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.

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.

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.

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.

Body interventional procedures: which is the best method for CT guidance?

OBJECTIVES: To compare sequential fluoroscopy guidance with spiral guidance in terms of safety, effectiveness, speed and radiation in interventional whole body procedures. METHODS: This study was a retrospective analysis of data from the prospective, randomised controlled, multicentre CTNAV2 study. The present analysis included 385 patients: 247 in the sequential group (SEQ) and 138 in the spiral group (SPI). Safety was assessed by the number of major complications. Effectiveness was measured according to the number of targets reached. Data on procedural time and radiation delivered to patients were also collected. RESULTS: There was no significant difference between the two groups (SEQ vs SPI) regarding the success rate (99.6% vs 99.3%, p = 0.680), procedural time (7 min 40 s ± 5 min 48 s vs 7 min 13 s ± 7 min 33 s, p = 0.507), or major complications (2.43% vs 5.8%, p = 0.101). Radiation dose to patients was 84% lower in the sequential group (54.8 ± 51.8 mGy cm vs 352.6 ± 404 mGy cm, p < 0.0001). CONCLUSIONS: Sequential CT fluoroscopy-guided whole-body interventional procedures seems to be as safe, effective and fast as spiral guidance, while also yielding a significant decrease in the radiation dose to patients. KEY POINTS: • Sequential CT fluoroscopy and spiral acquisition are comparable in terms of safety, effectiveness and speed. • Procedural times are comparable despite an increased number of acquisitions in sequential fluoroscopy. • Radiation dose to patients is 84% lower in sequential fluoroscopy compared with spiral CT.

Prediction of brace effect in scoliotic patients: blinded evaluation of a novel brace simulator-an observational cross-sectional study.

PURPOSE: Bracing is the most commonly used treatment for scoliosis. But braces remain predominantly "handcrafted." Our objective was to create a novel brace simulator using a high-fidelity 3D "avatar" of the patient's trunk. METHODS: An observational cross-sectional study was constructed. The inclusion criteria were patients with a moderate idiopathic scoliosis (between 15° and 35° of Cobb angle) aged between 9 and 15 years old with an indication of brace treatment. Twenty-nine scoliotic patients, 25 girls and four boys, with a mean age of 12.4 years were included. Twenty right thoracic and 14 left lumbar were measured with a mean Cobb angle of 24°. 3D "avatars" were generated using a novel technology called the "anatomy transfer." Biomedical simulations were conducted by engineers who were blinded to the clinical effect of the real patient brace. The in-brace Cobb angle effect (real effect) was compared with the virtual numeric in-brace Cobb angle observed using the blindly constructed avatar (simulation effect). RESULTS: Real and simulated in-brace Cobb angle were compared using a paired two-sided Student's t test. The real mean Cobb angle was 11° and 17° in the simulation which was statistically significant. The strength of prediction of the simulation was assessed for each individual patient; 76% of the real in-brace Cobb angles had good and moderate prediction (± 10°). CONCLUSIONS: Incorporating high-fidelity copy of the entire 3D shape of the patient's trunk and multiple 3D-reconstructed bony images into an anatomical reference avatar resulted in moderate-to-good prediction of brace effect in three quarters of patients. These slides can be retrieved under Electronic Supplementary Material.

Distinguishing surgical behavior by sequential pattern discovery.

OBJECTIVE: Each surgical procedure is unique due to patient's and also surgeon's particularities. In this study, we propose a new approach to distinguish surgical behaviors between surgical sites, levels of expertise and individual surgeons thanks to a pattern discovery method. METHODS: The developed approach aims to distinguish surgical behaviors based on shared longest frequent sequential patterns between surgical process models. To allow clustering, we propose a new metric called SLFSP. The approach is validated by comparison with a clustering method using Dynamic Time Warping as a metric to characterize the similarity between surgical process models. RESULTS: Our method outperformed the existing approach. It was able to make a perfect distinction between surgical sites (accuracy of 100%). We reached an accuracy superior to 90% and 85% for distinguishing levels of expertise and individual surgeons. CONCLUSION: Clustering based on shared longest frequent sequential patterns outperformed the previous study based on time analysis. SIGNIFICANCE: The proposed method shows the feasibility of comparing surgical process models, not only by their duration but also by their structure of activities. Furthermore, patterns may show risky behaviors, which could be an interesting information for surgical training to prevent adverse events.

Augmented Reality Using Transurethral Ultrasound for Laparoscopic Radical Prostatectomy: Preclinical Evaluation.

PURPOSE: To guide the surgeon during laparoscopic or robot-assisted radical prostatectomy an innovative laparoscopic/ultrasound fusion platform was developed using a motorized 3-dimensional transurethral ultrasound probe. We present what is to our knowledge the first preclinical evaluation of 3-dimensional prostate visualization using transurethral ultrasound and the preliminary results of this new augmented reality. MATERIALS AND METHODS: The transurethral probe and laparoscopic/ultrasound registration were tested on realistic prostate phantoms made of standard polyvinyl chloride. The quality of transurethral ultrasound images and the detection of passive markers placed on the prostate surface were evaluated on 2-dimensional dynamic views and 3-dimensional reconstructions. The feasibility, precision and reproducibility of laparoscopic/transurethral ultrasound registration was then determined using 4, 5, 6 and 7 markers to assess the optimal amount needed. The root mean square error was calculated for each registration and the median root mean square error and IQR were calculated according to the number of markers. RESULTS: The transurethral ultrasound probe was easy to manipulate and the prostatic capsule was well visualized in 2 and 3 dimensions. Passive markers could precisely be localized in the volume. Laparoscopic/transurethral ultrasound registration procedures were performed on 74 phantoms of various sizes and shapes. All were successful. The median root mean square error of 1.1 mm (IQR 0.8-1.4) was significantly associated with the number of landmarks (p = 0.001). The highest accuracy was achieved using 6 markers. However, prostate volume did not affect registration precision. CONCLUSIONS: Transurethral ultrasound provided high quality prostate reconstruction and easy marker detection. Laparoscopic/ultrasound registration was successful with acceptable mm precision. Further investigations are necessary to achieve sub mm accuracy and assess feasibility in a human model.

Morphologic and neuropsychological patterns in patients suffering from Alzheimer's disease.

INTRODUCTION: We conducted a retrospective study to identify morphological subgroups of patients referred for AD or aMCI and to seek for differences across neuropsychological performances. METHODS: One hundred forty-five patients (mean age = 76.01, 88 women and 57 men) referred for AD, either at the stage of dementia or aMCI, were examined using structural MRI. Five observers reviewed blindly twice all examinations. We rated microangiopathy, hippocampal, parietal atrophies, including a gradient of fronto-parietal atrophy (GFPA). A multiple component analysis (MCA) followed by a hierarchical ascending classification was conducted to identify morphologically distinct subgroups. Among these, 76 patients completed all the neuropsychological tests. Univariate and multivariate analyses were further conducted on these data across morphological subgroups. The institutional review board approved the research protocol. RESULTS: Inter- and intra-raters' agreements were excellent and very good for microangiopathy and hippocampal atrophy ratings. They were higher for GFPA than for the parietal atrophy scale. MCA without priors identified three groups: group 1 was characterized by no/discrete microangiopathy, severe hippocampal, and predominant parietal atrophy; group 2 had significant microangiopathy, severe hippocampal atrophy, and no predominant parietal atrophy; group 3 had a mild hippocampal atrophy and parietal atrophies. In group 1, working memory profile was less impaired than in group 2 (p = 0.01). Neuropsychological performances of group 3 were higher in most domains. CONCLUSION: Combined characterization of microangiopathy, hippocampal, parietal, and GFPA allows identifying morphological subgroups among patients referred for AD and at risk. These groups have some neuropsychological differences, suggesting different pathophysiological mechanisms or co-existing conditions.

Are normal hips being labeled as pathologic? A CT-based method for defining normal acetabular coverage.

BACKGROUND: Plain radiographic measures of the acetabulum may fail to accurately define coverage or pathomorphology such as impingement or dysplasia. CT scans might provide more precise measurements for overcoverage and undercoverage. However, a well-defined method for such CT-based measurements and normative data regarding CT-based acetabular coverage is lacking. QUESTIONS/PURPOSES: The purposes of the study were (1) to develop a method for evaluation of percent coverage of the femoral head by the acetabulum; and (2) to define normative data using a cohort of asymptomatic patient hip and pelvic CT scans and evaluate the variability in acetabular version for asymptomatic patients with normal lateral coverage (lateral center-edge angle [LCEA] 20°-40°) that has previously been defined as abnormal based on radiographic parameters. METHODS: Two-hundred thirty-seven patients (474 hips) with hip CT scans obtained for reasons other than hip-related pain were evaluated. The scans were obtained from a hospital database of patients who underwent CT evaluation of abdominal trauma or pain. In addition, hips with obvious dysplasia (LCEA < 20°) or profunda (LCE > 40°) were excluded resulting in a final cohort of 222 patients (409 hips [115 men, 107 women]) with CT scans and a mean age of 25 ± 3 years. CT scan alignment was corrected along the horizontal and vertical axis and percent acetabular coverage around the clockface (3 o'clock = anterior), and regional (anterior, superior, posterior) and global surface area coverage was determined. Percent coverage laterally was correlated with the LCEA and the presence and prevalence of cranial retroversion (crossover sign) and a positive posterior wall sign were determined. RESULTS: The mean regional percent femoral head surface area coverage for the asymptomatic cohort was 40% ± 2% anteriorly, 61% ± 3% superiorly, and 48% ± 3% posteriorly. Mean global coverage of the femoral head was 40% ± 2%. The local coverage anteriorly (3 o'clock) was 38% ± 3%, laterally (12 o'clock) was 67% ± 2%, and posteriorly (9 o'clock) was 52% ± 3%. The mean lateral coverage represented a mean LCEA of 31° (± 1 SD). Fifteen percent of hips demonstrated cranial retroversion that would correlate with a crossover sign, and 30% had < 50% posterior coverage that would correlate with a positive posterior wall sign on an anteroposterior pelvis radiograph. In addition, male hips had a higher prevalence of a crossover sign (19%; 95% confidence interval [CI], 14%-25% versus 11%; 95% CI, 7%-16%; p = 0.03) and posterior wall sign (46%; 95% CI. 39%-53% versus 13%; 95% CI, 9%-19%; p < 0.001) compared with women. A positive crossover sign or posterior wall sign was present for 113 male hips (53%; 95% CI, 46%-60%) compared with 39 female hips (20%; 95% CI, 15%-26%; p < 0.001). CONCLUSIONS: This study provides normative coverage data and a reproducible method for evaluating acetabular coverage. Cranial acetabular retroversion (crossover sign) and a positive posterior wall sign were frequent findings in a young asymptomatic cohort and might be a normal variant rather than pathologic in a significant number of cases.

External validation of the RENAL nephrometry score in renal tumours treated by partial nephrectomy.

OBJECTIVE: To evaluate the RENAL nephrometry score (RNS) as a predictor of the perioperative outcomes of a partial nephrectomy. PATIENTS AND METHODS: A retrospective review of 177 consecutive patients who were candidates for an open partial nephrectomy (OPN, n = 159) or a laparoscopic partial nephrectomy (LPN, n = 18) from August 2008 to January 2011 was undertaken. Tumour complexity was stratified into three categories: low (4-6), moderate (7-9) and high (10-12) complexity. Complications, and surgical and renal outcomes were recorded and analysed. Predictors of conversion to radical nephrectomy (RN) and complications were assessed using univariate and multivariate logistic regression. Multiple linear regression was used to evaluate the prediction of postoperative estimated glomerular filtration rate (eGFR) and warm ischaemia time (WIT). RESULTS: The median RNS was 7 (interquartile range 6-9). Tumour complexity was assessed as low in 72 (40.6%), moderate in 87 (49.2%) and high in 18 patients (10.2%). There were no significant differences among the three groups with respect to demographic characteristics, operating time, estimated blood loss, transfusion, length of stay, complications and positive surgical margins. Conversion to RN occurred in 29 patients (16.3%). RNS was significantly associated with an increased risk of conversion to RN (odds ratio [OR] = 3.5, P = 0.01 and OR = 6.7, P = 0.005, respectively, for moderate vs low, and high vs low complexity groups). On multivariate analysis, RNS was the only independent predictor of WIT (P = 0.03) and conversion to RN (P = 0.008), but failed to predict postoperative eGFR (P = 0.84) and the occurrence of major complications (P = 0.91). CONCLUSIONS: In the present series, RNS predicted an increased risk of conversion to RN and prolonged WIT. RNS was not a predictor of complications and postoperative renal function.

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.

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.

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.

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.

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.

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.

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.