[Construction of a methodology for clinical evaluation of medical devices using simulation tools and illustration through three studies].

Introduction: European regulations have recently moved towards more stringent requirements for demonstrating the safety and performance of medical devices (MDs).

Objective: To apply an innovative testing method using medical simulation to the evaluation of three medical devices at different stages of their life cycle.

Method: The methodology for evaluating DMs using simulation is based on seven stages: definition of the context, training, construction of a scenario to test the DM, validation of the scenario, realization of the scenario, evaluation of the scenario by the players and validation and exploitation of the results.

H inhalation therapy in patients with moderate COVID-19 (HCOVID): a prospective ascending-dose phase I clinical trial.

Unlabelled: The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has triggered a serious global health crisis, resulting in millions of reported deaths since its initial identification in China in November 2019. The global disparities in immunization access emphasize the urgent need for ongoing research into therapeutic interventions. This study focuses on the potential use of molecular dihydrogen (H2) inhalation as an adjunctive treatment for COVID-19.

Early prediction of in-hospital mortality utilizing multivariate predictive modelling of electronic medical records and socio-determinants of health of the first day of hospitalization.

Background: In France an average of 4% of hospitalized patients die during their hospital stay. To aid medical decision making and the attribution of resources, within a few days of admission the identification of patients at high risk of dying in hospital is essential.

Methods: We used de-identified routine patient data available in the first 2 days of hospitalization in a French University Hospital (between 2016 and 2018) to build models predicting in-hospital mortality (at ≥ 2 and ≤ 30 days after admission).

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016-2018.

Purpose: In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible is one of the first steps towards improving patient outcomes.

Development of Indirect Health Data Linkage on Health Product Use and Care Trajectories in France: Systematic Review.

Background: European national disparities in the integration of data linkage (ie, being able to match patient data between databases) into routine public health activities were recently highlighted. In France, the claims database covers almost the whole population from birth to death, offering a great research potential for data linkage. As the use of a common unique identifier to directly link personal data is often limited, linkage with a set of indirect key identifiers has been developed, which is associated with the linkage quality challenge to minimize errors in linked data.

A comparative study of two automated solutions for cross-sectional skeletal muscle measurement from abdominal computed tomography images.

Background: Measurement of cross-sectional muscle area (CSMA) at the mid third lumbar vertebra (L3) level from computed tomography (CT) images is becoming one of the reference methods for sarcopenia diagnosis. However, manual skeletal muscle segmentation is tedious and is thus restricted to research. Automated solutions are required for use in clinical practice.

Intra-operative fluorescence-based detection of positive surgical margins during radical prostatectomy: Lessons learned from a pilot ex vivo translational study.

Objectives: Nerve-sparing techniques during radical prostatectomy have been associated with an increased risk of positive surgical margins. The intra-operative detection of residual prostatic tissue could help mitigate this risk. The objectives of the present study were to assess the feasibility of using an anti-prostate-specific membrane antigen (anti-PSMA) antibody conjugated with a fluorophore to characterize fresh prostate tissue as prostatic or non-prostatic for intra-operative surgical margin detection.

ODIASP: Clinically Contextualized Image Analysis Using the PREDIMED Clinical Data Warehouse, Towards a Better Diagnosis of Sarcopenia.

Big Data and Deep Learning approaches offer new opportunities for medical data analysis. With these technologies, PREDIMED, the clinical data warehouse of Grenoble Alps University Hospital, sets up first clinical studies on retrospective data. In particular, ODIASP study, aims to develop and evaluate deep learning-based tools for automatic sarcopenia diagnosis, while using data collected via PREDIMED, in particular, medical images.

COVID-19 Geographical Maps and Clinical Data Warehouse PREDIMED.

PREDIMED, Clinical Data Warehouse of Grenoble Alps University Hospital, is currently participating in daily COVID-19 epidemic follow-up via spatial and chronological analysis of geographical maps. This monitoring is aimed for cluster detection and vulnerable population discovery. Our real-time geographical representations allow us to track the epidemic both inside and outside the hospital.

Hypergraph Based Data Model for Complex Health Data Exploration and Its Implementation in PREDIMED Clinical Data Warehouse.

Within the PREDIMED Clinical Data Warehouse (CDW) of Grenoble Alpes University Hospital (CHUGA), we have developed a hypergraph based operational data model, aiming at empowering physicians to explore, visualize and qualitatively analyze interactively the complex and massive information of the patients treated in CHUGA. This model constitutes a central target structure, expressed in a dual form, both graphical and formal, which gathers the concepts and their semantic relations into a hypergraph whose implementation can easily be manipulated by medical experts. The implementation is based on a property graph database linked to an interactive graphical interface allowing to navigate through the data and to interact in real time with a search engine, visualization and analysis tools.

Comparison of Trunk Motion between Moderate AIS and Healthy Children.

Analysis of kinematic and postural data of adolescent idiopathic scoliosis (AIS) patients seems relevant for a better understanding of biomechanical aspects involved in AIS and its etiopathogenesis. The present project aimed at investigating kinematic differences and asymmetries in early AIS in a static task and in uniplanar trunk movements (rotations, lateral bending, and forward bending). Trunk kinematics and posture were assessed using a 3D motion analysis system and a force plate.

Gait Analysis Platform for Measuring Surgery Recovery.

Gait analysis has evolved significantly during last years due to the great development of the Medical Internet of Things (MIoT) platforms that allow an easy integration of sensors (inertial, magnetic and pressure in our case) to the complex analytics required to compute, not only relevant parameters, but also meaningful indexes. In this paper, we extend a previous development based on a fully wireless pair of insoles by implementing an updated version with more reliable and user-friendly devices, smartphone app and web front-end and back-end. We also extend previous work focused on fall analysis (with the corresponding fall risk index or FRI) with the proposal of a new surgery recovery index (SRI) to account for the individual speed recovery speed that can be measured either at clinical facilities or at home in a telemedicine environment or while doing daily life activities.

Reuse of medical face masks in domestic and community settings without sacrificing safety: Ecological and economical lessons from the Covid-19 pandemic.

The need for personal protective equipment increased exponentially in response to the Covid-19 pandemic. To cope with the mask shortage during springtime 2020, a French consortium was created to find ways to reuse medical and respiratory masks in healthcare departments. The consortium addressed the complex context of the balance between cleaning medical masks in a way that maintains their safety and functionality for reuse, with the environmental advantage to manage medical disposable waste despite the current mask designation as single-use by the regulatory frameworks.

Explaining a model predicting quality of surgical practice: a first presentation to and review by clinical experts.

Purpose: Surgical Data Science (SDS) is an emerging research domain offering data-driven answers to challenges encountered by clinicians during training and practice. We previously developed a framework to assess quality of practice based on two aspects: exposure of the surgical scene (ESS) and the surgeon's profile of practice (SPP). Here, we wished to investigate the clinical relevance of the parameters learned by this model by (1) interpreting these parameters and identifying associated representative video samples and (2) presenting this information to surgeons in the form of a video-enhanced questionnaire.

Superselective Ischemia in Robotic Partial Nephrectomy Does Not Provide Better Long-term Renal Function than Renal Artery Clamping in a Randomized Controlled Trial (EMERALD): Should We Take the Risk?

Background: Superselective clamping of tumor-targeted arteries aims to eliminate ischemia of the remnant kidney while keeping tumor bed bloodless during excision.

Objective: To evaluate the impact of superselective clamping on long-term renal function, compared with renal artery early unclamping.

Design, Setting, And Participants: A randomized monocentric single-blind trial (1:1) was conducted from February 2018 to August 2019.

Random forest of perfect trees: concept, performance, applications and perspectives.

Motivation: The principle of Breiman's random forest (RF) is to build and assemble complementary classification trees in a way that maximizes their variability. We propose a new type of random forest that disobeys Breiman's principles and involves building trees with no classification errors in very large quantities. We used a new type of decision tree that uses a neuron at each node as well as an in-innovative half Christmas tree structure.

EOS stereographic assessment of femoral shaft malunion after intramedullary nailing. A prospective series of 48 patients at 9 months' follow-up.

Introduction: The aim of the present study was to assess femoral shaft malunion following anterograde intramedullary nailing, using low-dose EOS stereoradiography. The study hypothesis was that our surgical technique is associated with radiological rotation disorder rates equivalent to those reported in the literature.

Methods: All patients with unilateral femoral shaft fracture treated by anterograde nailing between January 2014 and December 2016 and followed up in our structure were included in a single-center prospective study.

External versus endoscopic ultrasound: Non-inferiority assessment for visualization of various structures of interest in the shoulder.

Introduction: Certain structures and pathologies can be difficult to reveal under videoscopy alone during arthroscopic surgery. Ultrasound can be a useful contribution in arthroscopic diagnostic and therapeutic procedures. The main aim of the present study was to assess equivalence between endoscopic and external ultrasound for shoulder exploration.

Evaluation of a biodegradable PLA-PEG-PLA internal biliary stent for liver transplantation: in vitro degradation and mechanical properties.

Internal biliary stenting during biliary reconstruction in liver transplantation decrease anastomotic biliary complications. Implantation of a resorbable internal biliary stent (RIBS) is interesting since it would avoid an ablation gesture. The objective of present work was to evaluate adequacy of selected PLA-b-PEG-b-PLA copolymers for RIBS aimed to secure biliary anastomose during healing and prevent complications, such as bile leak and stricture.

Biostatistics Disruptive Acculturation Through Serious Gaming: A New Hope.

Biostatistics is one of the transversal subjects that all future doctors must acquire and master. Nonetheless, it is a subject that has the reputation of being difficult, which has not been able to be corrected even with the application of new pedagogical methods such as blended learning. We address this problem with our acculturative and disruptive approach in the form of a serious game scenario in clinical research that integrates biostatistics with our R4Web adapted tools.

Cohort Creation and Visualization Using Graph Model in the PREDIMED Health Data Warehouse.

Grenoble Alpes University Hospital (CHUGA) is currently deploying a health data warehouse called PREDIMED [1], a platform designed to integrate and analyze for research, education and institutional management the data of patients treated at CHUGA. PREDIMED contains healthcare data, administrative data and, potentially, data from external databases. PREDIMED is hosted by the CHUGA Information Systems Department and benefits from its strict security rules.

Offline identification of surgical deviations in laparoscopic rectopexy.

Objective: According to a meta-analysis of 7 studies, the median number of patients with at least one adverse event during the surgery is 14.4%, and a third of those adverse events were preventable. The occurrence of adverse events forces surgeons to implement corrective strategies and, thus, deviate from the standard surgical process.