Conectar
by Milhan Chaze, Laurent Mériade, Corinne Rochette, Mélina Bailly, Rea Bingula, Christelle Blavignac, Martine Duclos, Bertrand Evrard, Anne Cécile Fournier, Lena Pelissier, David Thivel, on behalf of CAUVIM-19 Group
BackgroundWork on long COVID-19 has mainly focused on clinical care in hospitals. Thermal spa therapies represent a therapeutic offer outside of health care institutions that are nationally or even internationally attractive. Unlike local care (hospital care, general medicine, para-medical care), their integration in the care pathways of long COVID-19 patients seems little studied. The aim of this article is to determine what place french thermal spa therapies can take in the care pathway of long COVID-19 patients.
MethodsBased on the case of France, we carry out a geographic mapping analysis of the potential care pathways for long COVID-19 patients by cross-referencing, over the period 2020–2022, the available official data on COVID-19 contamination, hospitalisations in intensive care units and the national offer of spa treatments. This first analysis allows us, by using the method for evaluating the attractiveness of an area defined by David Huff, to evaluate the accessibility of each French department to thermal spas.
ResultsUsing dynamic geographical mapping, this study describes two essential criteria for the integration of the thermal spa therapies offer in the care pathways of long COVID-19 patients (attractiveness of spa areas and accessibility to thermal spas) and three fundamental elements for the success of these pathways (continuity of the care pathways; clinical collaborations; adaptation of the financing modalities to each patient). Using a spatial attractiveness method, we make this type of geographical analysis more dynamic by showing the extent to which a thermal spa is accessible to long COVID-19 patients.
ConclusionBased on the example of the French spa offer, this study makes it possible to place the care pathways of long COVID-19 patients in a wider area (at least national), rather than limiting them to clinical and local management in a hospital setting. The identification and operationalization of two geographical criteria for integrating a type of treatment such as a spa cure into a care pathway contributes to a finer conceptualization of the construction of healthcare pathways.
The objective of this study was to develop clinical classifiers aiming to identify prevalent ascending aortic dilatation in patients with bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV).
A prospective, single-centre and observational cohort.
The study involved 543 BAV and 491 TAV patients with aortic valve disease and/or ascending aortic dilatation, excluding those with coronary artery disease, undergoing cardiothoracic surgery at the Karolinska University Hospital (Sweden).
Predictors of high risk of ascending aortic dilatation (defined as ascending aorta with a diameter above 40 mm) were identified through the application of machine learning algorithms and classic logistic regression models.
Comprehensive multidimensional data, including valve morphology, clinical information, family history of cardiovascular diseases, prevalent diseases, demographic details, lifestyle factors, and medication.
BAV patients, with an average age of 60.4±12.4 years, showed a higher frequency of aortic dilatation (45.3%) compared with TAV patients, who had an average age of 70.4±9.1 years (28.9% dilatation, p
Cardiovascular risk profiles appear to be more predictive of aortopathy in TAV patients than in patients with BAV. This adds evidence to the fact that BAV-associated and TAV-associated aortopathy involves different pathways to aneurysm formation and highlights the need for specific aneurysm preventions in these patients. Further, our results highlight that machine learning approaches do not outperform classical prediction methods in addressing complex interactions and non-linear relations between variables.
FreshRSS 