Clinical and genetic data of Huntington disease in Moroccan patients

Background: Huntington's disease (HD) occurs worldwide with prevalence varying from 0.1 to 10 /100,000 depending of the ethnic origin. Since no data is available in the Maghreb population, the aim of this study is to describe clinical and genetic characteristics of Huntington patients of Moroccan origin.Methods: Clinical and genetics data of 21 consecutive patients recruited from 2009 to 2014 from the outpatient clinic of six medical centers were analyzed. Statistical analysis was performed using descriptive statistics.Results: Twenty one patients from 17 families were diagnosed positive for the IT15 gene CAG expansion. Clinical symptoms were predominantly motor (19/21). Twelve patients had psychiatric and behavioral disorders, and 11 patients had cognitive disorders essentially of memory impairment. Analysis of genetic results showed that 5 patients had reduced penetrant (RP) alleles and 16 had fully penetrant (FP) alleles. The mean CAG repeat length in patients with RP alleles was 38.4 ± 0.54, and 45.37 ± 8.30 in FP alleles. The age of onset and the size of the CAG repeat length showed significant inverse correlation (p <0.001, r = -0.754).Conclusion: Clinical and genetic data of Moroccan patients are similar to those of Caucasian populations previously reported in the literature.Keywords: Huntington disease/diagnosis, Huntington disease/epidemiology, Huntington disease/genetics, Trinucleotide repeat expansio

Clinical and genetic data of Huntington disease in Moroccan patients

Background: Huntington's disease (HD) occurs worldwide with prevalence varying from 0.1 to 10 /100,000 depending of the ethnic origin. Since no data is available in the Maghreb population, the aim of this study is to describe clinical and genetic characteristics of Huntington patients of Moroccan origin. Methods: Clinical and genetics data of 21 consecutive patients recruited from 2009 to 2014 from the outpatient clinic of six medical centers were analyzed. Statistical analysis was performed using descriptive statistics. Results: Twenty one patients from 17 families were diagnosed positive for the IT15 gene CAG expansion. Clinical symptoms were predominantly motor (19/21). Twelve patients had psychiatric and behavioral disorders, and 11 patients had cognitive disorders essentially of memory impairment. Analysis of genetic results showed that 5 patients had reduced penetrant (RP) alleles and 16 had fully penetrant (FP) alleles. The mean CAG repeat length in patients with RP alleles was 38.4 \ub1 0.54, and 45.37 \ub1 8.30 in FP alleles. The age of onset and the size of the CAG repeat length showed significant inverse correlation (p <0.001, r = -0.754). Conclusion: Clinical and genetic data of Moroccan patients are similar to those of Caucasian populations previously reported in the literature

Dexamethasone Use and Mortality in Hospitalized Patients with Coronavirus Disease 2019: a Multicenter Retrospective Observational Study

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Key Contributions in Clinical Research Informatics

Summary Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2020. Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between two section editors and the editorial team was organized to finally conclude on the selected four best papers. Results: Among the 877 papers published in 2020 and returned by the search, there were four best papers selected. The first best paper describes a method for mining temporal sequences from clinical documents to infer disease trajectories and enhancing high-throughput phenotyping. The authors of the second best paper demonstrate that the generation of synthetic Electronic Health Record (EHR) data through Generative Adversarial Networks (GANs) could be substantially improved by more appropriate training and evaluation criteria. The third best paper offers an efficient advance on methods to detect adverse drug events by computer-assisting expert reviewers with annotated candidate mentions in clinical documents. The large-scale data quality assessment study reported by the fourth best paper has clinical research informatics implications, in terms of the trustworthiness of inferences made from analysing electronic health records. Conclusions: The most significant research efforts in the CRI field are currently focusing on data science with active research in the development and evaluation of Artificial Intelligence/Machine Learning (AI/ML) algorithms based on ever more intensive use of real-world data and especially EHR real or synthetic data. A major lesson that the coronavirus disease 2019 (COVID-19) pandemic has already taught the scientific CRI community is that timely international high-quality data-sharing and collaborative data analysis is absolutely vital to inform policy decisions

Identification automatique des patients avec fractures ostéoporotiques à partir de comptes rendus médicaux

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Multilabel classification of medical concepts for patient clinical profile identification

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Classification multilabel de concepts médicaux pour l’identification du profil clinique du patient

International audienceLa première tâche du Défi fouille de textes 2021 a consisté à extraire automatiquement, à partir de cas cliniques, les phénotypes pathologiques des patients regroupés par tête de chapitre du MeSH-maladie. La solution présentée est celle d’un classifieur multilabel basé sur un transformer. Deux transformers ont été utilisés : le camembert-large classique (run 1) et le camembert-large fine-tuné (run 2) sur des articles biomédicaux français en accès libre. Nous avons également proposé un modèle « bout-enbout », avec une première phase d’extraction d’entités nommées également basée sur un transformer de type camembert-large et un classifieur de genre sur un modèle Adaboost. Nous obtenons un très bon rappel et une précision correcte, pour une F1-mesure autour de 0,77 pour les trois runs. La performance du modèle « bout-en-bout » est similaire aux autres méthodes

How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room?

The growing need for emergency imaging has greatly increased the number of conventional X-rays, particularly for traumatic injury. Deep learning (DL) algorithms could improve fracture screening by radiologists and emergency room (ER) physicians. We used an algorithm developed for the detection of appendicular skeleton fractures and evaluated its performance for detecting traumatic fractures on conventional X-rays in the ER, without the need for training on local data. This algorithm was tested on all patients (N = 125) consulting at the Louis Mourier ER in May 2019 for limb trauma. Patients were selected by two emergency physicians from the clinical database used in the ER. Their X-rays were exported and analyzed by a radiologist. The prediction made by the algorithm and the annotation made by the radiologist were compared. For the 125 patients included, 25 patients with a fracture were identified by the clinicians, 24 of whom were identified by the algorithm (sensitivity of 96%). The algorithm incorrectly predicted a fracture in 14 of the 100 patients without fractures (specificity of 86%). The negative predictive value was 98.85%. This study shows that DL algorithms are potentially valuable diagnostic tools for detecting fractures in the ER and could be used in the training of junior radiologists

External validation of prognostic scores for Covid-19: a multicentre cohort study of patients hospitalized in Greater Paris University Hospitals

International audienceThe Coronavirus disease 2019 (Covid-19) has led to an unparalleled influx of patients. Prognostic scores could help optimizing healthcare delivery, but most of them have not been comprehensively validated. We aim to externally validate existing prognostic scores for Covid-19. Methods We used "Covid-19 EvidenceAlerts" (McMaster University) to retrieve high-quality prognostic scores predicting death or intensive care unit (ICU) transfer from routinely collected data. We studied their accuracy in a retrospective multicentre cohort of adult patients hospitalized for Covid-19 from January 2020 to April 2021 in the Greater Paris University Hospitals. Areas under the receiver operating characteristic curves (AUC) were computed for the prediction of the original outcome, 30-day in-hospital mortality and the composite of 30-day in-hospital mortality or ICU transfer. Results We included 14,343 consecutive patients, 2,583 (18%) died and 5,067 (35%) died or were transferred to the ICU. We examined 274 studies and found 32 scores meeting the inclusion criteria: 19 had a significantly lower AUC in our cohort than in previously published validation studies for the original outcome; 25 performed better to predict in-hospital mortality than the composite of in-hospital mortality or ICU transfer; 7 had an AUC >0.75 to predict in-hospital mortality; 2 had an AUC >0.70 to predict the composite outcome. Conclusion Seven prognostic scores were fairly accurate to predict death in hospitalized Covid-19 patients. The 4C Mortality Score and the ABCS stand out because they performed as well in our cohort and their initial validation cohort, during the first epidemic wave and subsequent waves, and in younger and older patients

Effects of anticipated prescription of radiography by the triage nurse on the waiting time in an emergency department

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