1,919 research outputs found
Sleep-disordered breathing is a risk factor for delirium after cardiac surgery: a prospective cohort study
INTRODUCTION: Delirium is a frequent complication after cardiac surgery. Although various risk factors for postoperative delirium have been identified, the relationship between nocturnal breathing disorders and delirium has not yet been elucidated. This study evaluated the relationship between sleep-disordered breathing (SDB) and postoperative delirium in cardiac surgery patients without a previous diagnosis of obstructive sleep apnea. METHODS: In this prospective cohort study, 92 patients undergoing elective cardiac surgery with extracorporeal circulation were evaluated for both SDB and postoperative delirium. Polygraphic recordings were used to calculate the apnea-hypopnea index (AHI; mean number of apneas and hypopneas per hour recorded) of all patients preoperatively. Delirium was assessed during the first four postoperative days using the Confusion Assessment Method. Clinical differences between individuals with and without postoperative delirium were determined with univariate analysis. The relationship between postoperative delirium and those covariates that were associated with delirium in univariate analysis was determined by a multivariate logistic regression model. RESULTS: The median overall preoperative AHI was 18.3 (interquartile range, 8.7 to 32.8). Delirium was diagnosed in 44 patients. The median AHI differed significantly between patients with and without postoperative delirium (28 versus 13; P = 0.001). A preoperative AHI of 19 or higher was associated with an almost sixfold increased risk of postoperative delirium (odds ratio, 6.4; 95% confidence interval, 2.6 to 15.4; P <0.001). Multivariate logistic regression analysis showed that preoperative AHI, age, smoking, and blood transfusion were independently associated with postoperative delirium. CONCLUSIONS: Preoperative SDB (for example, undiagnosed obstructive sleep apnea) were strongly associated with postoperative delirium, and may be a risk factor for postoperative delirium. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13054-014-0477-1) contains supplementary material, which is available to authorized users
Automated screening of children with obstructive sleep apnea using nocturnal oximetry: An alternative to respiratory polygraphy in unattended settings
Producción CientíficaStudy Objectives: Nocturnal oximetry has emerged as a simple, readily available, and potentially useful diagnostic tool of childhood obstructive sleep apnea-hypopnea syndrome (OSAHS). However, at-home respiratory polygraphy (HRP) remains the preferred alternative to polysomnography (PSG) in unattended settings. The aim of this study was two-fold: (1) to design and assess a novel methodology for pediatric OSAHS screening based on automated analysis of at-home oxyhemoglobin saturation (SpO2), and (2) to compare its diagnostic performance with HRP.
Methods: SpO2 recordings were parameterized by means of time, frequency, and conventional oximetric measures. Logistic regression (LR) models were optimized using genetic algorithms (GAs) for 3 cutoffs for OSAHS: 1, 3, and 5 events per hour (e/h). The diagnostic performance of LR models, manual obstructive apnea-hypopnea index (OAHI) from HRP, and the conventional oxygen desaturation index ≥3% (ODI3) were assessed.
Results: For a cutoff of 1 e/h, the optimal LR model significantly outperformed both conventional HRP-derived ODI3 and OAHI: 85.5% Accuracy (HRP 74.6%; ODI3 65.9%) and 0.97 AUC (HRP 0.78; ODI3 0.75) were reached. For a cutoff of 3 e/h, the LR model achieved 83.4% Accuracy (HRP 85.0%; ODI3 74.5%) and 0.96 AUC (HRP 0.93; ODI3 0.85) whereas using a cutoff of 5 e/h, oximetry reached 82.8% Accuracy (HRP 85.1%; ODI3 76.7) and 0.97 AUC (HRP 0.95; ODI3 0.84).
Conclusions: Automated analysis of at-home SpO2 recordings provide accurate detection of children with high pre-test probability of OSAHS. Thus, unsupervised nocturnal oximetry may enable a simple and effective alternative to HRP and PSG in unattended settings.This research has been partially supported by the project 153/2015 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), the project RTC-2015-3446-1 from the Ministerio de Economía y Competitividad and the European Regional Development Fund (FEDER), and the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León and FEDER. L. Kheirandish-Gozal is supported by NIH grant 1R01HL130984-01. D. Álvarez was in receipt of a Juan de la Cierva grant from the Ministerio de Economía y Competitividad
Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity from at-Home Oximetry Recordings
Producción CientíficaComplexity, costs, and waiting lists issues demand a simplified alternative for sleep apnea-hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO2) carries useful information about SAHS and can be easily acquired from overnight oximetry. In this study, SpO2 single-channel recordings from 320 subjects were obtained at patients’ home. They were used to automatically obtain statistical, spectral, non-linear, and clinical SAHS-related information. Relevant and non-redundant data from these analyses were subsequently used to train and validate four machine-learning methods with ability to classify SpO2 signals into one out of the four SAHS-severity degrees (no-SAHS, mild, moderate, and severe). All the models trained (linear discriminant analysis, 1-vs-all logistic regression, Bayesian multi-layer perceptron, and AdaBoost), outperformed the diagnostic ability of the conventionally-used 3% oxygen desaturation index. An AdaBoost model built with linear discriminants as base classifiers reached the highest figures. It achieved 0.479 Cohen’s in the SAHS severity classification, as well as 92.9%, 87.4%, and 78.7% accuracies in binary classification tasks using increasing severity thresholds (apnea-hypopnea index: 5, 15, and 30 events/hour, respectively). These results suggest that machine learning can be used along with SpO2 information acquired at patients’ home to help in SAHS diagnosis simplification.This research has been supported by the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León, the project 265/2012 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), the projects RTC-2015-3446-1 and TEC2014-53196-R from the Ministerio de Economía y Competitividad, and the European Regional Development Fund (FEDER). D. Álvarez was in receipt of a Juan de la Cierva grant from the Ministerio de Economía y Competitivida
Statistical and Nonlinear Analysis of Oximetry from Respiratory Polygraphy to Assist in the Diagnosis of Sleep Apnea in Children
Producción CientíficaObstructive Sleep Apnea-Hypopnea Syndrome
(OSAHS) is a sleep related breathing disorder that has
important consequences in the health and development of
infants and young children. To enhance the early detection of
OSAHS, we propose a methodology based on automated
analysis of nocturnal blood oxygen saturation (SpO2) from
respiratory polygraphy (RP) at home. A database composed of
50 SpO2 recordings was analyzed. Three signal processing
stages were carried out: (i) feature extraction, where statistical
features and nonlinear measures were computed and combined
with conventional oximetric indexes, (ii) feature selection using
genetic algorithms (GAs), and (iii) feature classification through
logistic regression (LR). Leave-one-out cross-validation (loo-cv)
was applied to assess diagnostic performance. The proposed
method reached 80.8% sensitivity, 79.2% specificity, 80.0%
accuracy and 0.93 area under the ROC curve (AROC), which
improved the performance of single conventional indexes. Our
results suggest that automated analysis of SpO2 recordings from
at-home RP provides essential and complementary information
to assist in OSAHS diagnosis in children.Ministerio de Economía y Competitividad (TEC2011-22987)Fundación General CSIC (Proyecto Cero 2011 sobre Envejecimiento)Obra social de la Caixa y CSICJunta de Castilla y León (VA059U13
Multi-Class AdaBoost to Detect Sleep Apnea-Hypopnea Syndrome Severity from Oximetry Recordings Obtained at Home
Producción CientíficaThis paper aims at evaluating a novel multi-class methodology to establish Sleep Apnea-Hypopnea Syndrome (SAHS) severity by the use of single-channel at-home oximetry recordings. The study involved 320 participants derived to a specialized sleep unit due to SAHS suspicion. These were assigned to one out of the four SAHS severity degrees according to the apnea-hypopnea index (AHI): no-SAHS (AHI<5 events/hour), mild-SAHS (5≤AHI<15 e/h), moderate-SAHS (15≤AHI<30 e/h), and severe-SAHS (AHI≥30 e/h). A set of statistical, spectral, and non-linear features were extracted from blood oxygen saturation (SpO2) signals to characterize SAHS. Then, an optimum set among these features were automatically selected based on relevancy and redundancy analyses. Finally, a multi-class AdaBoost model, built with the optimum set of features, was obtained from a training set (60%) and evaluated in an independent test set (40%). Our AdaBoost model reached 0.386 Cohen’s kappa in the four-class classification task. Additionally, it reached accuracies of 89.8%, 85.8%, and 74.8% when evaluating the AHI thresholds 5 e/h, 15 e/h, and 30 e/h, respectively, outperforming the classic oxygen desaturation index. Our results suggest that SpO2 obtained at home, along with multi-class AdaBoost, are useful to detect SAHS severity.Junta de Castilla y León (project VA059U13)Pneumology and Thoracic Surgery Spanish Society (265/2012
Automated Analysis of Nocturnal Oximetry as Screening Tool for Childhood Obstructive Sleep Apnea-Hypopnea Syndrome
Producción CientíficaChildhood obstructive sleep apnea-hypopnea
syndrome (OSAHS) is a highly prevalent condition that
negatively affects health, performance and quality of life of
infants and young children. Early detection and treatment
improves neuropsychological and cognitive deficits linked with
the disease. The aim of this study was to assess the performance
of automated analysis of blood oxygen saturation (SpO2)
recordings as a screening tool for OSAHS. As an initial step,
statistical, spectral and nonlinear features were estimated to
compose an initial feature set. Then, fast correlation-based
filter (FCBF) was applied to search for the optimum subset.
Finally, the discrimination power (OSAHS negative vs. OSAHS
positive) of three pattern recognition algorithms was assessed:
linear discriminant analysis (LDA), quadratic discriminant
analysis (QDA) and logistic regression (LR). Three clinical cutoff
points commonly used in the literature for positive diagnosis
of the disease were applied: apnea-hypopnea index (AHI) of 1,
3 and 5 events per hour (e/h). Our methodology reached 88.6%
accuracy (71.4% sensitivity and 100.0% specificity, 100.0%
positive predictive value, and 84.0% negative predictive value)
in an independent test set using QDA for a clinical cut-off point
of 5 e/h. These results suggest that SpO2 nocturnal recordings
may be used to develop a reliable and efficient screening tool
for childhood OSAHSJunta de Castilla y León (project VA059U13
Multiscale entropy analysis of unattended oximetric recordings to assist in the screening of paediatric sleep apnoea at home
Producción CientíficaUntreated paediatric obstructive sleep apnoea syndrome (OSAS) can severely affect the development and quality of life of children. In-hospital polysomnography (PSG) is the gold standard for a definitive diagnosis though it is relatively unavailable and particularly intrusive. Nocturnal portable oximetry has emerged as a reliable technique for OSAS screening. Nevertheless, additional evidences are demanded. Our study is aimed at assessing the usefulness of multiscale entropy (MSE) to characterise oximetric recordings. We hypothesise that MSE could provide relevant information of blood oxygen saturation (SpO2) dynamics in the detection of childhood OSAS. In order to achieve this goal, a dataset composed of unattended SpO2 recordings from 50 children showing clinical suspicion of OSAS was analysed. SpO2 was parameterised by means of MSE and conventional oximetric indices. An optimum feature subset composed of five MSE-derived features and four conventional clinical indices were obtained using automated bidirectional stepwise feature selection. Logistic regression (LR) was used for classification. Our optimum LR model reached 83.5% accuracy (84.5% sensitivity and 83.0% specificity). Our results suggest that MSE provides relevant information from oximetry that is complementary to conventional approaches. Therefore, MSE may be useful to improve the diagnostic ability of unattended oximetry as a simplified screening test for childhood OSAS.Sociedad Española de Neumología y Cirugía Torácica (SEPAR) project 153/2015Junta de Castilla y León (Consejería de Educación) y el Fondo Europeo de Desarrollo Regional (FEDER), projects (RTC-2015-3446-1) y (TEC2014-53196-R)Ministerio de Economía y Competitividad (MINECO) y FEDER, y el proyecto POCTEP 0378_AD_EEGWA_2_P de la Comisión Europea. L.National Institutes of Health (NIH) grant 1R01HL130984-01Ministerio de Asuntos Económicos y Transformación Digital, grant IJCI-2014-2266
Oximetry use in obstructive sleep apnea
Producción CientíficaIntroduction. Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS.
Areas covered. Currently, high-resolution portable devices combined with pattern recognition-based applications are able to achieve high performance in the detection this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed.
Expert commentary. Oximetry is a cost-effective tool for OSAS screening in patients showing high pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in especial groups with significant comorbidities. In the following years, communication technologies and big data analysis will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.This research has been partially supported by the projects DPI2017-84280-R and RTC-2015-3446-1 from Ministerio de Economía, Industria y Competitividad and European Regional Development Fund (FEDER), the project 66/2016 of the Sociedad Española de Neumología y Cirugía Torácica (SEPAR), and the project VA037U16 from the Consejería de Educación de la Junta de Castilla y León and FEDER. D. Álvarez was in receipt of a Juan de la Cierva grant IJCI-2014-22664 from the Ministerio de Economía y Competitividad
Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography
The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave)
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