Associations of diabesity with all-cause and cardiovascular disease mortality: Findings from the Mexico City Prospective Study.

AIM: To investigate the joint associations of diabetes and obesity with all-cause and cardiovascular disease (CVD) mortality in the Mexico City Prospective Study. MATERIALS AND METHODS: In total, 154 128 participants (67.2% women) were included in this prospective analysis. Diabetes was self-reported, while body mass index was used to calculate obesity. Using diabetes and obesity classifications, six groups were created: (a) normal (no diabetes and normal weight); (b) normal weight and diabetes; (c) overweight but not diabetes (overweight); (d) overweight and diabetes (prediabesity); (e) obesity but not diabetes (obesity); and (f) obesity and diabetes (diabesity). Associations between these categories and outcomes were investigated using Cox proportional hazard models adjusted for confounder factors. RESULTS: During 18.3 years of follow-up, 27 197 (17.6%) participants died (28.5% because of CV causes). In the maximally adjusted model, participants those with the highest risk {hazard ratio (HR): 2.37 [95% confidence interval (CI): 2.24-2.51]}, followed by those with diabesity [HR: 2.04 (95% CI: 1.94-2.15)]. Similar trends of associations were observed for CVD mortality. The highest CV mortality risk was observed in individuals with diabesity [HR: 1.80 (95% CI: 1.63-1.99)], followed by normal weight and diabetic individuals [HR: 1.78 (95% CI: 1.60-1.98)]. CONCLUSION: This large prospective study identified that diabetes was the main driver of all-cause and CVD mortality in all the categories studied, with diabesity being the riskiest. Given the high prevalence of both conditions in Mexico, our results reinforce the importance of initiating prevention strategies from an early age.

Desarrollo de una app móvil para la evaluación preoperatoria de la tomografía de senos paranasales: un paso hacia la inteligencia artificial / Development of a Movil App for the Preoperative Evaluation of Sinus CT Scan: One Step Towards Artificial Intelligence

Introducción: la revolución tecnológica que hemos vivido en los últimos años ha generado un extendido uso de la inteligencia artificial (IA) como tecnología de base para el desarrollo de diversos sistemas y soluciones en medicina. En el campo de la otorrinolaringología, estamos viendo hasta ahora los primeros esfuerzos para aprovechar este surgimiento. Objetivo: el presente proyecto busca describir el proceso de desarrollo de una app móvil creada gracias al trabajo colaborativo entre otorrinolaringólogos e ingenieros biomédicos, que tiene la intención de optimizar la evaluación preoperatoria de la tomografía de senos paranasales (TC de SPN). Métodos: el desarrollo de la app siguió el método de priorización para especificaciones de MoSCoW. Utilizamos la información recolectada de encuestas realizadas a 29 expertos en rinología de diferentes partes del mundo, quienes evaluaron variantes anatómicas en la tomografía y se utilizaron dos modelos de regresión para la predicción de dificultad y riesgo a partir de herramientas de aprendizaje estadístico. Conclusión: hemos desarrollado una herramienta y un modelo estadístico que es fácil de utilizar y que idealmente ayudará al cirujano en el proceso de evaluación preoperatoria de TC de SPN. Este es un ejercicio que permite demostrar la eficacia de un desarrollo colaborativo para lograr soluciones en nuestra especialidad y un acercamiento hacia la IA.

Introduction: The recent technology revolution that we have experienced has generated extensive interest in the use of artificial intelligence (AI) in the development of various systems and solutions in medicine. In the field of Otorhinolaryngology, we are seeing the first efforts to take advantage of this flourishing area. Objective: We sought to describe the development process of a mobile app created through a collaborative effort between ENT surgeons and biomedical engineers. This app has the intention to optimize the preoperative evaluation of paranasal sinus tomography (CT) to improve safety and outcomes in Endoscopic Sinus Surgery (ESS). Methods: The development of the app followed the prioritization method for MoSCoW specifications. We used the information collected from surveys of 29 Rhinology experts from different parts of the world, who evaluated anatomical variants on sinus CT scans. Two regression models were used to predict difficulty and risk using statistical learning. Conclusion: Via statistical modelling, we have developed a user-friendly tool that will ideally help surgeons assess the risk and difficulty of ESS based on the pre-operative CT scan of the sinuses. This is an exercise that demonstrates the efficacy of the collaborative efforts between surgeons and engineers to leverage AI tools and promote better solutions for our patients.

Las minas antipersonales: posibilidades de acción en medio del conflicto / Antipersonal mines: action possibilities between of the conflict

En Colombia existen cerca de 70 mil minas antipersonales (MAP) sembradas y distribuídas en 23 departamentos y que afectan el 10/100 de los municipios. No obstante la situación resulta desconocida para la gran mayoría de los colombiano. Durante el primer semestre de 1998, 100 personas murieron a consecuencia de MAP y 155 resultaron heridas. La existencia de zonas minadas representa para el país la inutilización de vastas zonas y un alto costo en materia de salud pública. Las MAP no diferencian entre combatientes y no combatientes y permanecen activas mucho tiempo despues de finalizados los conflictos. Las víctimas que sobeviven sufren la amputación por lo menos de alguna de sus extremidades como consecuencia de las lesiones