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). We tested nine different machine learning algorithms with repeated 10-fold cross-validation. Models were trained with 283 variables including age, sex, socio-determinants of health, laboratory test results, procedures (Classification of Medical Acts), medications (Anatomical Therapeutic Chemical code), hospital department/unit and home address (urban, rural etc.). The models were evaluated using various performance metrics. The dataset contained 123,729 admissions, of which the outcome for 3542 was all-cause in-hospital mortality and 120,187 admissions (no death reported within 30 days) were controls. RESULTS: The support vector machine, logistic regression and Xgboost algorithms demonstrated high discrimination with a balanced accuracy of 0.81 (95%CI 0.80-0.82), 0.82 (95%CI 0.80-0.83) and 0.83 (95%CI 0.80-0.83) and AUC of 0.90 (95%CI 0.88-0.91), 0.90 (95%CI 0.89-0.91) and 0.90 (95%CI 0.89-0.91) respectively. The most predictive variables for in-hospital mortality in all three models were older age (greater risk), and admission with a confirmed appointment (reduced risk). CONCLUSION: We propose three highly discriminating machine-learning models that could improve clinical and organizational decision making for adult patients at hospital 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. 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.

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. OBJECTIVE: The 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. METHODS: A 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. RESULTS: In 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. CONCLUSIONS: This 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.

Development of artificial intelligence powered apps and tools for clinical pharmacy services: A systematic review.

OBJECTIVE: Artificial Intelligence (AI) offers potential opportunities to optimize clinical pharmacy services in community or hospital settings. The objective of this systematic literature review was to identify and analyse quantitative studies using or integrating AI for clinical pharmacy services. MATERIALS AND METHODS: A systematic review was conducted using PubMed/Medline and Web of Science databases, including all articles published from 2000 to December 2021. Included studies had to involve pharmacists in the development or use of AI-powered apps and tools.. RESULTS: 19 studies using AI for clinical pharmacy services were included in this review. 12 out of 19 articles (63.1%) were published in 2020 or 2021. Various methodologies of AI were used, mainly machine learning techniques and subsets (natural language processing and deep learning). The datasets used to train the models were mainly extracted from electronic medical records (6 studies, 32%). Among clinical pharmacy services, medication order review was the service most targeted by AI-powered apps and tools (9 studies), followed by health product dispensing (4 studies), pharmaceutical interviews and therapeutic education (2 studies). The development of these tools mainly involved hospital pharmacists (12/19 studies). DISCUSSION AND CONCLUSION: The development of AI-powered apps and tools for clinical pharmacy services is just beginning. Pharmacists need to keep abreast of these developments in order to position themselves optimally while maintaining their human relationships with healthcare teams and patients. Significant efforts have to be made, in collaboration with data scientists, to better assess whether AI-powered apps and tools bring value to clinical pharmacy services in real practice.

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. 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 "AutoMATiCA." 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.

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. 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 intra-operative 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.

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. This model and its agile implementation allow for easy structural changes inherent to the evolution of techniques and practices in the health field. This flexibility provides adaptability to the evolution of interoperability standards.

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.

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

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. 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.

What value do digital health solutions bring, what are the funding mechanisms and evaluations?

Digital health is currently booming, providing major innovations, particularly in terms of changing the practices of the stakeholders in the healthcare system as a whole. It allows our healthcare system to draw on new synergies between independent, hospital and medico-social professionals, as well as on high-performance digital tools for the benefit of all, users, patients and professionals. These tools, or digital solutions, have a strong potential to improve the healthcare system but also a strong potential for economic development. In this respect, the great diversity of existing and future digital solutions, as well as their vast fields of application, are prompting public and private stakeholders in the sector to question their integration into our healthcare system. The resulting challenges concern the identification of the targets they are intended for, the values they embody and, as a result, the methods of funding and evaluation. At a time when the first reimbursement terms for digital solutions are taking shape in the context of the Social Security Financing Bill for 2022, the roundtable wished to propose 8 recommendations to help structure exchanges between the various stakeholders and initiate avenues of work around the integration of digital solutions into the healthcare system. The main orientations are based on the proposal of a common and transparent reflection methodology around the technical scope of these solutions, the values they bring and the funding mechanisms. Other work will be necessary beyond the points addressed by the round table in order to go into greater depth on certain themes such as the adaptation of existing funding methods to the momentum and specificities of digital technology or the development of research work on the evaluation of the value claimed by these digital solutions.

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. Patients with a single renal tumor were candidates for a robot-assisted partial nephrectomy (RAPN) in a referral center. EMERALD (NCT03679572) was powered to include 50 patients with an interim analysis after 30 cases. INTERVENTION: Superselective RAPN (SS-RAPN) with near-infrared fluorescence (NIRF) or conventional RAPN with renal artery early unclamping. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary endpoint was the percent change of estimated glomerular filtration rate (eGFR) in the operated kidney after 6 mo (combination of eGFR and relative function on 99mTc-DMSA scintigraphy). Secondary endpoints assessed feasibility and safety of the technique. RESULTS AND LIMITATIONS: Relative eGFR reduction in the operated kidney at 6 mo did not differ significantly (-21.4% vs -23.4%, p=0.66). This absence of difference remained after adjusting on percentage of kidney volume preserved, which was an independent predictor of functional preservation. There were no significant differences in terms of blood loss, change in hemoglobin, postoperative complications, transfusion, and conversion to radical nephrectomy (two vs zero) or to open surgery (one vs zero). Despite a good accrual, the steering committee interrupted the trial after the interim analysis for futility given the absence of trend in favor of SS-RAPN. CONCLUSIONS: SS-RAPN using NIRF does not provide better renal function preservation than renal artery clamping, questioning the interest of this technique at a higher risk of bleeding. PATIENT SUMMARY: In this randomized controlled trial, superselective clamping of tumor feeding arteries did not show any advantage in terms of long-term renal function compared with conventional artery clamping.

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. We report a Workflow that provides a quantitative basis to determine the safety and efficacy of a medical mask that is decontaminated for reuse. The type IIR polypropylene medical masks can be washed up to 10 times, washed 5 times and autoclaved 5 times, or washed then sterilized with radiations or ethylene oxide, without any degradation of their filtration or breathability properties. There is loss of the anti-projection properties. The Workflow rendered the medical masks to comply to the AFNOR S76-001 standard as "type 1 non-sanitory usage masks". This qualification gives a legal status to the Workflow-treated masks and allows recommendation for the reuse of washed medical masks by the general population, with the significant public health advantage of providing better protection than cloth-tissue masks. Additionally, such a legal status provides a basis to perform a clinical trial to test the masks in real conditions, with full compliance with EN 14683 norm, for collective reuse. The rational reuse of medical mask and their end-of-life management is critical, particularly in pandemic periods when decisive turns can be taken. The reuse of masks in the general population, in industries, or in hospitals (but not for surgery) has significant advantages for the management of waste without degrading the safety of individuals wearing reused masks.

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. This new index can be personalized for different types of surgeries that affect gait such as hip, knee, etc. This paper presents the case of hip recovery and is built on top of the clinical standard SPPB test and allows obtaining quantitative parameters directly from the sensors.

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. To our knowledge, this is the first approach in the field of SDS for laparoscopy linking the choices made by a machine learning model predicting surgical quality to clinical expertise. METHOD: Spatial features and quality of practice scores extracted from labeled and segmented frames in 30 laparoscopic videos were used to predict the ESS and the SPP. The relationships between the inputs and outputs of the model were then analyzed and translated into meaningful sentences (statements, e.g., "To optimize the ESS, it is very important to correctly handle the spleen"). Representative video clips illustrating these statements were semi-automatically identified. Eleven statements and video clips were used in a survey presented to six experienced digestive surgeons to gather their opinions on the algorithmic analyses. RESULTS: All but one of the surgeons agreed with the proposed questionnaire overall. On average, surgeons agreed with 7/11 statements. CONCLUSION: This proof-of-concept study provides preliminary validation of our model which has a high potential for use to analyze and understand surgical practices.

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. With these new RFs, we developed a score, based on a family of ten new statistical information criteria, called Nguyen information criteria (NICs), to evaluate the predictive qualities of features in three dimensions. RESULTS: The first NIC allowed the Akaike information criterion to be minimized more quickly than data obtained with the Gini index when the features were introduced in a logistic regression model. The selected features based on the NICScore showed a slight advantage compared to the support vector machines-recursive feature elimination (SVM-RFE) method. We demonstrate that the inclusion of artificial neurons in tree nodes allows a large number of classifiers in the same node to be taken into account simultaneously and results in perfect trees without classification errors. AVAILABILITY AND IMPLEMENTATION: The methods used to build the perfect trees in this article were implemented in the 'ROP' R package, archived at https://cran.r-project.org/web/packages/ROP/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. The main endpoint was≥15° transverse malrotation compared to the contralateral side as measured on EOS stereoradiography. Correlations between malrotation and Harris Hip and SF12 functional scores were assessed, as were risk factors for onset of shaft malunion in rotation. Forty-eight patients with a mean age of 31.4 years were analyzed at a mean 9.3 months' follow-up. RESULTS: Stereoradiographic malrotation was found in 29.2% of patients. Mean anteversion was 18.5±13.8°. In 2.1% of patients, symptomatic rotation disorder required revision surgery. No correlations emerged between transverse malrotation and functional scores (p>0.05). Risk factors for malrotation comprised multi-site fracture (p=0.04), surgeon's inexperience (p=0.04), and open reduction (p=0.01). CONCLUSION: The present radiologic malrotation rate was comparable to those reported in the literature, using the EOS stereoradiographic system, which provides precise assessment of rotation disorder following closed nailing of femoral shaft fracture. LEVEL OF EVIDENCE: III; prospective study without control group.

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. The kinetics of degradation and mechanical properties of a RIBS prototype were evaluated with respect to the main bile duct stenting requirements in liver transplantation. For this purpose, RIBS degradation under biliary mimicking solution versus standard phosphate buffer control solution was discussed. Morphological changes, mass loss, water uptake, molecular weight, permeability, pH variations, and mechanical properties were examined over time. The permeability and mechanical properties were evaluated under simulated biliary conditions to explore the usefulness of a PLA-b-PEG-b-PLA RIBS to secure biliary anastomosis. Results showed no pH influence on the kinetics of degradation, with degradable RIBS remaining impermeable for at least 8 weeks, and keeping its mechanical properties for 10 weeks. Complete degradation is reached at 6 months. PLA-b-PEG-b-PLA RIBS have the required in vitro degradation characteristics to secure biliary anastomosis in liver transplantation and envision in vivo applications.

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. Secondary objectives comprised qualitative assessment of endoscopic ultrasound images and comparative assessment of acquisition time between the two techniques. MATERIAL AND METHODS: An anatomic non-inferiority study was conducted on 6 shoulders from 3 subjects with a mean age of 84 years. After ultrasound examination by a radiologist specializing in osteoarticular imaging, shoulder arthroscopy was performed by a single specialized surgeon, using an ultrasound endoscope. Number of visualized structures and image quality were assessed by independent observers. RESULTS: Ten of the 11 structures of interest (91%) were visualizable on endoscopic ultrasound, versus 4 (36%) on external ultrasound (p<0.05). Mean endoscopic acquisition time was 9.5±6.3minutes [range, 5;22]. In the 11 structures, image quality was better on endoscopic than external ultrasound, except for the acromioclavicular joint, where quality was better on external ultrasound, and the lateral side of the rotator cuff, where quality was equivalent. CONCLUSION: The present study demonstrated equivalence between endoscopic and external ultrasound for shoulder exploration. LEVEL OF EVIDENCE: IV, Non-inferiority cadaver study.