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.

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.

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.

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.

Assessment of Nocturnal Autonomic Cardiac Imbalance in Positional Obstructive Sleep Apnea. A Multiscale Nonlinear Approach.

Positional obstructive sleep apnea (POSA) is a major phenotype of sleep apnea. Supine-predominant positional patients are frequently characterized by milder symptoms and less comorbidity due to a lower age, body mass index, and overall apnea-hypopnea index. However, the bradycardia-tachycardia pattern during apneic events is known to be more severe in the supine position, which could affect the cardiac regulation of positional patients. This study aims at characterizing nocturnal heart rate modulation in the presence of POSA in order to assess potential differences between positional and non-positional patients. Patients showing clinical symptoms of suffering from a sleep-related breathing disorder performed unsupervised portable polysomnography (PSG) and simultaneous nocturnal pulse oximetry (NPO) at home. Positional patients were identified according to the Amsterdam POSA classification (APOC) criteria. Pulse rate variability (PRV) recordings from the NPO readings were used to assess overnight cardiac modulation. Conventional cardiac indexes in the time and frequency domains were computed. Additionally, multiscale entropy (MSE) was used to investigate the nonlinear dynamics of the PRV recordings in POSA and non-POSA patients. A total of 129 patients (median age 56.0, interquartile range (IQR) 44.8-63.0 years, median body mass index (BMI) 27.7, IQR 26.0-31.3 kg/m2) were classified as POSA (37 APOC I, 77 APOC II, and 15 APOC III), while 104 subjects (median age 57.5, IQR 49.0-67.0 years, median BMI 29.8, IQR 26.6-34.7 kg/m2) comprised the non-POSA group. Overnight PRV recordings from positional patients showed significantly higher disorderliness than non-positional subjects in the smallest biological scales of the MSE profile (τ = 1: 0.25, IQR 0.20-0.31 vs. 0.22, IQR 0.18-0.27, p < 0.01) (τ = 2: 0.41, IQR 0.34-0.48 vs. 0.37, IQR 0.29-0.42, p < 0.01). According to our findings, nocturnal heart rate regulation is severely affected in POSA patients, suggesting increased cardiac imbalance due to predominant positional apneas.

Influence of Chronic Obstructive Pulmonary Disease and Moderate-To-Severe Sleep Apnoea in Overnight Cardiac Autonomic Modulation: Time, Frequency and Non-Linear Analyses.

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent lung diseases worldwide. COPD patients show major dysfunction in cardiac autonomic modulation due to sustained hypoxaemia, which has been significantly related to higher risk of cardiovascular disease. Obstructive sleep apnoea syndrome (OSAS) is a frequent comorbidity in COPD patients. It has been found that patients suffering from both COPD and OSAS simultaneously, the so-called overlap syndrome, have notably higher morbidity and mortality. Heart rate variability (HRV) has demonstrated to be useful to assess changes in autonomic functioning in different clinical conditions. However, there is still little scientific evidence on the magnitude of changes in cardiovascular dynamics elicited by the combined effect of both respiratory diseases, particularly during sleep, when apnoeic events occur. In this regard, we hypothesised that a non-linear analysis is able to provide further insight into long-term dynamics of overnight cardiovascular modulation. Accordingly, this study is aimed at assessing the usefulness of sample entropy (SampEn) to distinguish changes in overnight pulse rate variability (PRV) recordings among three patient groups while sleeping: COPD, moderate-to-severe OSAS, and overlap syndrome. In order to achieve this goal, a population composed of 297 patients were studied: 22 with COPD alone, 213 showing moderate-to-severe OSAS, and 62 with COPD and moderate-to-severe OSAS simultaneously (COPD+OSAS). Cardiovascular dynamics were analysed using pulse rate (PR) recordings from unattended pulse oximetry carried out at patients' home. Conventional time- and frequency- domain analyses were performed to characterise sympathetic and parasympathetic activation of the nervous system, while SampEn was applied to quantify long-term changes in irregularity. Our analyses revealed that overnight PRV recordings from COPD+OSAS patients were significantly more irregular (higher SampEn) than those from patients with COPD alone (0.267 [0.210-0.407] vs. 0.212 [0.151-0.267]; p < 0.05) due to recurrent apnoeic events during the night. Similarly, COPD + OSAS patients also showed significantly higher irregularity in PRV during the night than subjects with OSAS alone (0.267 [0.210-0.407] vs. 0.241 [0.189-0.325]; p = 0.05), which suggests that the cumulative effect of both diseases increases disorganization of pulse rate while sleeping. On the other hand, no statistical significant differences were found between COPD and COPD + OSAS patients when traditional frequency bands (LF and HF) were analysed. We conclude that SampEn is able to properly quantify changes in overnight cardiovascular dynamics of patients with overlap syndrome, which could be useful to assess cardiovascular impairment in COPD patients due to the presence of concomitant OSAS.

Nocturnal Oximetry-based Evaluation of Habitually Snoring Children.

RATIONALE: The vast majority of children around the world undergoing adenotonsillectomy for obstructive sleep apnea-hypopnea syndrome (OSA) are not objectively diagnosed by nocturnal polysomnography because of access availability and cost issues. Automated analysis of nocturnal oximetry (nSpO2), which is readily and globally available, could potentially provide a reliable and convenient diagnostic approach for pediatric OSA. METHODS: Deidentified nSpO2 recordings from a total of 4,191 children originating from 13 pediatric sleep laboratories around the world were prospectively evaluated after developing and validating an automated neural network algorithm using an initial set of single-channel nSpO2 recordings from 589 patients referred for suspected OSA. MEASUREMENTS AND MAIN RESULTS: The automatically estimated apnea-hypopnea index (AHI) showed high agreement with AHI from conventional polysomnography (intraclass correlation coefficient, 0.785) when tested in 3,602 additional subjects. Further assessment on the widely used AHI cutoff points of 1, 5, and 10 events/h revealed an incremental diagnostic ability (75.2, 81.7, and 90.2% accuracy; 0.788, 0.854, and 0.913 area under the receiver operating characteristic curve, respectively). CONCLUSIONS: Neural network-based automated analyses of nSpO2 recordings provide accurate identification of OSA severity among habitually snoring children with a high pretest probability of OSA. Thus, nocturnal oximetry may enable a simple and effective diagnostic alternative to nocturnal polysomnography, leading to more timely interventions and potentially improved outcomes.

EFFECTIVENESS OF VIDEO FEEDBACK AND INTERACTIVE QUESTIONING IN IMPROVING TACTICAL KNOWLEDGE IN VOLLEYBALL.

The objective of the study was to develop and apply a tactical-cognitive training program based on the use of video feedback and questioning in real game time, in order to improve tactical knowledge in volleyball. A two-group quasi-experimental design was used with a sample of eight female players (M=14.8 yr., SD=0.7), who were divided into an Experimental group (n=4) and a Control group (n=4). The independent variable was the tactical-cognitive training program, which was applied for 11 wk. in a 6×6 game situation training context. The dependent variable was tactical knowledge, which was measured by problem representation and strategy planning with a verbal protocol. The results showed that after applying the intervention program the players in the Experimental group showed more complex, sophisticated, and structured tactical knowledge, compared with the players from the Control group. These results suggest that complementing the training process with cognitive tools may enable athletes to increases their tactical behavior and presumably improve their performance.

Implementation of Spanish adaptation of the European guidelines on cardiovascular disease prevention in primary care.

BACKGROUND: The successful implementation of cardiovascular disease (CVD) prevention guidelines relies heavily on primary care physicians (PCPs) providing risk factor evaluation, intervention and patient education. The aim of this study was to ascertain the degree of awareness and implementation of the Spanish adaptation of the European guidelines on CVD prevention in clinical practice (CEIPC guidelines) among PCPs. METHODS: A cross-sectional survey of PCPs was conducted in Spain between January and June 2011. A random sample of 1,390 PCPs was obtained and stratified by region. Data were collected by means of a self-administered questionnaire. RESULTS: More than half (58%) the physicians were aware of and knew the recommendations, and 62% of those claimed to use them in clinical practice, with general physicians (without any specialist accreditation) being less likely to so than family doctors. Most PCPs (60%) did not assess cardiovascular risk, with the limited time available in the surgery being cited as the greatest barrier by 81%. The main reason to be sceptical about recommendations, reported by 71% of physicians, was that there are too many guidelines. Almost half the doctors cited the lack of training and skills as the greatest barrier to the implementation of lifestyle and behavioural change recommendations. CONCLUSIONS: Most PCPs were aware of the Spanish adaptation of the European guidelines on CVD prevention (CEIPC guidelines) and knew their content. However, only one third of PCPs used the guidelines in clinical practice and less than half CVD risk assessment tools.

Analysis of the relationship between the amount of training and cognitive expertise. A study of young volleyball players.

The main goal of this research was to analyze the relationship between the amount of practice accumulated in training and the level of cognitive expertise achieved by volleyball players who are still in training. Another goal was to determine the number of training hours per week needed to improve knowledge significantly. The study's sample was composed of 520 volleyball players between the ages of 12 and 16 years. The independent variable was the amount of training, defined as the number of weekly hours that the volleyball player devoted to training. The dependent variable was cognitive expertise, measured by declarative knowledge and procedural knowledge. A univariate analysis of variance was done to examine the relationship between the number of weekly hours and the declarative and procedural knowledge reached by volleyball players in the athletic formation training stages. Statistical significance was set at p < 0.05. There were significant differences in knowledge according to the number of weekly training hours (p < 0.001). These results confirm that there is a relationship between the quantity of practice and the development of cognitive expertise. It is recommended that young players dedicate at least 4 hours weekly to training to achieve a significant improvement in cognitive expertise.

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.

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.

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.

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.

Tactical knowledge in tennis: a comparison of two groups with different levels of expertise.

Differences in the tactical knowledge of tennis players are described using the expert-novice approach to examine problem representation and strategy planning in 6 pre-professionals and 6 intermediate tennis players, by means of the McPherson and Thomas protocol for analysing verbal reports during game play. Statistical analyses indicated significant differences in conceptual content, structure, and sophistication. These pre-professional tennis players had greater, more elaborated, and sophisticated tactical knowledge; with expertise, more complex structures are developed in long-term memory. Specific training programmes to improve tennis players' tactical knowledge and cognitive skills may be desirable.

Analysis of declarative and procedural knowledge in volleyball according to the level of practice and players' age.

The main objective of the research was to analyse the cognitive expertise of volleyball players, according to their level of practice and age, as well as to verify the existing difference in the knowledge of individuals of the same age but with different levels of practice. The study sample was comprised of 535 individuals ages 12 to 16 years. The independent variables were the level of practice, i.e., playing category in training and in competition (Under-14 and Under-16), and the age. The dependent variables were declarative knowledge and procedural knowledge. An analysis of variance was performed to examine the influence of the level of practice on the declarative knowledge and procedural knowledge of the volleyball players in training stages. There were significant differences both in declarative knowledge and in procedural knowledge according to level of practice. Significant differences were also observed between consecutive ages at different levels of practice. These results show that the level of practice in training and competition is a more relevant factor than the change of age in development of specific knowledge of the sport.

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.

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.