Observational study of adherence to European clinical practice guidelines for the management of acute coronary syndrome in revascularized versus non-revascularized patients – the CONNECT Study

SummaryBackgroundThe CONNECT study compared clinician adherence to guideline-recommended secondary prevention therapies prescribed at discharge for patients hospitalized for acute coronary syndrome (ACS) in those managed initially with percutaneous coronary intervention (PCI; revascularized) and those who did not undergo revascularization.MethodsPatients aged greater than or equal to 18 years, hospitalized for a documented ST-segment elevation or non-ST-segment elevation ACS, were enrolled consecutively over 1 month at 238 sites in France.ResultsCompared with revascularized patients (n=870), non-revascularized patients (n=706) were significantly older, and a greater proportion were women, had high-blood pressure, type-2 diabetes or a history of atherothrombotic or cardiac disease, but a smaller proportion had a history of coronary angioplasty. On discharge, non-revascularized patients were prescribed beta-blockers, aspirin, statins, angiotensin-converting enzyme inhibitors or adenosine diphosphate receptor antagonists less frequently than revascularized patients. An adherence score greater than or equal to 80% (at least four of the five recommended agents prescribed at discharge) was found in 96.7% of revascularized patients and 74.4% of non-revascularized patients (P<0.001).ConclusionsDespite a similar or even higher level of cardiovascular risk, non-revascularized ACS patients were prescribed guideline-recommended secondary prevention therapy less frequently than revascularized patients

A Formal Framework for Modeling and Prediction of Aircraft Operability using SysML

Aircraft operability characterizes the ability of anaircraft to meet operational requirements in terms of reliability, availability, risks and costs. Airlines policy must cope with operational decision-making and maintenance planning to handle the impacts of any event that generates a maintenance demand during operations. Aircraft operability is therefore considereda major requirement by each airline. The subject reaches a complexity level that deserves investigations in a Model-Based System Engineering (MBSE) approach enabling abstractions, as well as simulation and formal verification of models. In this paper, aircraft operability is modeled using Finite State Machines(FSM) supported by SysML. Simulation and model checking techniques are used to evaluate the impact of an event on airline operations using operability Key Performance Indicators (KPIs)such as reliability, availability and cost. The modeling frameworkis demonstrated on a case study of air-conditioning pack. This kind of operability analysis helps to project the potential impactof aircraft design on airline operations early in the aircraft development

Operability projection of major aircraft components during early aircraft design

Aircraft operational performance is a key factor to achieve airline profitability and meet passenger expectations. It is determined by the ‘operability’ of major aircraft components along with the operational context in which the aircraft operates. Operability is the ability of a system to meet its operational requirements in terms of reliability, availability and costs. This paper proposes a approach to take into account the type of technology employed in a major aircraft component to perform operability projections. An operability model is developed using Bayesian networks that helps project the influence of different input parameters on the operational performance of the major aircraft components. An approach combining engineering and in-service data is used to instantiate the different parameters and train the Bayesian network model. The trained model can be used by system designers to perform operability projections of different design solutions through Bayesian inference and make trade-off studies from an operability point of view. Clustering of the data using unsupervised learning is also addressed in this paper to identify the best combinations of input parameters that can produce the desirable operational performance

Infarctus du myocarde récidivant après un intervalle libre révélant un phéochromocytome

LILLE2-BU Santé-Recherche (593502101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Les métastases cardiaques intra-cavitaires (à propos de trois cas et revue de la littérature)

LILLE2-BU Santé-Recherche (593502101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Suspicion de syndromes coronariens aigus sans élévation du segment ST (194 patients admis à l'Unité de soins intensifs cardiologiques du CHRU de Lille)

LILLE2-BU Santé-Recherche (593502101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

INSUFFISANCE CARDIAQUE POST-TRANSPLANTATION HEPATIQUE (UNE DYSFONCTION SYSTOLIQUE POTENTIELLEMENT REVERSIBLE ? A PROPOS DE 7 CAS)

LILLE2-BU Santé-Recherche (593502101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Learning-Enhanced Adaptive Robust GNSS Navigation in Challenging Environments

International audienceGlobal Navigation Satellite System (GNSS) is the widely used technology when it comes to outdoor positioning. But it has severe limitations with regard to safety-critical applications involving unmanned autonomous systems. Namely, the positioning performance degrades in harsh propagation environment such as urban canyons. In this letter we propose a new algorithm for GNSS navigation in challenging environments based on robust statistics. M-estimators showed promising results in this context, but are limited by some fixed hyper-parameters. Our main idea is to adapt this parameter, for the Huber cost function, to the current environment in a data-driven manner. Doing so, we also present a simple yet efficient way of learning with satellite data, whose number may vary over time. Focusing the learning problem on a single parameter enables to efficiently learn with a lightweight neural network. The generalization capability and the positioning performance of the proposed method are evaluated in multiple contexts scenarios (open-sky, trees, urban and urban canyon), with two distinct GNSS receivers, and in an airplane ground inspection scenario. The maximum positioning error is reduced by up to 68% with respect to M-estimators

A hybrid approach of machine learning and expert knowledge for projection of aircraft operability

Aircraft operational performance is a key driving factor to flight punctuality and airline profitability. The ability of a system to meet its operational requirements in terms of reliability, availability and costs is termed as 'Operability'. It is of high importance for aircraft manufacturers to project operability during the early stages of development of an aircraft in order to make trade-off studies. This paper proposes a hybrid approach of using machine learning and expert knowledge to aid the projection of aircraft operational performance during the early design stages. This approach aims to benefit from the huge amount of in-service data available from the current and past fleet of aircraft. Hence, machine learning techniques are used to learn how different technical issues and their associated maintenance activities impact aircraft operations. Expert knowledge is used to establish the default rules of the simulation model used for the operability projection. Results from machine learning are used to improve these rules allowing one to make holistic projections of the operational performance of future aircraft. This approach allows one to estimate the elapsed time in different operational states of an aircraft like flying, turn-around, etc. which can then be used to calculate different operability Key Performance Indicators (KPIs) like aircraft reliability and maintenance unavailability

Holistic Operability Projection during Early Aircraft Design

Aircraft operational performance is one of the key drivers to airline profitability and punctuality. Along with safety and technical performance, aircraft operational performance needs to be projected from the early stages of development to design an aircraft that can fully meet the expectations of airlines and passengers. The ability of a system to meet its operational requirements in terms of reliability, availability and costs is termed as ‘Operability’. This paper proposes a method to model the operability of an aircraft during early design and use it to predict its operational performance. Initially, in-service data is used to create a reference baseline for a system of interest. For a new design, the designers evaluate the changes (deltas) in terms of few high-level metrics from an operations point of view called Consolidated Operability Metrics. An operability model is developed using Bayesian networks that is then used to project the changes in operational performance of the new design in comparison to the baseline. This method will help aircraft architects in conducting trade-off studies during early design from an operational point of view