Trichobezoar gastrique - à propos de deux cas

Le trichobezoar gastrique est une affection rare (un peu plus d’une dizaine de cas dans la littérature), qui affecte essentiellement des jeunes filles perturbées par des désordres psychologiques. Les auteurs rapportent deux cas de jeunes filles, hospitalisées pour volumineuse masse épigastrique. La fibroscopie gastrique a posé le diagnostic de trichobezoar. Une exérèse chirurgicale a été réalisée à travers une gastrotomie, sans complications. Un suivi psychiatrique des deux patientes a été recommandé. Le trichobezoar gastrique désigne l’accumulation inhabituelle de cheveux au niveau de l’estomac. Son diagnostic est facile en présence d’un contexte de trichophagie évocateur. La fibroscopie œsogastroduodénale est l’examen de référence permettant la visualisation du trichobezoar dont le traitement est essentiellement chirurgical

Duplication iléale chez l’adulte révélée par une perforation

Les duplications intestinales sont des malformations digestives rares (0,2 % des malformations de l’enfant). Elles sont diagnostiquées généralement avant l’âge d’un an, mais elles peuvent rester asymptomatiques et diagnostiquées à l’âge adulte. Le diagnostic est fait le plus souvent lors de laparotomie faite en urgence devant une complication. Elles sont traitées par résection chirurgicale

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts

Abstract Background and Aims: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. Methods: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal–external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. Results: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. Conclusions: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors.Abstract Background and Aims: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. Methods: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal–external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. Results: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. Conclusions: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts

BACKGROUND AND AIMS: Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. METHODS: The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal-external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. RESULTS: There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. CONCLUSIONS: More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors

Wavelet estimation for point processes

Wavelet theory constitutes one of the most significant mathematical advances for signal processing, and thus presents a great interest in point process analysis. For instance, numerous approaches have been explored to estimate the first-order intensity of a point process with wavelets. In this thesis, we intend to consolidate existing results in wavelet-based linear estimation and investigate further applications of wavelets on point processes in the context of multiresolution analysis. We initially take this wavelet-based approach to estimate the first and higher-order intensities of a point process in any finite dimension and under a continuous spatial setting. We perform a statistical study of wavelet linear estimators when the observed events are located in a hyperrectangle of R^d. It is notably shown that the linear estimator of the complete k-th order intensity is the product of k linear estimators of the first-order intensity. Such wavelet modelling also motivates the construction of a first-order multiresolution analysis, through the definition of properties at different scales, termed J-th level homogeneity and L-th level innovation. Likelihood ratio tests for these properties are provided and studied under Poisson processes and Haar wavelets. A key result is that the applicability of these tests is linked to the product of the number of realizations and the expectation measure of the process. This means that one can use the asymptotic distributions of the test statistics from a single point pattern if the expectation measure is itself sufficiently high. The hypothesis test for L-th level innovation is then used to design new data-driven thresholding strategies, each based on a different grouping of wavelet coefficients. Our thresholding methods are studied through extensive simulations and applied to NetFlow data to exhibit the differences between human and automated behaviour. Since wavelet estimation of Cox processes has received very little treatment to this day, we provide new developments in this topic essentially through the wavelet-based estimation of the pointwise probability density or mass function for the intensity field. The behaviour of this estimator is studied with example Cox process models for different wavelets and resolutions, followed by an application to firing patterns from virtual reality military training. We eventually extend the idea of J-th level homogeneity to Cox processes by re-defining it through the mean intensity field, which we then test with a method based on Hotelling’s t-squared statistic.Open Acces