Erratum: Martín-Montero et al. Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea. Entropy 2021, 23, 1016.

The author wishes to make the following correction to this paper [...].

Association between obstructive sleep apnea and cancer incidence in a large multicenter Spanish cohort.

RATIONALE: Obstructive sleep apnea (OSA) has been associated with increased cancer mortality, but whether it is also associated with cancer incidence is unknown. OBJECTIVES: To investigate whether OSA is associated with increased cancer incidence in a large clinical cohort. METHODS: A multicenter, clinical cohort study including consecutive patients investigated for suspected OSA between 2003 and 2007 in seven Spanish teaching hospitals. Apnea-hypopnea index (AHI) and percent nighttime with oxygen saturation less than 90% (TSat(90)) were used as surrogates of OSA severity, both as continuous variables and categorized by tertiles. Cox proportional hazards regression analyses were used to calculate hazard ratio (HR) and 95% confidence interval (CI) for cancer incidence after adjusting for confounding variables. MEASUREMENTS AND MAIN RESULTS: A total of 4,910 patients were analyzed (median follow-up, 4.5 yr; interquartile range, 3.4-5.2). Compared with the lower TSat(90) category (<1.2%), the adjusted hazards (95% CI) of cancer incidence for increasing categories were 1.58 (1.07-2.34) for TSat(90) 1.2-12% and 2.33 (1.57-3.46) for TSat(90) greater than 12%. Continuous TSat(90) was also associated with cancer incidence (adjusted HR, 1.07 [1.02-1.13] per 10-unit increase in TSat(90)). In stratified analyses, TSat(90) was associated with cancer incidence in patients younger than 65 years (adjusted HR, 1.13 [95% CI, 1.06-1.21] per 10-unit increase in TSat(90)) and males (adjusted HR, 1.11 [95% CI, 1.04-1.17] per 10-unit increase in TSat(90)). AHI was not associated with cancer incidence in the adjusted analyses, except for patients younger than 65 years (adjusted HR for AHI >43 vs. <18.7, 1.66; 95% CI, 1.04-2.64). CONCLUSIONS: Increased overnight hypoxia as a surrogate of OSA severity was associated with increased cancer incidence. This association seems to be limited to men and patients younger than 65 years of age.

A Convolutional Neural Network Architecture to Enhance Oximetry Ability to Diagnose Pediatric Obstructive Sleep Apnea.

This study aims at assessing the usefulness of deep learning to enhance the diagnostic ability of oximetry in the context of automated detection of pediatric obstructive sleep apnea (OSA). A total of 3196 blood oxygen saturation (SpO2) signals from children were used for this purpose. A convolutional neural network (CNN) architecture was trained using 20-min SpO2 segments from the training set (859 subjects) to estimate the number of apneic events. CNN hyperparameters were tuned using Bayesian optimization in the validation set (1402 subjects). This model was applied to three test sets composed of 312, 392, and 231 subjects from three independent databases, in which the apnea-hypopnea index (AHI) estimated for each subject (AHICNN) was obtained by aggregating the output of the CNN for each 20-min SpO2 segment. AHICNN outperformed the 3% oxygen desaturation index (ODI3), a clinical approach, as well as the AHI estimated by a conventional feature-engineering approach based on multi-layer perceptron (AHIMLP). Specifically, AHICNN reached higher four-class Cohen's kappa in the three test databases than ODI3 (0.515 vs 0.417, 0.422 vs 0.372, and 0.423 vs 0.369) and AHIMLP (0.515 vs 0.377, 0.422 vs 0.381, and 0.423 vs 0.306). In addition, our proposal outperformed state-of-the-art studies, particularly for the AHI severity cutoffs of 5 e/h and 10 e/h. This suggests that the information automatically learned from the SpO2 signal by deep-learning techniques helps to enhance the diagnostic ability of oximetry in the context of pediatric OSA.

Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry.

Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children's health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagnosis (overnight polysomnography) has several limitations, which has led to the search for alternative tests. In this sense, automated analysis of overnight oximetry has emerged as a simplified technique. Previous studies have focused on the extraction of ad-hoc features from the blood oxygen saturation (SpO2) signal, which may miss useful information related to apnea and hypopnea (AH) events. In order to overcome this limitation of traditional approaches, we propose the use of convolutional neural networks (CNN), a deep learning technique, to automatically detect AH events from the SpO2 raw data. CHAT-baseline dataset, composed of 453 SpO2 recordings, was used for this purpose. A CNN model was trained using 60-s segments from the SpO2 signal using a training set (50% of subjects). Optimum hyperparameters of the CNN architecture were obtained using a validation set (25% of subjects). This model was applied to a third test set (25% of subjects), reaching 93.6% accuracy to detect AH events. These results suggest that the application of CNN may be useful to detect changes produced in the oximetry signal by AH events in pediatric SAHS patients.

Bispectral Analysis to Enhance Oximetry as a Simplified Alternative for Pediatric Sleep Apnea Diagnosis.

This study aims at assessing the bispectral analysis of blood oxygen saturation (SpO2) from nocturnal oximetry to help in pediatric sleep apnea-hypopnea syndrome (SAHS) diagnosis. Recent studies have found excessive redundancy in the SAHS-related information usually extracted from SpO2, while proposing only two features as a reduced set to be used. On the other hand, it has been suggested that SpO2 bispectral analysis is able to provide complementary information to common anthropometric, spectral, and clinical variables. We address these novel findings to assess whether bispectrum provides new non-redundant information to help in SAHS diagnosis. Thus, we use 981 pediatric SpO2 recordings to extract both the reduced set of features recently proposed as well as 9 bispectral features. Then, a feature selection method based on the fast correlationbased filter and bootstrapping is used to assess redundancy among all the features. Finally, the non-redundant ones are used to train a Bayesian multi-layer perceptron neural network (BYMLP) that estimate the apnea-hypopnea index (AHI), which is the diagnostic reference variable. Bispectral phase entropy was found complementary to the two previously recommended features and a BY-MLP model trained with the three of them reached high agreement with actual AHI (intra-class correlation coefficient = 0.889). Estimated AHI also showed high diagnostic ability, reaching 82.1%, 81.9%, and 90.3% accuracies and 0.814, 0.880, and 0.922 area under the receiver-operating characteristics curve for three common AHI thresholds: 1 e/h, 5 e/h, and 10 e/h, respectively. These results suggest that the information extracted from the bispectrum of SpO2 can improve the diagnostic performance of the oximetry test.

Estudio de la adherencia al tratamiento con presión continua positiva en la vía aérea en pacientes con síndrome de apnea obstructiva del sueño en el confinamiento impuesto durante la pandemia de COVID-19 / Study of the Adherence to continuous positive airway pressure Treatment in Patients with Obstructive Sleep Apnea Syndrome in the Confinement During the COVID-19 Pandemic

No disponible

Influencia del dióxido de nitrógeno ambientalen las consultas a urgencias por asma en un entorno con baja contaminación: análisis de series temporales y casos cruzados / Impact of atmospheric nitrogen dioxide on emergency department visits for asthma in a low air pollution area: a time series and case-crossover study

Objetivo: Analizar la asociación entre los niveles ambientales de dióxido de nitrógeno (NO2) y el número de consultas a urgencias por un episodio de agudización de asma bronquial en la población adulta de un entorno urbano con bajos niveles de contaminación. Método: Estudio ecológico retrospectivo de series temporales. Se consideraron las visitas por asma de pacientes mayores de 14 años que acudieron a un servicio de urgencias de forma consecutiva entre 2010 y 2018 (3.287 días). La asociación entre la concentración media de NO2 y el número diario de visitas a urgencias por asma se estudió mediante un modelo lineal generalizado con regresión de Poisson. Se evaluó el impacto en el riesgo individual mediante un análisis de casos cruzados. Se ajustó por las variables confusoras meteorológicas, se corrigió la estacionalidad mediante análisis de tendencias y se evaluaron tres lags temporales (0, 1 y 3 días). Resultados: Se analizaron 2.527 urgencias por asma correspondientes a 1.588 pacientes (edad media 51 ± 21 años, 70% mujeres). Hubo una asociación positiva significativa (riesgo relativo: RR = 1,056, IC 95%: 1,006-1,108; p < 0,05) entre la concentración de NO2 y un mayor riesgo de consulta a urgencias por asma a los 3 días. Un incremento de 10 μgr/m3 de NO2 explicó el 5,3% de las consultas (fracción atribuible: FA = 5,30, IC 95%: 0,60-9,75; p < 0,05). Conclusiones: El incremento de los niveles ambientales de NO2 se asocia con un mayor número de urgencias hospitalarias por exacerbación de asma en adultos en un entorno con baja contaminación. (AU)

Objectives: To analyze the association between atmospheric levels of nitrogen dioxide (NO2) and the number of visits by adults to an emergency department (ED) for exacerbated asthma in an urban area with low levels of air pollution. Material and methods: Retrospective ecological time-series study. We quantified ED visits for asthma by consecutive patients over the age of 14 years between 2010 and 2018 (3287 days). The association between the mean atmospheric concentration of NO2 and the number of daily visits to the ED for asthma was analyzed with generalized linear regression analysis (Poisson modeling). The impact of exposure on individual risk was assessed by crossover analysis of case periods. We adjusted for confounding meteorologic variables, potential variability due to seasonal changes was corrected by trend analysis, and 3 time lags were assessed (0, 1, and 3 days). Results: We analyzed 2527 asthma emergencies in 1588 patients (70% female) with a mean (SD) age of 51 (21) years. A significant positive association (relative risk, 1.056, 95% CI, 1.006-1.108; P .05) between atmospheric NO2 concentration and greater risk of visiting an ED within 3 days was detected. An increase of 10 µg/m3 of NO2 accounted for 5.3% of the visits (attributable fraction, 5.30, 95% CI, 0.60-9.75; P .05). Conclusion: In an urban area with low pollution levels, an elevation in atmospheric NO2 is associated with more hospital ED visits for asthma attacks in adults. (AU)

Aplicaciones clínicas de las redes neuronales en el síndrome de apnea hipopnea del sueño / No disponible

Existe en la actualidad un gran interés en la utilización de procedimientos basados en la inteligencia artificial y en el análisis de las señales biomédicas como métodos de ayuda en la toma de decisiones clínicas, fundamentalmente en el área diagnóstica y terapéutica. Dentro de estos sistemas expertos, se incluye la utilización de regresión logística, árboles genealógicos, análisis discriminante y las redes neuronales. Las redes neuronales presentan grandes posibilidades de aplicación en el campo de la predicción. Su limitación principal es su complejidad en el momento de su diseño. En el campo de los trastornos respiratorios del sueño han sido ampliamente utilizadas tanto en el diagnóstico como en en el reconocimiento de señales, y destaca su gran aporte a los importantes avances tecnológicos acaecidos en el diseño de las CPAP (AU)

There is currently a great interest in the use of procedures based on artificial intelligence and in biomedical signal processing as helpful methods for clinical choice making, mainly in the diagnosis and therapeutical field. Some of these expert systems are logistic regression, family trees, linear discriminant analysis and neural networks. Neural networks show great application options in the prediction field. Their main limitation is their complexity when being designed. They have been widely used in the sleep respiratory disorders field, both for diagnosis and for signal recognition, and they greatly contribute to key technological progress achieved in CPAP design (AU)

Espirometría / Spirometry

La espirometría es la principal prueba de función pulmonar, y resulta imprescindible para la evaluación y el seguimiento de las enfermedades respiratorias. Su utilidad trasciende el ámbito de la neumología, adquiere una creciente importancia en atención primaria e incluso se han descrito aplicaciones fuera del campo de las enfermedades respiratorias. Por ello, este documento pretende servir de apoyo a todos los profesionales de la salud que utilicen la espirometría, proporcionando recomendaciones basadas en las mejores evidencias científicas disponibles. Se propone una actualización de las indicaciones y contraindicaciones de la prueba. El documento establece recomendaciones sobre los requerimientos necesarios para los espirómetros convencionales y los equipos portátiles de oficina, así como sobre las medidas de higiene y de control de calidad de los espirómetros. Se definen los parámetros espirométricos que deben ser considerados, la realización de las maniobras, los criterios de aceptabilidad y repetibilidad de las medidas y su control de calidad. También se establece una propuesta para la presentación de los resultados y se recomienda una evaluación e interpretación acorde a la información generada en los últimos años. Por último, se consideran las líneas de adaptación e integración de la espirometría en el campo de las nuevas tecnologías (AU)

Spirometry is the main pulmonary function test and is essential for the evaluation and monitoring of respiratory diseases. Its utility transcends the field of Respiratory Medicine, is becoming increasingly important in primary care and applications have even been described outside the field of respiratory diseases. This document is therefore intended to serve as support for all health professionals who use spirometry, providing recommendations based on the best scientific evidence available. An update of the indications and contraindications of the test is proposed. The document sets out recommendations on the requirements necessary for conventional spirometers and portable office equipment, as well as on spirometer hygiene and quality control measures. Spirometric parameters that must be considered, performance of manoeuvres, criteria for acceptability and repeatability of measurements and their quality control are defined. A proposal is also established for presentation of the results and an evaluation and interpretation is proposed according to information generated in recent years. Finally, lines of adaptation and integration of spirometry in the field of new technologies are considered (AU)