A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea.

Characterization of sleep stages is essential in the diagnosis of sleep-related disorders but relies on manual scoring of overnight polysomnography (PSG) recordings, which is onerous and labor-intensive. Accordingly, we aimed to develop an accurate deep-learning model for sleep staging in children suffering from pediatric obstructive sleep apnea (OSA) using pulse oximetry signals. For this purpose, pulse rate (PR) and blood oxygen saturation (SpO2) from 429 childhood OSA patients were analyzed. A CNN-RNN architecture fed with PR and SpO2 signals was developed to automatically classify wake (W), non-Rapid Eye Movement (NREM), and REM sleep stages. This architecture was composed of: (i) a convolutional neural network (CNN), which learns stage-related features from raw PR and SpO2 data; and (ii) a recurrent neural network (RNN), which models the temporal distribution of the sleep stages. The proposed CNN-RNN model showed a high performance for the automated detection of W/NREM/REM sleep stages (86.0% accuracy and 0.743 Cohen's kappa). Furthermore, the total sleep time estimated for each children using the CNN-RNN model showed high agreement with the manually derived from PSG (intra-class correlation coefficient = 0.747). These results were superior to previous works using CNN-based deep-learning models for automatic sleep staging in pediatric OSA patients from pulse oximetry signals. Therefore, the combination of CNN and RNN allows to obtain additional information from raw PR and SpO2 data related to sleep stages, thus being useful to automatically score sleep stages in pulse oximetry tests for children evaluated for suspected OSA.Clinical Relevance-This research establishes the usefulness of a CNN-RNN architecture to automatically score sleep stages in pulse oximetry tests for pediatric OSA diagnosis.

An explainable deep-learning model to stage sleep states in children and propose novel EEG-related patterns in sleep apnea.

Automatic deep-learning models used for sleep scoring in children with obstructive sleep apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings. Accordingly, we aimed to develop an accurate and interpretable deep-learning model for sleep staging in children using single-channel electroencephalogram (EEG) recordings. We used EEG signals from the Childhood Adenotonsillectomy Trial (CHAT) dataset (n = 1637) and a clinical sleep database (n = 980). Three distinct deep-learning architectures were explored to automatically classify sleep stages from a single-channel EEG data. Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable artificial intelligence (XAI) algorithm, was then applied to provide an interpretation of the singular EEG patterns contributing to each predicted sleep stage. Among the tested architectures, a standard convolutional neural network (CNN) demonstrated the highest performance for automated sleep stage detection in the CHAT test set (accuracy = 86.9% and five-class kappa = 0.827). Furthermore, the CNN-based estimation of total sleep time exhibited strong agreement in the clinical dataset (intra-class correlation coefficient = 0.772). Our XAI approach using Grad-CAM effectively highlighted the EEG features associated with each sleep stage, emphasizing their influence on the CNN's decision-making process in both datasets. Grad-CAM heatmaps also allowed to identify and analyze epochs within a recording with a highly likelihood to be misclassified, revealing mixed features from different sleep stages within these epochs. Finally, Grad-CAM heatmaps unveiled novel features contributing to sleep scoring using a single EEG channel. Consequently, integrating an explainable CNN-based deep-learning model in the clinical environment could enable automatic sleep staging in pediatric sleep apnea tests.

Could the Hybridization of the SE/TGfU Pedagogical Models Be an Alternative for Learning Sports and Promoting Health? School Context Study.

The present study aims to analyze the influence of the Sport Education (SE)/Teaching for understanding (TGfU) hybrid unit on enjoyment, perceived competence, intention to be physically active, skill execution, decision making, performance and game involvement. A short-term (12-lesson) pre-test/post-test quasi-experimental design was conducted in two groups: control (technical approach: 70 students; age = 14.43 ± 0.693; n = 32 female) and experimental (hybrid unit SE-TGfU: 67 students; age = 13.91 ± 0.900; n = 30 female). The coding instrument was based on the Game performance Assessment Instrument. The Enjoyment and Perceived Competence Scale and the Measure of Intentionality to be Physically Active questionnaire were also used. The results of pairwise comparisons between the groups showed higher post-test scores for most dependent variables for boys and girls using the hybrid SE/TGfU unit. Lower post-test scores were found in pairwise comparisons for several dependent variables in both boys and girls. The present study showed that the application of hybrid models SE/TGfU could increase and help facilitate students' game involvement and game performance, enjoyment, perceived competence and intention to be physically active, in both boys and girls. In future studies, it would be necessary to analyze psychological variables in the educational context for a deeper assessment.

Derivation and validation of a blood biomarker score for 2-day mortality prediction from prehospital care: a multicenter, cohort, EMS-based study.

Identifying potentially life-threatening diseases is a key challenge for emergency medical services. This study aims at examining the role of different prehospital biomarkers from point-of-care testing to derive and validate a score to detect 2-day in-hospital mortality. We conducted a prospective, observational, prehospital, ongoing, and derivation-validation study in three Spanish provinces, in adults evacuated by ambulance and admitted to the emergency department. A total of 23 ambulance-based biomarkers were collected from each patient. A biomarker score based on logistic regression was fitted to predict 2-day mortality from an optimum subset of variables from prehospital blood analysis, obtained through an automated feature selection stage. 2806 cases were analyzed, with a median age of 68 (interquartile range 51-81), 42.3% of women, and a 2-day mortality rate of 5.5% (154 non-survivors). The blood biomarker score was constituted by the partial pressure of carbon dioxide, lactate, and creatinine. The score fitted with logistic regression using these biomarkers reached a high performance to predict 2-day mortality, with an AUC of 0.933 (95% CI 0.841-0.973). The following risk levels for 2-day mortality were identified from the score: low risk (score < 1), where only 8.2% of non-survivors were assigned to; medium risk (1 ≤ score < 4); and high risk (score ≥ 4), where the 2-day mortality rate was 57.6%. The novel blood biomarker score provides an excellent association with 2-day in-hospital mortality, as well as real-time feedback on the metabolic-respiratory patient status. Thus, this score can help in the decision-making process at critical moments in life-threatening situations.

Prosumer capitalism in the sharing economy: a gender approach to service providers' experiences in ridesharing platforms.

Introduction: The study draws on the theory of "prosumer capitalism" to explore the experiences of female drivers in ridesharing platforms. Methods: Twenty-five phenomenological in-depth interviews were carried out with Mexican female drivers in ridesharing platforms. Results: The results yielded insights regarding the motives of women to become rideshare drivers, their prosumption experiences, and gender issues related to the job. Discussion: The study offers a novel gender-based approach to comprehend the status of female service providers as prosumer-as-producers and the diverse risks and challenges they face while working in the sharing economy. In a practical sense, platform designers and marketers can improve the application functions to attend to the specific needs of female drivers and implement inclusive measures to safeguard their integrity and well-being.

Perda de Produtividade Atribuída a Neoplasias na América do Sul / Lost Productivity Attributed to Neoplasms in South America / Pérdida de Productividad Atribuida a Neoplasias en América del Sur

Introdução: A carga da doença tem sido empregada em estimativas do impacto das neoplasias, mas a perda de produtividade em razão dessas enfermidades ainda não foi tão explorada. Objetivo: Estimar os anos de vida produtiva perdidos (AVPP) e a perda de produtividade por conta da mortalidade prematura relacionada ao câncer em países da América do Sul em 2019. Método: Dados de mortalidade disponíveis no Global Burden of Disease (GBD) Study 2019 foram usados para estimar a carga de doença atribuível a neoplasias. A perda de produtividade em termos monetários foi calculada usando um proxy da abordagem do capital humano (ACH). Os cálculos foram realizados por sexo, nas faixas etárias de trabalho. Resultados: O total de óbitos foi de 192.240 e o de AVPP, 2.463.155. A perda total de produtividade permanente foi de US$ 4,4 bilhões e US$ 9,4 bilhões em purchasing power parity (PPP) ­ 0,13% do produto interno bruto (PIB) da região. O custo total por morte foi de US$ 23.617. Houve diferenças significativas entre os países, mas a variação dos cenários mostra robustez das estimativas. Conclusão: O câncer impõe um ônus econômico significativo à América do Sul tanto em termos de saúde quanto de produtividade. Sua caracterização pode subsidiar os governos na alocação de recursos destinados ao planejamento de políticas e execução de intervenções de saúde

Introduction: The burden of the disease has been utilized in estimates of the impact of neoplasms, but the loss of productivity due to these diseases has not yet been explored. Objective: To estimate the years of productivity life lost (YPLL) and lost productivity due to premature cancer-related mortality in South American countries in 2019. Method: Mortality data available from Global Burden of Disease (GBD) Study 2019 was analyzed to estimate the burden attributable to neoplasms. The productivity loss in monetary terms was estimated using a proxy of the human capital approach (HCA). Calculations were performed by sex, in working age groups. Results: The total deaths and YPLL reached 192,240 and 2.463.155, respectively. The total permanent productivity loss was around US$ 4.4 billion and US$ 9.4 billion in purchasing power parity (PPP) ­ 0.13% of the continent's gross domestic product (GDP). Total cost per death was US$23,617. There were significant differences among countries, but the variation of scenarios shows robustness of the estimates. Conclusion: Cancer imposes a significant economic burden on South American, both in terms of health and productivity. Its characterization can help governments to allocate resources for policies planning and health interventions.

Introducción: Se ha utilizado la carga de enfermedad en las estimaciones del impacto de las neoplasias, pero aún no se ha explorado la pérdida de productividad por estas enfermedades. Objetivo: Estimar los años de vida productiva perdidos (AVPP) y la pérdida de productividad debido a la mortalidad prematura relacionada con el cáncer en los países de la América del Sur en 2019. Método: Datos de mortalidad disponibles del Global Burden of Disease (GBD) Study 2019 fueron utilizado para estimar la carga de enfermedad atribuible a las neoplasias. La pérdida de productividad en términos monetarios se calculó utilizando un proxy de enfoque de capital humano (ACH). Los cálculos se realizaron por sexo, en los grupos de edad laboral. Resultados: El número total de muertes fue de 192.240 y de AVPP, 2.463.155. La pérdida total de productividad permanente fue del orden de US$ 4.400 millones y US$ 9.400 millones en purchasing power parity (PPP) ­ 0,13% del producto interior bruto (PIB) de la región. El costo total por muerte fue de $23,617. Hubo diferencias significativas entre países, pero la variación de escenarios muestra la robustez de las estimaciones. Conclusión: El cáncer impone una carga económica significativa a América del Sur, tanto en términos de salud como de productividad. Su caracterización puede apoyar a los gobiernos en la asignación de recursos para la planificación de políticas y ejecución de intervenciones en salud.

Burden of disease attributed to acute respiratory infections in South America.

INTRODUCTION: Respiratory diseases (RD) are an important public health problem. Their burden has not been comprehensively evaluated in South America (SA). This study describes the burden of acute respiratory infections (ARIs) in SA in 2019. METHODOLOGY: This is an exploratory, population-based study with a quantitative approach to incidence, mortality, and Disability-adjusted life years (DALYs) by standardized age group among the 12 countries. Measurements were captured through the Institute for Health Metrics and Evaluation (IHME) website. It used the Burden Study Global Disease, Injury and Risk Factors (GBD) 2019 assessment. Correlation analyses were performed. RESULTS: The age-standardized incidence rate per 1,00,000 people for lower respiratory infections (LRIs) is lowest in Chile (3,902) and highest in Peru (9,997). For upper respiratory infections (URIs), Bolivia (2,25,826) had the lowest rates, while Brazil (3,16,667) and Colombia (3,06,302) had the highest. Standardized mortality rates for LRI were lowest in Colombia (15.10) and highest in Bolivia (80.53). Bolivia had the highest standardized DALY rate (2,083), while Uruguay had the lowest (468). Upper ARI had lower incidence rates than lower ARI. The lowest DALY rates were in Suriname (82) and the highest were in Brazil (111). There is a correlation between sociodemographic and economic health indicators and the standardized rates of incidence and DALY in the upper ARIs. CONCLUSIONS: The present paper provides comprehensive ARI burden estimates for the region. The substantial incidence and considerable mortality and DALYs are noteworthy and lead to reflections on preventive measures such as rational use of antibiotics and deeper epidemiological investigations.

Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea.

The overnight polysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learning methods have been already evaluated for this purpose. In this chapter, we show the main approaches found in the scientific literature along with the most used data to develop the models, useful and large easily available databases, and suitable methods to assess performances. In addition, a range of results from selected studies are presented as examples of these methods. Very high diagnostic performances are reported in these results regardless of the approaches taken. This leads us to conclude that conventional machine learning methods are useful techniques to develop new OSA diagnosis simplification proposals and to act as benchmark for other more recent methods such as deep learning.

ECG and Heart Rate Variability in Sleep-Related Breathing Disorders.

Here we discuss the current perspectives of comprehensive heart rate variability (HRV) analysis in electrocardiogram (ECG) signals as a non-invasive and reliable measure to assess autonomic function in sleep-related breathing disorders (SDB). It is a tool of increasing interest as different facets of HRV can be implemented to screen and diagnose SDB, monitor treatment efficacy, and prognose adverse cardiovascular outcomes in patients with sleep apnea. In this context, the technical aspects, pathophysiological features, and clinical applications of HRV are discussed to explore its usefulness in better understanding SDB.

Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures.

Obstructive sleep apnea (OSA) is a multidimensional disease often underdiagnosed due to the complexity and unavailability of its standard diagnostic method: the polysomnography. Among the alternative abbreviated tests searching for a compromise between simplicity and accurateness, oximetry is probably the most popular. The blood oxygen saturation (SpO2) signal is characterized by a near-constant profile in healthy subjects breathing normally, while marked drops (desaturations) are linked to respiratory events. Parameterization of the desaturations has led to a great number of indices of severity assessment commonly used to assist in OSA diagnosis. In this chapter, the main methodologies used to characterize the overnight oximetry profile are reviewed, from visual inspection and simple statistics to complex measures involving signal processing and pattern recognition techniques. We focus on the individual performance of each approach, but also on the complementarity among the great amount of indices existing in the state of the art, looking for the most relevant oximetric feature subset. Finally, a quick overview of SpO2-based deep learning applications for OSA management is carried out, where the raw oximetry signal is analyzed without previous parameterization. Our research allows us to conclude that all the methodologies (conventional, time, frequency, nonlinear, and hypoxemia-based) demonstrate high ability to provide relevant oximetric indices, but only a reduced set provide non-redundant complementary information leading to a significant performance increase. Finally, although oximetry is a robust tool, greater standardization and prospective validation of the measures derived from complex signal processing techniques are still needed to homogenize interpretation and increase generalizability.

Deep-Learning Model Based on Convolutional Neural Networks to Classify Apnea-Hypopnea Events from the Oximetry Signal.

Automated analysis of the blood oxygen saturation (SpO2) signal from nocturnal oximetry has shown usefulness to simplify the diagnosis of obstructive sleep apnea (OSA), including the detection of respiratory events. However, the few preceding studies using SpO2 recordings have focused on the automated detection of respiratory events versus normal respiration, without making any distinction between apneas and hypopneas. In this sense, the characteristics of oxygen desaturations differ between obstructive apnea and hypopnea episodes. In this chapter, we use the SpO2 signal along with a convolutional neural network (CNN)-based deep-learning architecture for the automatic identification of apnea and hypopnea events. A total of 398 SpO2 signals from adult OSA patients were used for this purpose. A CNN architecture was trained using 30-s epochs from the SpO2 signal for the automatic classification of three classes: normal respiration, apnea, and hypopnea. Then, the apnea index (AI), the hypopnea index (HI), and the apnea-hypopnea index (AHI) were obtained by aggregating the outputs of the CNN for each subject (AICNN, HICNN, and AHICNN). This model showed a promising diagnostic performance in an independent test set, with 80.3% 3-class accuracy and 0.539 3-class Cohen's kappa for the classification of respiratory events. Furthermore, AICNN, HICNN, and AHICNN showed a high agreement with the values obtained from the standard PSG: 0.8023, 0.6774, and 0.8466 intra-class correlation coefficients (ICCs), respectively. This suggests that CNN can be used to analyze SpO2 recordings for the automated diagnosis of OSA in at-home oximetry tests.

A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry.

The gold standard approach to diagnose obstructive sleep apnea (OSA) in children is overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous to healthcare systems and families. Simplification of PSG should enhance availability and comfort, and reduce complexity and waitlists. Airflow (AF) and oximetry (SpO2) signals summarize most of the information needed to detect apneas and hypopneas, but automatic analysis of these signals using deep-learning algorithms has not been extensively investigated in the pediatric context. The aim of this study was to evaluate a convolutional neural network (CNN) architecture based on these two signals to estimate the severity of pediatric OSA. PSG-derived AF and SpO2 signals from the Childhood Adenotonsillectomy Trial (CHAT) database (1638 recordings), as well as from a clinical database (974 recordings), were analyzed. A 2D CNN fed with AF and SpO2 signals was implemented to estimate the number of apneic events, and the total apnea-hypopnea index (AHI) was estimated. A training-validation-test strategy was used to train the CNN, adjust the hyperparameters, and assess the diagnostic ability of the algorithm, respectively. Classification into four OSA severity levels (no OSA, mild, moderate, or severe) reached 4-class accuracy and Cohen's Kappa of 72.55% and 0.6011 in the CHAT test set, and 61.79% and 0.4469 in the clinical dataset, respectively. Binary classification accuracy using AHI cutoffs 1, 5 and 10 events/h ranged between 84.64% and 94.44% in CHAT, and 84.10%-90.26% in the clinical database. The proposed CNN-based architecture achieved high diagnostic ability in two independent databases, outperforming previous approaches that employed SpO2 signals alone, or other classical feature-engineering approaches. Therefore, analysis of AF and SpO2 signals using deep learning can be useful to deploy reliable computer-aided diagnostic tools for childhood OSA.

Heart rate variability as a potential biomarker of pediatric obstructive sleep apnea resolution.

STUDY OBJECTIVES: Pediatric obstructive sleep apnea (OSA) affects cardiac autonomic regulation, altering heart rate variability (HRV). Although changes in classical HRV parameters occur after OSA treatment, they have not been evaluated as reporters of OSA resolution. Specific frequency bands (named BW1, BW2, and BWRes) have been recently identified in OSA. We hypothesized that changes with treatment in these spectral bands can reliably identify changes in OSA severity and reflect OSA resolution. METHODS: Four hundred and four OSA children (5-9.9 years) from the prospective Childhood Adenotonsillectomy Trial were included; 206 underwent early adenotonsillectomy (eAT), while 198 underwent watchful waiting with supportive care (WWSC). HRV changes from baseline to follow-up were computed for classical and OSA-related frequency bands. Causal mediation analysis was conducted to evaluate how treatment influences HRV through mediators such as OSA resolution and changes in disease severity. Disease resolution was initially assessed by considering only obstructive events, and was followed by adding central apneas to the analyses. RESULTS: Treatment, regardless of eAT or WWSC, affects HRV activity, mainly in the specific frequency band BW2 (0.028-0.074 Hz). Furthermore, only changes in BW2 were specifically attributable to all OSA resolution mediators. HRV activity in BW2 also showed statistically significant differences between resolved and non-resolved OSA. CONCLUSIONS: OSA treatment affects HRV activity in terms of change in severity and disease resolution, especially in OSA-related BW2 frequency band. This band allowed to differentiate HRV activity between children with and without resolution, so we propose BW2 as potential biomarker of pediatric OSA resolution. CLINICAL TRIAL REGISTRATION: Childhood Adenotonsillectomy Trial, NCT00560859, https://sleepdata.org/datasets/chat.

Automatic Sleep Staging in Children with Sleep Apnea using Photoplethysmography and Convolutional Neural Networks.

Sleep staging is of paramount importance in children with suspicion of pediatric obstructive sleep apnea (OSA). Complexity, cost, and intrusiveness of overnight polysomnography (PSG), the gold standard, have led to the search for alternative tests. In this sense, the photoplethysmography signal (PPG) carries useful information about the autonomous nervous activity associated to sleep stages and can be easily acquired in pediatric sleep apnea home tests with a pulse oximeter. In this study, we use the PPG signal along with convolutional neural networks (CNN), a deep-learning technique, for the automatic identification of the three main levels of sleep: wake (W), rapid eye movement (REM), and non-REM sleep. A database of 366 PPG recordings from pediatric OSA patients is involved in the study. A CNN architecture was trained using 30-s epochs from the PPG signal for three-stage sleep classification. This model showed a promising diagnostic performance in an independent test set, with 78.2% accuracy and 0.57 Cohen's kappa for W/NREM/REM classification. Furthermore, the percentage of time in wake stage obtained for each subject showed no statistically significant differences with the manually scored from PSG. These results were superior to the only state-of-the-art study focused on the analysis of the PPG signal in the automated detection of sleep stages in children suffering from OSA. This suggests that CNN can be used along with PPG recordings for sleep stages scoring in pediatric home sleep apnea tests.

Health-Economic Analyses of Diagnostics: Guidance on Design and Reporting.

Cost-effectiveness analyses (CEAs) can be used to assess the value of diagnostics in clinical practice. Due to the introduction of the European in vitro diagnostic and medical devices regulations, more clinical data on new diagnostics may become available, which may improve the interest and feasibility of performing CEAs. We present eight recommendations on the reporting and design of CEAs of diagnostics. The symptoms patients experience, the clinical setting, locations of test sampling and analysis, and diagnostic algorithms should be clearly reported. The used time horizon should reflect the time horizon used to model the treatment after the diagnostic pathway. Quality-adjusted life-years (QALYs) or disability-adjusted life-years (DALYs) should be used as the clinical outcomes but may be combined with other relevant outcomes, such as real options value. If the number of tests using the same equipment can vary, the economy of scale should be considered. An understandable graphical representation of the various diagnostic algorithms should be provided to understand the results, such as an efficiency frontier. Finally, the budget impact and affordability should be considered. These recommendations can be used in addition to other, more general, recommendations, such as the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) or the reference case for economic evaluation by the international decision support initiative.

The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.

Economic Analyses of Respiratory Tract Infection Diagnostics: A Systematic Review.

BACKGROUND: Diagnostic testing for respiratory tract infections is a tool to manage the current COVID-19 pandemic, as well as the rising incidence of antimicrobial resistance. At the same time, new European regulations for market entry of in vitro diagnostics, in the form of the in vitro diagnostic regulation, may lead to more clinical evidence supporting health-economic analyses. OBJECTIVE: The objective of this systematic review was to review the methods used in economic evaluations of applied diagnostic techniques, for all patients seeking care for infectious diseases of the respiratory tract (such as pneumonia, pulmonary tuberculosis, influenza, sinusitis, pharyngitis, sore throats and general respiratory tract infections). METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, articles from three large databases of scientific literature were included (Scopus, Web of Science and PubMed) for the period January 2000 to May 2020. RESULTS: A total of 70 economic analyses are included, most of which use decision tree modelling for diagnostic testing for respiratory tract infections in the community-care setting. Many studies do not incorporate a generally comparable clinical outcome in their cost-effectiveness analysis: fewer than half the studies (33/70) used generalisable outcomes such as quality-adjusted life-years. Other papers consider outcomes related to the accuracy of the test or outcomes related to the prescribed treatment. The time horizons of the studies generally are limited. CONCLUSIONS: The methods to economically assess diagnostic tests for respiratory tract infections vary and would benefit from clear recommendations from policy makers on the assessed time horizon and outcomes used.

How TGfU Influence on Students' Motivational Outcomes in Physical Education? A Study in Elementary School Context.

The purpose of this study was to implement a comprehensive teaching program based on the principles of Teaching Games for Understanding (TGfU) model and questioning, and to assess its consequences for students' satisfaction of basic psychological needs, motivation, perceptions of ability and intention to be physically active during Physical Education lessons in primary education. A quasi-experimental design was utilized. Participants were 111 students from two different groups of fifth and sixth graders, all enrolled in one primary school. Participants were divided into experimental and control group. Experimental group experienced a TGfU unit, according to small side games and the questioning. Control group experienced a small side games unit, without questioning. Within-group results showed that experimental group students reported significantly higher mean scores in all dependents variables of the study, in both genders. Results showed that control group only reported significantly higher mean scores in intention to be physically active variable, also in both genders. The results demonstrate the need to implement didactic units under comprehensive pedagogical approaches to improve motivation and the intention to develop healthy lifestyle habits in female and male students. More researches are needed to support this evidence.

Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children.

This study focused on the automatic analysis of the airflow signal (AF) to aid in the diagnosis of pediatric obstructive sleep apnea (OSA). Thus, our aims were: (i) to characterize the overnight AF characteristics using discrete wavelet transform (DWT) approach, (ii) to evaluate its diagnostic utility, and (iii) to assess its complementarity with the 3% oxygen desaturation index (ODI3). In order to reach these goals, we analyzed 946 overnight pediatric AF recordings in three stages: (i) DWT-derived feature extraction, (ii) feature selection, and (iii) pattern recognition. AF recordings from OSA patients showed both lower detail coefficients and decreased activity associated with the normal breathing band. Wavelet analysis also revealed that OSA disturbed the frequency and energy distribution of the AF signal, increasing its irregularity. Moreover, the information obtained from the wavelet analysis was complementary to ODI3. In this regard, the combination of both wavelet information and ODI3 achieved high diagnostic accuracy using the common OSA-positive cutoffs: 77.97%, 81.91%, and 90.99% (AdaBoost.M2), and 81.96%, 82.14%, and 90.69% (Bayesian multi-layer perceptron) for 1, 5, and 10 apneic events/hour, respectively. Hence, these findings suggest that DWT properly characterizes OSA-related severity as embedded in nocturnal AF, and could simplify the diagnosis of pediatric OSA.

Bispectral analysis of overnight airflow to improve the pediatric sleep apnea diagnosis.

Pediatric Obstructive Sleep Apnea (OSA) is a respiratory disease whose diagnosis is performed through overnight polysomnography (PSG). Since it is a complex, time-consuming, expensive, and labor-intensive test, simpler alternatives are being intensively sought. In this study, bispectral analysis of overnight airflow (AF) signal is proposed as a potential approach to replace PSG when indicated. Thus, our objective was to characterize AF through bispectrum, and assess its performance to diagnose pediatric OSA. This characterization was conducted using 13 bispectral features from 946 AF signals. The oxygen desaturation index ≥3% (ODI3), a common clinical measure of OSA severity, was also obtained to evaluate its complementarity to the AF bispectral analysis. The fast correlation-based filter (FCBF) and a multi-layer perceptron (MLP) were used for subsequent automatic feature selection and pattern recognition stages. FCBF selected 3 bispectral features and ODI3, which were used to train a MLP model with ability to estimate apnea-hypopnea index (AHI). The model reached 82.16%, 82.49%, and 90.15% accuracies for the common AHI cut-offs 1, 5, and 10 events/h, respectively. The different bispectral approaches used to characterize AF in children provided complementary information. Accordingly, bispectral analysis showed that the occurrence of apneic events decreases the non-gaussianity and non-linear interaction of the AF harmonic components, as well as the regularity of the respiratory patterns. Moreover, the bispectral information from AF also showed complementarity with ODI3. Our findings suggest that AF bispectral analysis may serve as a useful tool to simplify the diagnosis of pediatric OSA, particularly for children with moderate-to-severe OSA.