography (HPLC). Total gastrointestinal score was calculated at the end of each month using UCLA Scleroderma Clinical Trial Consortium GIT 2.0 scoring. To compare the mean AUC, linear mixed effect model was fit by considering treatment as the fixed effect and subject as the random effect. MMF was set as the reference treatment for the other three treatments and these were analysed together using Linear mixed effect model.Results:All patients were females with mean age of 45 years. Addition of either ranitidine or esomeprazole significantly reduced the mean AUC and C max of the MMF over 12-hour time period. On the other hand, PPI or HRB helped in reduction of the total GI score at the end of 1 month. Details of pharmacokinetics are depicted in the table 1.Table 1.Pharmacokinetics and GI score with MMF in combination with PPI / HRBMMFMMF+ RMMF + EpAUCmean (95% CI)67.97 (62.73, 73.20)53.04 (44.80, 61.27)45.69 (41.10, 50.28)<0.001*T- MAXmean (95% CI)42.00 (33.60, 50.40)46.50 (32.48, 60.52)79.50 (58.99, 100.01)<0.001*C-MAXmean (95% CI)29.61(26.74, 32.48)15.14 (11.32, 18.97)12.62 (10.58, 14.66)<0.001*Mean GI scoremean (95% CI)0.28 (0.15,0.40)0.19 (0.09, 0.30)0.14 (0.06,0.23)0.009AUC, area under curve Mycophenolic acid; C-MAX, maximum concentration of MPA in 12 hours following MMF; CI confidence interval;Mean GI score, UCLA Scleroderma Clinical Trial Consortium GIT 2.0 scoring; MMF, mycophenolate mofetil; MMF+E, mycophenolate mofetil + esomeprazole; MMF+R, mycophenolate mofetil+ ranitidine;*p value < 0.05 considered as significantConclusion:As co administration of PPI or HRB can significantly reduce the bioavailability of MMF in patients with systemic sclerosis. To avoid therapeutic failure of MMF drug level monitoring is essential when these agents re prescribed with MMF.References:[1]Schaier M, Scholl C, Scharpf D, Hug F, Bönisch-Schmidt S, Dikow R, et al. Proton pump inhibitors interfere with the immunosuppressive potency of mycophenolate mofetil. Rheumatology (Oxford, England). 2010;49:2061–7.[2]Rissling O, Glander P, Hambach P, Mai M, Brakemeier S, Klonower D, et al. No relevant pharmacokinetic interaction between pantoprazole and mycophenolate in renal transplant patients: a randomized crossover study. British Journal of Clinical Pharmacology. 2015;80:1086–96.Disclosure of Interests:None declared


2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mouhamad Nasser ◽  
Salim Si-Mohamed ◽  
Ségolène Turquier ◽  
Julie Traclet ◽  
Kaïs Ahmad ◽  
...  

Abstract Background Pleuroparenchymal fibroelastosis (PPFE) has a variable disease course with dismal prognosis in the majority of patients with no validated drug therapy. This study is to evaluate the effect of nintedanib in patients with idiopathic and secondary PPFE. Patients admitted to a tertiary care center (2010–2019) were included into this retrospective analysis if they had a multidisciplinary diagnosis of PPFE, had been followed-up for 3 months or more, and had lung function tests and chest CTs available for review. Changes in pulmonary function tests were assessed using non-parametric tests and linear mixed effect model. Lung volumes were measured with lobar segmentation using chest CT. Results Out of 21 patients with PPFE, nine had received nintedanib, six had received another treatment and another six patients were monitored without drug therapy. Annual FVC (% of predicted) relative decline was − 13.6 ± 13.4%/year before nintedanib and − 1.6 ± 6.02%/year during nintedanib treatment (p = 0.014), whereas no significant change in FVC% relative decline was found in patients receiving another treatment (− 13.25 ± 34 before vs − 16.61 ± 36.2%/year during treatment; p = 0.343). Using linear mixed effect model, the slope in FVC was − 0.97%/month (95% CI: − 1.42; − 0.52) before treatment and − 0.50%/month (95% CI: − 0.88; 0.13) on nintedanib, with a difference between groups of + 0.47%/month (95% CI: 0.16; 0.78), p = 0.004. The decline in the upper lung volumes measured by CT was − 233 mL/year ± 387 mL/year before nintedanib and − 149 mL/year ± 173 mL/year on nintedanib (p = 0.327). Nintedanib tolerability was unremarkable. Conclusion In patients with PPFE, nintedanib treatment might be associated with slower decline in lung function, paving the way for prospective, controlled studies.


Methodology ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 133-142 ◽  
Author(s):  
Zhehan Jiang

Abstract. Extending from classical test theory, G theory allows more sources of variations to be investigated and therefore provides the accuracy of generalizing observed scores to a broader universe. However, G theory has been used less due to the absence of analytic facilities for this purpose in popular statistical software packages. Besides, there is rarely a systematic G theory introduction in the linear mixed-effect model context, which is a widely taught technique in statistical analysis curricula. The present paper fits G theory into linear mixed-effect models and estimates the variance components via the well-known lme4 package in R. Concrete examples, modeling procedures, and R syntax are illustrated so that practitioners may use G theory for their studies. Realizing the G theory estimation in R provides more flexible features than other platforms, such that users need not rely on specialized software such as GENOVA and urGENOVA.


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