How to deal with the Poisson-gamma model to forecast patients' recruitment in clinical trials when there are pauses in recruitment dynamic?

International audienceRecruiting patients is a crucial step of a clinical trial. Estimation of the trial duration is a question of paramount interest. Most techniques are based on deterministic models and various ad hoc methods neglecting the variability in the recruitment process. To overpass this difficulty the so-called Poisson-gamma model has been introduced involving, for each centre, a recruitment process modelled by a Poisson process whose rate is assumed constant in time and gamma-distributed. The relevancy of this model has been widely investigated. In practice, rates are rarely constant in time, there are breaks in recruitment (for instance weekends or holidays). Such information can be collected and included in a model considering piecewise constant rate functions yielding to an inhomogeneous Cox model. The estimation of the trial duration is much more difficult. Three strategies of computation of the expected trial duration are proposed considering all the breaks, considering only large breaks and without considering breaks. The bias of these estimations procedure are assessed by means of simulation studies considering three scenarios of breaks simulation. These strategies yield to estimations with a very small bias. Moreover, the strategy with the best performances in terms of prediction and with the smallest bias is the one which does not take into account of breaks. This result is important as, in practice, collecting breaks data is pretty hard to manage

Randomised clinical trial for the cost–utility evaluation of two strategies of perineal reconstruction after abdominoperineal resection in the context of anorectal carcinoma: biological mesh repair versus primary perineal wound closure, study protocol for the GRECCAR 9 Study

International audienceIntroduction: Abdominoperineal resections performed for anorectal tumours leave a large pelvic and perineal defect causing a high rate of morbidity of the perineal wound (40%-60%). Biological meshes offer possibilities for new standards of perineal wound reconstruction. Perineal fillings with biological mesh are expected to increase quality of life by reducing perineal morbidity.Methods and analysis: This is a multicentre, randomised and single-blinded study with a blinded endpoint evaluation, the experimental arm of which uses a biological mesh and the control arm of which is defined by the primary closure after abdominoperineal resection for cancer. Patients eligible for inclusion are patients with a proven history of rectal adenocarcinoma and anal canal epidermoid carcinoma for whom abdominoperineal resection was indicated after a multidisciplinary team discussion. All patients must have social security insurance or equivalent social protection. The main objective is to assess the incremental cost-utility ratio (ICUR) of two strategies of perineal closure after an abdominoperineal resection performed for anorectal cancer treatment: perineal filling with biological mesh versus primary perineal closure (70 patient in each arm). The secondary objectives focus on quality of life and morbidity data during a 1-year follow-up. Deterministic and probabilistic sensitivity analyses will be performed in order to estimate the uncertainty surrounding the ICUR. CIs will be constructed using the non-parametric bootstrap approach. A cost-effectiveness acceptability curve will be built so as to estimate the probability of efficiency of the biological meshes given a collective willingness-to-pay threshold.Ethics and dissemination: The study was approved by the Regional Ethical Review Board of 'Nord Ouest 1' (protocol reference number: 20.05.14.60714; national number: 2020-A01169-30).The results will be disseminated through conventional scientific channels.Trial registration number: ClinicalTrials.gov Registry (NCT02841293)

A higher risk of acute rejection of human kidney allografts can be predicted from the level of CD45RC expressed by the recipients' CD8 T cells.

Although transplantation is the common treatment for end-stage renal failure, allograft rejection and marked morbidity from the use of immunosuppressive drugs remain important limitations. A major challenge in the field is to identify easy, reliable and noninvasive biomarkers allowing the prediction of deleterious alloreactive immune responses and the tailoring of immunosuppressive therapy in individuals according to the rejection risk. In this study, we first established that the expression of the RC isoform of the CD45 molecule (CD45RC) on CD4 and CD8 T cells from healthy individuals identifies functionally distinct alloreactive T cell subsets that behave differently in terms of proliferation and cytokine secretion. We then investigated whether the frequency of the recipients CD45RC T cell subsets before transplantation would predict acute graft rejection in a cohort of 89 patients who had undergone their first kidney transplantation. We showed that patients exhibiting more than 54.7% of CD8 CD45RC(high) T cells before transplantation had a 6 fold increased risk of acute kidney graft rejection. In contrast, the proportions of CD4 CD45RC T cells were not predictive. Thus, a higher risk of acute rejection of human kidney allografts can be predicted from the level of CD45RC expressed by the recipients' CD8 T cells

Distribution of CD4 and CD8 CD45RC T cell subsets in healthy individuals.

<p>Peripheral blood leukocytes from 608 healthy individuals were stained with mAbs against TCR, CD4, CD8 and CD45RC. The histograms represent CD45RC expression on CD4 T cells (<b>A</b>, top panels) and CD8 T cells (<b>B</b>, top panels) from three healthy individuals, to illustrate the inter-individual variability. The distribution of the proportion of CD45RC^high CD4 T cells (<b>A</b>) and of CD45RC^high, CD45RC^int and CD45RC^low CD8 T cells (<b>B</b>) in the cohort of 608 healthy individuals is presented as histograms. The proportion of CD45RC subsets in CD4 (<b>A</b>) and CD8 (<b>B</b>) T cells is presented according to age and each dot represents a separate individual. The R² values were calculated using linear regression.</p

Distribution of CD45RC T cell subsets in the studied cohort compared to healthy individuals.

<p>Peripheral blood leucocytes from 89 patients were frozen in liquid nitrogen before renal transplantation. Thereafter, these cells were thawed and stained with mAbs against TCR, CD4, CD8 and CD45RC. (A, B) the proportion of CD45RC subsets within CD4 (A) and CD8 T cells (B) in the 89 recipient before the graft were compared to those of 608 healthy individuals. The p-values were calculated using the Mann-Whitney test.</p

Proportion of CD45RC CD8 T cells before transplantation, a biomarker of acute kidney allograft rejection.

<p>(<b>A</b>) ROC curve analysis for CD45RC^high CD4 T cells (black), CD45RC^high CD8 T cells (red), CD45RC^int CD8 T cells (blue) and CD45RC^low CD8 T cells (green). To study CD45RC^high CD8 T cells (respectively CD45RC^high CD4 T cells), the number of true positives is the number of subjects who reject the transplant and who have a proportion of CD45RC^high CD8 T cells (respectively CD45RC^high CD4 T cells) higher or equal to the threshold value. To study CD45RC^int CD8 T cells (respectively CD45RC^low CD8 T cells), the number of true positives is the number of subjects who reject the transplant and who have a proportion of CD45RC^int CD8 T cells (respectively CD45RC^low CD8 T cells) lower or equal to the threshold value. (<b>B</b>, <b>C</b>, <b>D</b>, <b>E</b>) Kaplan-Meier curve estimates the survival without rejection based on CD45RC expression. The AUC for CD45RC^high CD4 T cell subset (0.60 CI95% [0.44–0.76]) and for CD45RC^low CD8 T cells (0.61 CI95% [0.44–0.77]) did not allow defining a threshold. The patients were therefore separated into two groups based on the median value for CD4 CD45RC^high and CD45RC^low CD8 T cells (<b>B</b>, <b>E</b>). However, the AUC for CD45RC^high and CD45RC^int CD8 T cell subset (0.71 CI95% [0.56–0.86] and 0.76 CI95% [0.65–0.88] respectively) permitted to define a threshold of 54.7% for CD45RC^high and 24.0% for CD45RC^int. The patients were therefore separated into two groups based on these cut-off values obtained from ROC analyses for CD45RC^high (<b>C</b>) and CD45RC^int (<b>D</b>) within CD8 T cells.</p

CD45RC expression identifies distinct human CD8 T cell subsets with different alloreactive properties <i>in vitro</i>.

<p>(<b>A</b>) CD45RC expression on CD8 T cells identifies three CD45RC subsets. (<b>B</b>) Purified CD45RC^high (High), CD45RC^int (Int) and CD45RC^low (Low) CD8 T cells were stimulated by MLR as described above for CD4 T cells and the proliferation and cytokine analysis were performed at day 5. The results obtained in 7 healthy individuals are presented as box-plot diagrams. Statistical results comparing the CD45RC^high, CD45RC^int and CD45RC^low subsets: proliferation (p{high/low}=0.043; p{high/int}=0.043), IFN-γ (p{high/low}=0.018; p{high/int}=0.043), TNF-α p{high/int} = 0.028), IL-17 (p{high/low} = 0.028; p{high/int} = 0.042), IL-4 (p{high/low} = 0.018; p{high/int} = 0.018; p{int/low} = 0.018), IL-5 (p{high/low} = 0.018; p{high/int} = 0.018; p{int/low} = 0.018) and IL-10 (p{high/low} = 0.018; p{high/int} = 0.027; p{int/low} = 0.028). The p-values were calculated using the Wilcoxon matched-pairs test; *, p<0.05; **, p<0.02; ***, p<0.002.</p

Deficit in phonological processes: a characteristic of the neuropsychological profile of children with NF1

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Twice Weekly Induction with Ixazomib-Lenalidomide-Dexamethasone (IRd) Combination Followed By Extended IRD Consolidation and Lenalidomide Maintenance in Transplant Eligible Patients with Newly Diagnosed Multiple Myeloma (NDMM): A Phase 2 Study from the Intergroupe Francophone Du Myelome (IFM 2014-03)

61st Annual Meeting and Exposition of the American-Society-of-Hematology (ASH), Orlando, FL, DEC 07-10, 2019International audienc