Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions.

Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models' performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain

Interactive Parallelization of Embedded Real-Time Applications Starting from Open-Source Scilab & Xcos

International audienceIn this paper, we introduce the workflow of interactive parallelization for optimizing embedded real-time applications for multicore architectures. In our approach, the real-time applications are written in the Scilab high-level mathematical & scientific programming language or with a Scilab Xcos block-diagram ap-proach. By using code generation and code parallelization technol-ogy combined with an interactive GUI, the end user can map appli-cations to the multicore processor iteratively. The approach is eval-uated on two use cases: (1) an image processing application written in Scilab and (2) an avionic system modeled in Xcos. Using the workflow, an end-to-end model-based approach targeting multicore processors is enabled resulting in a significant reduction in devel-opment effort and high application speedup. The workflow de-scribed in this paper is developed and tested within the EU-funded ARGO project focused on WCET-Aware Parallelization of Model-Based Applications for Heterogeneous Parallel Systems

Développement d'un modèle pharmacocinétique pharmacodynamique d'un inhibiteur mixte de la monoamine oxydase (MAO-A et MAO-B) chez le volontaire sain jeune

PARIS-BIUP (751062107) / SudocSudocFranceF

Interactive Parallelization of Embedded Real-Time Applications Starting from Open-Source Scilab & Xcos

International audienceIn this paper, we introduce the workflow of interactive parallelization for optimizing embedded real-time applications for multicore architectures. In our approach, the real-time applications are written in the Scilab high-level mathematical & scientific programming language or with a Scilab Xcos block-diagram ap-proach. By using code generation and code parallelization technol-ogy combined with an interactive GUI, the end user can map appli-cations to the multicore processor iteratively. The approach is eval-uated on two use cases: (1) an image processing application written in Scilab and (2) an avionic system modeled in Xcos. Using the workflow, an end-to-end model-based approach targeting multicore processors is enabled resulting in a significant reduction in devel-opment effort and high application speedup. The workflow de-scribed in this paper is developed and tested within the EU-funded ARGO project focused on WCET-Aware Parallelization of Model-Based Applications for Heterogeneous Parallel Systems

Microscale cristalline rare-earth doped resonators for strain-coupled optomechanics

International audienceRare-earth ion doped crystals for hybrid quantum technologies is an area of growing interest in the solid-state physics community. We have earlier theoretically proposed a hybrid scheme of a mechanical resonator which is fabricated out of a rare-earth doped mono-cristalline structure. The rare-earth ion dopants have absorption energies which are sensitive to crystal strain, and it is thus possible to couple the ions to the bending motion of the crystal cantilever. Here, we present the design and fabrication method based on focused-ion-beam etching techniques which we have successfully employed in order to create such microscale resonators, as well as the design of the environment which will allow to study the quantum behavior of the res-onators

Validating the accuracy of deep learning for the diagnosis of pneumonia on chest x-ray against a robust multimodal reference diagnosis: a post hoc analysis of two prospective studies

Abstract Background Artificial intelligence (AI) seems promising in diagnosing pneumonia on chest x-rays (CXR), but deep learning (DL) algorithms have primarily been compared with radiologists, whose diagnosis can be not completely accurate. Therefore, we evaluated the accuracy of DL in diagnosing pneumonia on CXR using a more robust reference diagnosis. Methods We trained a DL convolutional neural network model to diagnose pneumonia and evaluated its accuracy in two prospective pneumonia cohorts including 430 patients, for whom the reference diagnosis was determined a posteriori by a multidisciplinary expert panel using multimodal data. The performance of the DL model was compared with that of senior radiologists and emergency physicians reviewing CXRs and that of radiologists reviewing computed tomography (CT) performed concomitantly. Results Radiologists and DL showed a similar accuracy on CXR for both cohorts (p ≥ 0.269): cohort 1, radiologist 1 75.5% (95% confidence interval 69.1–80.9), radiologist 2 71.0% (64.4–76.8), DL 71.0% (64.4–76.8); cohort 2, radiologist 70.9% (64.7–76.4), DL 72.6% (66.5–78.0). The accuracy of radiologists and DL was significantly higher (p ≤ 0.022) than that of emergency physicians (cohort 1 64.0% [57.1–70.3], cohort 2 63.0% [55.6–69.0]). Accuracy was significantly higher for CT (cohort 1 79.0% [72.8–84.1], cohort 2 89.6% [84.9–92.9]) than for CXR readers including radiologists, clinicians, and DL (all p-values < 0.001). Conclusions When compared with a robust reference diagnosis, the performance of AI models to identify pneumonia on CXRs was inferior than previously reported but similar to that of radiologists and better than that of emergency physicians. Relevance statement The clinical relevance of AI models for pneumonia diagnosis may have been overestimated. AI models should be benchmarked against robust reference multimodal diagnosis to avoid overestimating its performance. Trial registration NCT02467192 , and NCT01574066 . Key point • We evaluated an openly-access convolutional neural network (CNN) model to diagnose pneumonia on CXRs. • CNN was validated against a strong multimodal reference diagnosis. • In our study, the CNN performance (area under the receiver operating characteristics curve 0.74) was lower than that previously reported when validated against radiologists’ diagnosis (0.99 in a recent meta-analysis). • The CNN performance was significantly higher than emergency physicians’ (p ≤ 0.022) and comparable to that of board-certified radiologists (p ≥ 0.269). Graphical Abstrac

Combining individual patient data from randomized and non-randomized studies to predict real-world effectiveness of interventions

Meta-analysis of randomized controlled trials is generally considered the most reliable source of estimates of relative treatment effects. However, in the last few years, there has been interest in using non-randomized studies to complement evidence from randomized controlled trials. Several meta-analytical models have been proposed to this end. Such models mainly focussed on estimating the average relative effects of interventions. In real-life clinical practice, when deciding on how to treat a patient, it might be of great interest to have personalized predictions of absolute outcomes under several available treatment options. This paper describes a general framework for developing models that combine individual patient data from randomized controlled trials and non-randomized study when aiming to predict outcomes for a set of competing medical interventions applied in real-world clinical settings. We also discuss methods for measuring the models’ performance to identify the optimal model to use in each setting. We focus on the case of continuous outcomes and illustrate our methods using a data set from rheumatoid arthritis, comprising patient-level data from three randomized controlled trials and two registries from Switzerland and Britain

WCET-aware parallelization of model-based applications for multi-cores: The ARGO approach

International audienceParallel architectures are nowadays not only confined to the domain of high performance computing, they are also increasingly used in embedded time-critical systems. The ARGO H2020 project 1 provides a programming paradigm and associated tool flow to exploit the full potential of architectures in terms of development productivity, time-to-market, exploitation of the platform computing power and guaranteed real-time performance. In this paper we give an overview of the objectives of ARGO and explore the challenges introduced by our approach

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

BackgroundWe previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15-20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in similar to 80% of cases.MethodsWe report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded.ResultsNo gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5-528.7, P=1.1x10(-4)) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR=3.70[95%CI 1.3-8.2], P=2.1x10(-4)). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR=19.65[95%CI 2.1-2635.4], P=3.4x10(-3)), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR=4.40[9%CI 2.3-8.4], P=7.7x10(-8)). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD]=43.3 [20.3] years) than the other patients (56.0 [17.3] years; P=1.68x10(-5)).ConclusionsRare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old