329 research outputs found

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

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    International audienceBackground: 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. Purpose: The aim of this study was to compare the reliability of two automated solutions for the measurement of CSMA. Methods: We conducted a retrospective analysis of CT images in our hospital database.We included consecutive individuals hospitalized at the Grenoble University Hospital in France between January and May 2018 with abdominal CT images and sagittal reconstruction. We used two types of software to automatically segment skeletal muscle: ABACS, a module of the SliceOmatic software solution "ABACS-SliceOmatic,"and a deep learning-based solution called "Auto-MATiCA." Manual segmentation was performed by a medical expert to generate reference data using "SliceOmatic." The Dice similarity coefficient (DSC) was used to measure overlap between the results of the manual and the automated segmentations. The DSC value for each method was compared with the Mann-Whitney U test. Results: A total of 676 hospitalized individuals was retrospectively included (365 males [53.8%] and 312 females [46.2%]).The median DSC for SliceOmatic vs AutoMATiCA (0.969 [5th percentile: 0.909]) was greater than the median DSC for SliceOmatic vs. ABACS-SliceOmatic (0.949 [5th percentile: 0.836]) (p < 0.001). Conclusions: AutoMATiCA, which used artificial intelligence, was more reliable than ABACS-SliceOmatic for skeletal muscle segmentation at the L3 level in a cohort of hospitalized individuals. The next step is to develop and validate a neural network that can identify L3 slices, which is currently a fastidious process

    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

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    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. Risk assessment can enable healthcare providers to target resources to patients in greatest need through adaptations in processes and procedures. Electronic health data facilitates the application of machine-learning methods for risk analysis. We aim, first to reveal correlations between HAE occurrence and patients’ characteristics and/or the procedures they undergo during their hospitalisation, and second, to build models that allow the early identification of patients at an elevated risk of HAE.Participants 143 865 adult patients hospitalised at Grenoble Alpes University Hospital (France) between 1 January 2016 and 31 December 2018.Findings to date In this set-up phase of the project, we describe the preconditions for big data analysis using machine-learning methods. We present an overview of the retrospective de-identified multisource data for a 2-year period extracted from the hospital’s Clinical Data Warehouse, along with social determinants of health data from the National Institute of Statistics and Economic Studies, to be used in machine learning (artificial intelligence) training and validation. No supplementary information or evaluation on the part of medical staff will be required by the information system for risk assessment.Future plans We are using this data set to develop predictive models for several general HAEs including secondary intensive care admission, prolonged hospital stay, 7-day and 30-day re-hospitalisation, nosocomial bacterial infection, hospital-acquired venous thromboembolism, and in-hospital mortality

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

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    BackgroundEuropean 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. ObjectiveThe aim of this systematic review is to analyze the type and quality of research publications on indirect data linkage on health product use and care trajectories in France. MethodsA comprehensive search for all papers published in PubMed/Medline and Embase databases up to December 31, 2022, involving linked French database focusing on health products use or care trajectories was realized. Only studies based on the use of indirect identifiers were included (ie, without a unique personal identifier available to easily link the databases). A descriptive analysis of data linkage with quality indicators and adherence to the Bohensky framework for evaluating data linkage studies was also realized. ResultsIn total, 16 papers were selected. Data linkage was performed at the national level in 7 (43.8%) cases or at the local level in 9 (56.2%) studies. The number of patients included in the different databases and resulting from data linkage varied greatly, respectively, from 713 to 75,000 patients and from 210 to 31,000 linked patients. The diseases studied were mainly chronic diseases and infections. The objectives of the data linkage were multiple: to estimate the risk of adverse drug reactions (ADRs; n=6, 37.5%), to reconstruct the patient’s care trajectory (n=5, 31.3%), to describe therapeutic uses (n=2, 12.5%), to evaluate the benefits of treatments (n=2, 12.5%), and to evaluate treatment adherence (n=1, 6.3%). Registries are the most frequently linked databases with French claims data. No studies have looked at linking with a hospital data warehouse, a clinical trial database, or patient self-reported databases. The linkage approach was deterministic in 7 (43.8%) studies, probabilistic in 4 (25.0%) studies, and not specified in 5 (31.3%) studies. The linkage rate was mainly from 80% to 90% (reported in 11/15, 73.3%, studies). Adherence to the Bohensky framework for evaluating data linkage studies showed that the description of the source databases for the linkage was always performed but that the completion rate and accuracy of the variables to be linked were not systematically described. ConclusionsThis review highlights the growing interest in health data linkage in France. Nevertheless, regulatory, technical, and human constraints remain major obstacles to their deployment. The volume, variety, and validity of the data represent a real challenge, and advanced expertise and skills in statistical analysis and artificial intelligence are required to treat these big data

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

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    International audienceObjectives: 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. Methods: Fresh prostatic tissue samples were collected from transurethral resections of the prostate (TURP) or prostate biopsies, and either immunolabelled with anti-PSMA antibody conjugated with Alexa Fluor 488 or used as controls. A dedicated, laparoscopy-compliant fluorescence device was developed for real-time fluorescence detection. Confocal microscopy was used as the gold standard for comparison. Spectral unmixing was used to distinguish specific, Alexa Fluor 488 fluorescence from nonspecific autofluorescence. Results: The average peak wavelength of the immuno-labeled TURP samples (n = 4) was 541.7 ± 0.9 nm and of the control samples (n = 4) was 540.8 ± 2.2 nm. Spectral unmixing revealed that these similar measures were explained by significant autofluorescence, linked to electrocautery. Three biopsy samples were then obtained from seven patients and also displayed significant nonspecific fluorescence, raising questions regarding the reproducibility of the fixation of the anti-PSMA antibodies on the samples. Comparing the fluorescence results with final pathology proved challenging due to the small sample size and tissue alterations. Conclusions: This study showed similar fluorescence of immuno-labeled prostate tissue samples and controls, failing to demonstrate the feasibility of intraoperative margin detection using PSMA immuno-labeling, due to marked tissue autofluorescence. We successfully developed a fluorescence device that could be used intraoperatively in a laparoscopic setting. Use of the infrared range as well as newly available antibodies could prove interesting options for future research

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

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    International audienceBig 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. Here we describe a methodology of data preparation for a clinical study via PREDIMED

    An Orthotopic Model of Glioblastoma Is Resistant to Radiodynamic Therapy with 5-AminoLevulinic Acid

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    Radiosensitization of glioblastoma is a major ambition to increase the survival of this incurable cancer. The 5-aminolevulinic acid (5-ALA) is metabolized by the heme biosynthesis pathway. 5-ALA overload leads to the accumulation of the intermediate fluorescent metabolite protoporphyrin IX (PpIX) with a radiosensitization potential, never tested in a relevant model of glioblastoma. We used a patient-derived tumor cell line grafted orthotopically to create a brain tumor model. We evaluated tumor growth and tumor burden after different regimens of encephalic multifractionated radiation therapy with or without 5-ALA. A fractionation scheme of 5 × 2 Gy three times a week resulted in intermediate survival [48-62 days] compared to 0 Gy (15-24 days), 3 × 2 Gy (41-47 days) and, 5 × 3 Gy (73-83 days). Survival was correlated to tumor growth. Tumor growth and survival were similar after 5 × 2 Gy irradiations, regardless of 5-ALA treatment (RT group (53-67 days), RT+5-ALA group (40-74 days), HR = 1.57, p = 0.24). Spheroid growth and survival were diminished by radiotherapy in vitro, unchanged by 5-ALA pre-treatment, confirming the in vivo results. The analysis of two additional stem-like patient-derived cell lines confirmed the absence of radiosensitization by 5-ALA. Our study shows for the first time that in a preclinical tumor model relevant to human glioblastoma, treated as in clinical routine, 5-ALA administration, although leading to important accumulation of PpIX, does not potentiate radiotherapy

    Comparison of Trunk Motion between Moderate AIS and Healthy Children

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    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. A total of fifteen healthy girls, fifteen AIS girls with a left lumbar main curve, and seventeen AIS girls with a right thoracic main curve were compared. Statistical analyses were performed to investigate presumed differences between the three groups. This study showed kinematic and postural differences between mild AIS patients and controls such as static imbalance, a reduced range of motion in the frontal plane, and a different kinematic strategy in lateral bending. These differences mainly occurred in the same direction, whatever the type of scoliosis, and suggested that AIS patients behave similarly from a dynamic point of view

    COVID-19 Geographical Maps and Clinical Data Warehouse PREDIMED

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    International audiencePREDIMED, 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

    Differential Uridyl-diphosphate-Glucuronosyl Transferase 1A enzymatic arsenal explains the specific cytotoxicity of resveratrol towards tumor colorectal cells

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    International audienceResveratrol belongs to the Bioactive Food Component (BFC) family. It seems admitted that its cytotoxic action impacts tumor cells and spares healthy cells, but the published proofs remain rare. We hypothesized that cells may differentially metabolize resveratrol and lead to different systemic impacts. For this, resveratrol metabolization was evaluated by ultra-high-performance liquid chromatography (UHPLC) coupled with diode array detection (DAD), and correlated with the expression of Uridyl-diphosphate-Glucuronosyl Transferase 1A (UGT1A) genes. The expression of UGT1A genes in human colorectal tissues was studied with RNAseq databases. Functional validation of UGT1A enzymes implication in resveratrol sensitivity of colorectal cells established by UGT1A expression modulation. As resveratrol impacts the S phase of the cell cycle, nucleotide metabolic balance was assessed. We found that resveratrol was more cytotoxic in cells with downregulation of UGTs, i.e. tumor cells. Conversely, overexpression of the UGT1A10 gene in an initial resveratrol-sensitive tumor cell line restored the metabolization accompanied by cytotoxicity diminution. Resveratrol affected intestinal sensitive tumor cell homeostasis with a cell growth/proliferation decoupling, cell-cycle modulation, and UXP/AXP nucleotide imbalance resulting in a global reduction of transcription and translation. This impact on global cell activity was restricted to tumor cells. This study improves resveratrol’s general knowledge and explains how its antitumor action can spare non-tumor cells. It also paves the way to select colorectal tumors eligible for resveratrol treatment potentiation without additional toxicity to healthy digestive tissues
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