Nocturnal Hypoxemia Associates With Symptom Progression and Mortality in Patients With Progressive Fibrotic Interstitial Lung Disease.

BACKGROUND: OSA and nocturnal hypoxemia (NH) are common in patients with fibrotic interstitial lung disease (F-ILD), but their relationship with disease outcomes remains unclear. RESEARCH QUESTION: What is the relationship between NH and OSA and clinical outcomes in patients with F-ILD? STUDY DESIGN AND METHODS: This was a prospective observational cohort study of patients with F-ILD and without daytime hypoxemia. Patients underwent home sleep study at baseline and were followed up for at least 1 year or until death. NH was defined as ≥ 10% of sleep with oxygen saturation of < 90%. OSA was defined as an apnea-hypopnea index of ≥ 15 events/h. RESULTS: Among 102 participants (male, 74.5%; age, 73.0 ± 8.7 years; FVC, 2.74 ± 0.78 L; 91.1% idiopathic pulmonary fibrosis), 20 patients (19.6%) demonstrated prolonged NH and 32 patients (31.4%) showed OSA. No significant differences were found between those with and without NH or OSA at baseline. Despite this, NH was associated with a more rapid decline in both quality of life as measured by the King's Brief Interstitial Lung Disease questionnaire (change, -11.3 ± 5.3 points in the NH group vs -6.7 ± 6.5 in those without NH; P = .005) and higher all-cause mortality at 1 year (hazard ratio, 8.21; 95% CI, 2.40-28.1; P < .001). No statistically significant difference was seen between the groups in annualized change in measures of pulmonary function testing. INTERPRETATION: Prolonged NH, but not OSA, is associated with worsening disease-related quality of life and increased mortality in patients with F-ILD.

Segmentación de imágenes cerebrales de Resonancia Magnética basada en Redes Neuronales de Regresión Generalizada / Segmentation of Magnetic Resonance images of the brain based on Generalized Regression Neural Networks

El análisis de los cambios estructurales del cerebro a través de imágenes de Resonancia Magnética puede proveer información útil para el diagnóstico y el manejo clínico de los pacientes con demencia. Si bien el grado de sofisticación alcanzado por el equipamiento de Resonancia Magnética es alto, la cuantificación de estructuras y tejidos aún no ha sido completamente solucionada. Las segmentaciones que estos equipos permiten en la actualidad fracasan en aquellas estructuras donde los bordes no están claramente definidos. En este trabajo se presenta un método de segmentación automática de imágenes de Resonancia Magnética cerebrales basada en la utilización de Redes Neuronales de Regresión Generalizada utilizando algoritmos genéticos para el ajuste de los parámetros. La red se entrena a partir de una sola imagen y clasifica al resto de ellas siempre que las imágenes de Resonancia Magnética hayan sido adquiridas con el mismo protocolo. Un método de medición de la atrofia progresiva y sus posibles cambios frente a un efecto terapéutico debe ser fundamentalmente automático y por lo tanto independiente del radiólogo(AU)

The analysis of structural changes in the brain through Magnetic Resonance Images may provide useful information for the diagnosis and clinical management of patients with dementia. While the degree of sophistication achieved by the MRI equipment is high, the quantification of structures and tissues has not been completely solved. The segmentations that these equipment provide nowadays, fail on those structures where the edges are not clearly defined. This paper presents a method for automatic segmentation of magnetic resonance images of the brain, based on the use of generalized regression neural networks using genetic algorithms for adjusting parameters. The network is trained from a single image and classifies rest of them whenever magnetic resonance images were acquired with the same protocol. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist(AU)

Segmentación de imágenes cerebrales de Resonancia Magnética basada en Redes Neuronales de Regresión Generalizada / Segmentation of Magnetic Resonance images of the brain based on Generalized Regression Neural Networks

El análisis de los cambios estructurales del cerebro a través de imágenes de Resonancia Magnética puede proveer información útil para el diagnóstico y el manejo clínico de los pacientes con demencia. Si bien el grado de sofisticación alcanzado por el equipamiento de Resonancia Magnética es alto, la cuantificación de estructuras y tejidos aún no ha sido completamente solucionada. Las segmentaciones que estos equipos permiten en la actualidad fracasan en aquellas estructuras donde los bordes no están claramente definidos. En este trabajo se presenta un método de segmentación automática de imágenes de Resonancia Magnética cerebrales basada en la utilización de Redes Neuronales de Regresión Generalizada utilizando algoritmos genéticos para el ajuste de los parámetros. La red se entrena a partir de una sola imagen y clasifica al resto de ellas siempre que las imágenes de Resonancia Magnética hayan sido adquiridas con el mismo protocolo. Un método de medición de la atrofia progresiva y sus posibles cambios frente a un efecto terapéutico debe ser fundamentalmente automático y por lo tanto independiente del radiólogo(AU)

The analysis of structural changes in the brain through Magnetic Resonance Images may provide useful information for the diagnosis and clinical management of patients with dementia. While the degree of sophistication achieved by the MRI equipment is high, the quantification of structures and tissues has not been completely solved. The segmentations that these equipment provide nowadays, fail on those structures where the edges are not clearly defined. This paper presents a method for automatic segmentation of magnetic resonance images of the brain, based on the use of generalized regression neural networks using genetic algorithms for adjusting parameters. The network is trained from a single image and classifies rest of them whenever magnetic resonance images were acquired with the same protocol. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist(AU)