Méthodologie orientée objet pour le traitement temps réel du signal (application au monitorage médical)

LILLE1-BU (590092102) / SudocSudocFranceF

Relevance of a novel external dynamic distraction device for treating back pain.

Low back pain is a common, expensive, and disabling condition in industrialized countries. There is still no consensus for its ideal management. Believing in the beneficial effect of traction, we developed a novel external dynamic distraction device. The purpose of this work was to demonstrate that external distraction allows limiting the pressure exerted in standing-up position on the lower intervertebral discs. Numerical and cadaveric studies were used as complementary approaches. Firstly, we implemented the device into a numerical model of a validated musculoskeletal software (Anybody Modeling System) and we calculated the lower disc pressure while traction forces were applied. Secondly, we performed an anatomical study using a non-formalin preserved cadaver placed in a sitting position. A pressure sensor was placed in the lower discs under fluoroscopic control through a Jamshidi needle. The intradiscal pressure was then measured continuously at rest while applying a traction force of 200 N. Both numerical and cadaveric studies demonstrated a decrease in intradiscal pressures after applying a traction force with the external device. Using the numerical model, we showed that tensile forces below 500 N in total were sufficient. The application of higher forces seems useless and potentially deleterious. External dynamic distraction device is able to significantly decrease the intradiscal pressure in a sitting or standing position. However, the therapeutic effects need to be proven using clinical studies

Development and usage of an anesthesia data warehouse: lessons learnt from a 10-year project

International audienceAbstract This paper describes the development and implementation of an anesthesia data warehouse in the Lille University Hospital. We share the lessons learned from a ten-year project and provide guidance for the implementation of such a project. Our clinical data warehouse is mainly fed with data collected by the anesthesia information management system and hospital discharge reports. The data warehouse stores historical and accurate data with an accuracy level of the day for administrative data, and of the second for monitoring data. Datamarts complete the architecture and provide secondary computed data and indicators, in order to execute queries faster and easily. Between 2010 and 2021, 636 784 anesthesia records were integrated for 353 152 patients. We reported the main concerns and barriers during the development of this project and we provided 8 tips to handle them. We have implemented our data warehouse into the OMOP common data model as a complementary downstream data model. The next step of the project will be to disseminate the use of the OMOP data model for anesthesia and critical care, and drive the trend towards federated learning to enhance collaborations and multicenter studies