RESUMO
INTRODUCTION AND OBJECTIVES: Advanced chronic kidney disease (A-CKD) combined with atrial fibrillation increases the risk of both thrombogenic and bleeding events. Left atrial appendage occlusion (LAAO) may be an alternative to oral anticoagulation to prevent thromboembolic events. We aimed to evaluate the outcomes of LAAO in patients with A-CKD. METHODS: Comparison at long-term follow-up of patients diagnosed with and without A-CKD (eGFR<30 mL/min/1.73 m2 ) who underwent LAAO between 2009 and May 2022. RESULTS: Five hundred seventy-three patients were included. Eighty-one (14%) were diagnosed with A-CKD. There were no differences in sex, age, and cardiovascular risk factors, except for diabetes which was more frequent in patients with A-CKD. The control group had higher rates of stroke, both ischemic and hemorrhagic. There were no differences in the CHA2 DS2 -VASc score, although A-CKD patients had a higher bleeding risk according to the HASBLED scale. Global procedural success was 99.1%. At follow-up, there were no differences in stroke rate: at 1-year (HR: 1.22, IC-95%: 0.14-10.42, p = 0.861); at 5-years (HR: 0.60, IC-95%: 0.08-4.58, p = 0.594). Although bleeding events were higher in the A-CKD group, no differences were found in major bleeding (defined BARC ≥ 3) at 1-year (HR: 1.34, IC-95%: 0.63-2.88, p = 0.464) or at 5-years follow-up (HR: 1.30, IC-95%: 0.69-2.48, p = 0.434). Mortality rate at 5 years was higher in the A-CKD patients (HR: 1.84, IC-95%: 1.18-2.87, p = 0.012). CONCLUSIONS: LAAO is an effective and safe treatment in A-CKD patients to prevent ischemic events and bleeding. This strategy could be an alternative to oral anticoagulation in this high-risk group of patients.
Assuntos
Apêndice Atrial , Fibrilação Atrial , Insuficiência Renal Crônica , Acidente Vascular Cerebral , Humanos , Seguimentos , Apêndice Atrial/diagnóstico por imagem , Resultado do Tratamento , Acidente Vascular Cerebral/diagnóstico , Acidente Vascular Cerebral/etiologia , Acidente Vascular Cerebral/prevenção & controle , Hemorragia/induzido quimicamente , Hemorragia/prevenção & controle , Insuficiência Renal Crônica/complicações , Insuficiência Renal Crônica/diagnóstico , Fibrilação Atrial/complicações , Fibrilação Atrial/diagnóstico , Fibrilação Atrial/terapia , Anticoagulantes/efeitos adversosRESUMO
A growing number of studies have shown the strong relationship between sleep and different cognitive processes, especially those that involve memory consolidation. Traditionally, these processes were attributed to mechanisms related to the macroarchitecture of sleep, as sleep cycles or the duration of specific stages, such as the REM stage. More recently, the relationship between different cognitive traits and specific waves (sleep spindles or slow oscillations) has been studied. We here present the most important physiological processes induced by sleep, with particular focus on brain electrophysiology. In addition, recent and classical literature were reviewed to cover the gap between sleep and cognition, while illustrating this relationship by means of clinical examples. Finally, we propose that future studies may focus not only on analyzing specific waves, but also on the relationship between their characteristics as potential biomarkers for multiple diseases.
Assuntos
Eletroencefalografia , Consolidação da Memória , Encéfalo/fisiologia , Cognição , Consolidação da Memória/fisiologia , Sono/fisiologia , Fases do Sono/fisiologiaRESUMO
The airflow (AF) is a physiological signal involved in the overnight polysomnography (PSG) that reflects the respiratory activity. This signal is able to show the particularities of sleep apnea and is therefore used to define apneic events. In this regard, a growing number of studies have shown the usefulness of employing the overnight airflow as the only or combined information source for diagnosing sleep apnea in both children and adults. Due to its easy acquisition and interpretation, this biosignal has been widely analyzed by means of different signal processing techniques. In this chapter, we review the main methodological approaches applied to characterize and extract relevant information from this signal. In view of the results, we can conclude that the overnight airflow successfully reflects the particularities caused by the occurrence of apneic and hypopneic events and provides useful information for obtaining relevant biomarkers that characterize this disease.
Assuntos
Síndromes da Apneia do Sono , Apneia Obstrutiva do Sono , Adulto , Criança , Humanos , Polissonografia/métodos , Ventilação Pulmonar/fisiologia , Processamento de Sinais Assistido por Computador , Sono , Síndromes da Apneia do Sono/diagnóstico , Apneia Obstrutiva do Sono/diagnósticoRESUMO
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.
Assuntos
Síndromes da Apneia do Sono , Apneia Obstrutiva do Sono , Humanos , Hipóxia/diagnóstico , Oximetria/métodos , Oxigênio , Síndromes da Apneia do Sono/diagnóstico , Síndromes da Apneia do Sono/terapia , Apneia Obstrutiva do Sono/diagnóstico , Apneia Obstrutiva do Sono/terapiaRESUMO
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.
Assuntos
Inteligência Artificial , Apneia Obstrutiva do Sono , Humanos , Aprendizado de Máquina , Polissonografia/métodos , Apneia Obstrutiva do Sono/diagnósticoRESUMO
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.
Assuntos
Aprendizado Profundo , Síndromes da Apneia do Sono , Apneia Obstrutiva do Sono , Adulto , Humanos , Redes Neurais de Computação , Oximetria , Oxigênio , Polissonografia , Síndromes da Apneia do Sono/diagnóstico , Apneia Obstrutiva do Sono/diagnósticoRESUMO
BACKGROUND: Classic spectral analysis of heart rate variability (HRV) in pediatric sleep apnea-hypopnea syndrome (SAHS) traditionally evaluates the very low frequency (VLF: 0-0.04 Hz), low frequency (LF: 0.04-0.15 Hz), and high frequency (HF: 0.15-0.40 Hz) bands. However, specific SAHS-related frequency bands have not been explored. METHODS: One thousand seven hundred and thirty-eight HRV overnight recordings from two pediatric databases (0-13 years) were evaluated. The first one (981 children) served as training set to define new HRV pediatric SAHS-related frequency bands. The associated relative power (RP) were computed in the test set, the Childhood Adenotonsillectomy Trial database (CHAT, 757 children). Their relationships with polysomnographic variables and diagnostic ability were assessed. RESULTS: Two new specific spectral bands of pediatric SAHS within 0-0.15 Hz were related to duration of apneic events, number of awakenings, and wakefulness after sleep onset (WASO), while an adaptive individual-specific new band from HF was related to oxyhemoglobin desaturations, arousals, and WASO. Furthermore, these new spectral bands showed improved diagnostic ability than classic HRV. CONCLUSIONS: Novel spectral bands provide improved characterization of pediatric SAHS. These findings may pioneer a better understanding of the effects of SAHS on cardiac function and potentially serve as detection biomarkers. IMPACT: New specific heart rate variability (HRV) spectral bands are identified and characterized as potential biomarkers in pediatric sleep apnea. Spectral band BW1 (0.001-0.005 Hz) is related to macro sleep disruptions. Spectral band BW2 (0.028-0.074 Hz) is related to the duration of apneic events. An adaptive spectral band within the respiratory range, termed ABW3, is related to oxygen desaturations. The individual and collective diagnostic ability of these novel spectral bands outperforms classic HRV bands.
Assuntos
Frequência Cardíaca , Síndromes da Apneia do Sono/fisiopatologia , Adolescente , Criança , Pré-Escolar , Feminino , Humanos , Lactente , Recém-Nascido , MasculinoRESUMO
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.
Assuntos
Apneia Obstrutiva do Sono , Análise de Ondaletas , Teorema de Bayes , Criança , Feminino , Humanos , Masculino , Oximetria , Polissonografia , Apneia Obstrutiva do Sono/diagnósticoRESUMO
The author wishes to make the following correction to this paper [...].
RESUMO
Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to the assessment of HRV in pediatric OSA. Here, this work aimed to assess HRV using bispectral analysis in children with OSA for signal characterization and diagnostic purposes in two large pediatric databases (0-13 years). The first database (training set) was composed of 981 overnight ECG recordings obtained during polysomnography. The second database (test set) was a subset of the Childhood Adenotonsillectomy Trial database (757 children). We characterized three bispectral regions based on the classic HRV frequency ranges (very low frequency: 0-0.04 Hz; low frequency: 0.04-0.15 Hz; and high frequency: 0.15-0.40 Hz), as well as three OSA-specific frequency ranges obtained in recent studies (BW1: 0.001-0.005 Hz; BW2: 0.028-0.074 Hz; BWRes: a subject-adaptive respiratory region). In each region, up to 14 bispectral features were computed. The fast correlation-based filter was applied to the features obtained from the classic and OSA-specific regions, showing complementary information regarding OSA alterations in HRV. This information was then used to train multi-layer perceptron (MLP) neural networks aimed at automatically detecting pediatric OSA using three clinically defined severity classifiers. Both classic and OSA-specific MLP models showed high and similar accuracy (Acc) and areas under the receiver operating characteristic curve (AUCs) for moderate (classic regions: Acc = 81.0%, AUC = 0.774; OSA-specific regions: Acc = 81.0%, AUC = 0.791) and severe (classic regions: Acc = 91.7%, AUC = 0.847; OSA-specific regions: Acc = 89.3%, AUC = 0.841) OSA levels. Thus, the current findings highlight the usefulness of bispectral analysis on HRV to characterize and diagnose pediatric OSA.
RESUMO
The reference standard to diagnose pediatric Obstructive Sleep Apnea (OSA) syndrome is an overnight polysomnographic evaluation. When polysomnography is either unavailable or has limited availability, OSA screening may comprise the automatic analysis of a minimum number of signals. The primary objective of this study was to evaluate the complementarity of airflow (AF) and oximetry (SpO2) signals to automatically detect pediatric OSA. Additionally, a secondary goal was to assess the utility of a multiclass AdaBoost classifier to predict OSA severity in children. We extracted the same features from AF and SpO2 signals from 974 pediatric subjects. We also obtained the 3% Oxygen Desaturation Index (ODI) as a common clinically used variable. Then, feature selection was conducted using the Fast Correlation-Based Filter method and AdaBoost classifiers were evaluated. Models combining ODI 3% and AF features outperformed the diagnostic performance of each signal alone, reaching 0.39 Cohens's kappa in the four-class classification task. OSA vs. No OSA accuracies reached 81.28%, 82.05% and 90.26% in the apnea-hypopnea index cutoffs 1, 5 and 10 events/h, respectively. The most relevant information from SpO2 was redundant with ODI 3%, and AF was complementary to them. Thus, the joint analysis of AF and SpO2 enhanced the diagnostic performance of each signal alone using AdaBoost, thereby enabling a potential screening alternative for OSA in children.
RESUMO
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.
RESUMO
The ability of a cloud-driven Bluetooth oximetry-based algorithm to diagnose obstructive sleep apnoea syndrome (OSAS) was examined in habitually snoring children concurrently undergoing overnight polysomnography.Children clinically referred for overnight in-laboratory polysomnographic evaluation for suspected OSAS were simultaneously hooked to a Bluetooth oximeter linked to a smartphone. Polysomnography findings were scored and the apnoea/hypopnoea index (AHIPSG) was tabulated, while oximetry data yielded an estimated AHIOXI using a validated algorithm.The accuracy of the oximeter in identifying correctly patients with OSAS in general, or with mild (AHI 1-5â events·h-1), moderate (5-10â events·h-1) or severe (>10â events·h-1) OSAS was examined in 432 subjects (6.5±3.2â years), with 343 having AHIPSG >1â event·h-1 The accuracies of AHIOXI were consistently >79% for all levels of OSAS severity, and specificity was particularly favourable for AHI >10â events·h-1 (92.7%). Using the criterion of AHIPSG >1â event·h-1, only 4.7% of false-negative cases emerged, from which only 0.6% of cases showed moderate or severe OSAS.Overnight oximetry processed via Bluetooth technology by a cloud-based machine learning-derived algorithm can reliably diagnose OSAS in children with clinical symptoms suggestive of the disease. This approach provides virtually limitless scalability and should alleviate the substantial difficulties in accessing paediatric sleep laboratories while markedly reducing the costs of OSAS diagnosis.
Assuntos
Computação em Nuvem , Oximetria/métodos , Processamento de Sinais Assistido por Computador , Apneia Obstrutiva do Sono/diagnóstico , Ronco/diagnóstico , Adolescente , Algoritmos , Criança , Pré-Escolar , Feminino , Humanos , Masculino , Polissonografia , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
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.
RESUMO
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.
Assuntos
Oximetria/métodos , Apneia Obstrutiva do Sono/diagnóstico , Ronco/diagnóstico , Adolescente , Algoritmos , Criança , Pré-Escolar , Feminino , Humanos , Masculino , Estudos Prospectivos , Reprodutibilidade dos Testes , Índice de Gravidade de Doença , Apneia Obstrutiva do Sono/complicações , Ronco/complicações , Inquéritos e QuestionáriosRESUMO
PURPOSE: A variety of statistical models based on overnight oximetry has been proposed to simplify the detection of children with suspected obstructive sleep apnea syndrome (OSAS). Despite the usefulness reported, additional thorough comparative analyses are required. This study was aimed at assessing common binary classification models from oximetry for the detection of childhood OSAS. METHODS: Overnight oximetry recordings from 176 children referred for clinical suspicion of OSAS were acquired during in-lab polysomnography. Several training and test datasets were randomly composed by means of bootstrapping for model optimization and independent validation. For every child, blood oxygen saturation (SpO2) was parameterized by means of 17 features. Fast correlation-based filter (FCBF) was applied to search for the optimum features. The discriminatory power of three statistical pattern recognition algorithms was assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression (LR). The performance of each automated model was evaluated for the three common diagnostic polysomnographic cutoffs in pediatric OSAS: 1, 3, and 5 events/h. RESULTS: Best screening performances emerged using the 1 event/h cutoff for mild-to-severe childhood OSAS. LR achieved 84.3% accuracy (95% CI 76.8-91.5%) and 0.89 AUC (95% CI 0.83-0.94), while QDA reached 96.5% PPV (95% CI 90.3-100%) and 0.91 AUC (95% CI 0.85-0.96%). Moreover, LR and QDA reached diagnostic accuracies of 82.7% (95% CI 75.0-89.6%) and 82.1% (95% CI 73.8-89.5%) for a cutoff of 5 events/h, respectively. CONCLUSIONS: Automated analysis of overnight oximetry may be used to develop reliable as well as accurate screening tools for childhood OSAS.
Assuntos
Oximetria/métodos , Oxigênio/sangue , Apneia Obstrutiva do Sono/diagnóstico , Adolescente , Gasometria , Criança , Feminino , Humanos , Modelos Logísticos , Masculino , Monitorização Ambulatorial/métodos , Oximetria/instrumentação , Polissonografia/métodos , Reprodutibilidade dos Testes , Síndromes da Apneia do Sono/diagnósticoRESUMO
The discrimination of early Alzheimer's disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel-Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.
RESUMO
OBJECTIVE: The aim of this split-mouth, randomized controlled clinical trial was to evaluate the efficacy of proximal sealing for avoiding the development of new caries lesions or arresting incipient caries lesions on the mesial surface of first permanent molars (6m) abutting lesions on the distal surface of second primary molars (05d) in children at high caries risk. METHODS: A total of 61 children 8-10 years old were selected based on the caries status of the proximal sites of 05d and 6m. Children with caries on 05d and caries-free 6m were placed in the preventive sealing group and children with caries on both 05d and 6m in the therapeutic group. The children in each group had one 6m surface pair and in each pair one 6m surface was randomly allocated to receive a preventive or therapeutic sealing. Using a split-mouth design, the other 6m surface in the pair served as control. RESULTS: After 3.5 years, standardized follow-up radiographs were obtained for the 45 children who remained. In the preventive sealing group, 4 out of 30 (13.3%) sealed and 16 out of 30 (53.3%) unsealed sound 6m surfaces had developed new caries lesions (p = 0.004, McNemar test). In the therapeutic sealing group, the progression of the carious lesions on 6m was observed in 3 out of 15 sealed (20.0%) and 8 out of 15 (53.3%) unsealed caries control surfaces (p = 0.06). CONCLUSIONS: Preventive sealing on sound 6m surfaces abutting 05d lesions in children at high caries risk efficaciously prevents the development of caries lesions. Therapeutically sealing active non-cavitated caries lesions reduces the progression.
Assuntos
Cárie Dentária/terapia , Selantes de Fossas e Fissuras/uso terapêutico , Criança , Cárie Dentária/prevenção & controle , Feminino , Seguimentos , Humanos , Masculino , Fatores de TempoRESUMO
Social neuroscience has called for new experimental paradigms aimed toward real-time interactions. A distinctive feature of interactions is mutual information exchange: One member of a pair changes in response to the other while simultaneously producing actions that alter the other. Combining mathematical and neurophysiological methods, we introduce a paradigm called the human dynamic clamp (HDC), to directly manipulate the interaction or coupling between a human and a surrogate constructed to behave like a human. Inspired by the dynamic clamp used so productively in cellular neuroscience, the HDC allows a person to interact in real time with a virtual partner itself driven by well-established models of coordination dynamics. People coordinate hand movements with the visually observed movements of a virtual hand, the parameters of which depend on input from the subject's own movements. We demonstrate that HDC can be extended to cover a broad repertoire of human behavior, including rhythmic and discrete movements, adaptation to changes of pacing, and behavioral skill learning as specified by a virtual "teacher." We propose HDC as a general paradigm, best implemented when empirically verified theoretical or mathematical models have been developed in a particular scientific field. The HDC paradigm is powerful because it provides an opportunity to explore parameter ranges and perturbations that are not easily accessible in ordinary human interactions. The HDC not only enables to test the veracity of theoretical models, it also illuminates features that are not always apparent in real-time human social interactions and the brain correlates thereof.
Assuntos
Relações Interpessoais , Modelos Teóricos , Neurociências/métodos , Desempenho Psicomotor , Interface Usuário-Computador , Adaptação Psicológica , Adolescente , Adulto , Inteligência Artificial , Comportamento , Encéfalo , Feminino , Dedos , Humanos , Masculino , Pessoa de Meia-Idade , Movimento , Software , Adulto JovemRESUMO
The mode of action of a group of glycosylated antimicrobial peptides known as glycocins remains to be elucidated. In the current study of one glycocin, sublancin, we identified the phosphoenolpyruvate:sugar phosphotransferase system (PTS) of Bacillus species as a key player in bacterial sensitivity. Sublancin kills several Gram-positive bacteria, such as Bacillus species and Staphylococcus aureus, including methicillin-resistant S. aureus (MRSA). Unlike other classes of bacteriocins for which the PTS is involved in their mechanism of action, we show that the addition of PTS-requiring sugars leads to increased resistance rather than increased sensitivity, suggesting that sublancin has a distinct mechanism of action. Collectively, our present mutagenesis and genomic studies demonstrate that the histidine-containing phosphocarrier protein (HPr) and domain A of enzyme II (PtsG) in particular are critical determinants for bacterial sensitivity to sublancin.