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Wilson A, Hartnett C, Kilner D, Davies K, Slee N, Chawla J, Iyer K, Kevat A. Real-world utility of overnight oximetry for the screening of obstructive sleep apnea in children. Int J Pediatr Otorhinolaryngol 2024; 178:111892. [PMID: 38387157 DOI: 10.1016/j.ijporl.2024.111892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 01/23/2024] [Accepted: 02/07/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Obstructive sleep apnea (OSA) is a common problem in children and can result in developmental and cognitive complications if untreated. The gold-standard tool for diagnosis is polysomnography (PSG); however, it is an expensive and time-consuming test to undertake. Overnight oximetry has been suggested as a faster and cheaper initial test in comparison to PSG as it can be performed at home using limited, reusable equipment. AIM This retrospective case control study aims to evaluate the effectiveness of a home oximetry service (implemented in response to extended waiting times for routine PSG) in reducing the time between patient referral and treatment. METHODS Patients undergoing diagnostic sleep evaluation for suspected OSA who utilized the Queensland Children's Hospital screening home oximetry service in the first year since its inception in 2021 (n = 163) were compared to a historical group of patients who underwent PSG in 2018 (n = 311). Parameters compared between the two groups included time from sleep physician review to sleep test, ENT review, and definitive treatment in the form of adenotonsillectomy surgery (or CPAP initiation for those who had already undergone surgery). RESULTS The time from sleep physician review and request of the sleep-related study to ENT surgical treatment was significantly reduced (187 days for the HITH oximetry group vs 359 days for the comparable PSG group; p-value <0.05), and time from sleep study request to the report of results was significantly lower for patients in the oximetry group compared to those in the PSG group (11 days vs 105 days; p-value <0.05). CONCLUSION These results suggest that for children referred to a tertiary sleep center for possible obstructive sleep disordered breathing, a home oximetry service can be effective in assisting sleep evaluation and reducing the time to OSA treatment.
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Affiliation(s)
- Alice Wilson
- Faculty of Medicine, The University of Queensland, Brisbane, Australia.
| | - Chloe Hartnett
- Department of Respiratory and Sleep Medicine, Queensland Children's Hospital, Brisbane, Australia.
| | - David Kilner
- Department of Respiratory and Sleep Medicine, Queensland Children's Hospital, Brisbane, Australia.
| | - Kate Davies
- Department of General Medicine, Queensland Children's Hospital, Brisbane, Australia.
| | - Nicola Slee
- Department of Otolaryngology Head and Neck Surgery, Queensland Children's Hospital, Brisbane, Australia.
| | - Jasneek Chawla
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Department of Respiratory and Sleep Medicine, Queensland Children's Hospital, Brisbane, Australia.
| | - Kartik Iyer
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; QIMR Berghofer Medical Research Institute, Brisbane, Australia.
| | - Ajay Kevat
- Faculty of Medicine, The University of Queensland, Brisbane, Australia; Department of Respiratory and Sleep Medicine, Queensland Children's Hospital, Brisbane, Australia.
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He S, Cistulli PA, de Chazal P. A Review of Novel Oximetry Parameters for the Prediction of Cardiovascular Disease in Obstructive Sleep Apnoea. Diagnostics (Basel) 2023; 13:3323. [PMID: 37958218 PMCID: PMC10649141 DOI: 10.3390/diagnostics13213323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/18/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
Obstructive sleep apnoea (OSA) is a sleep disorder with repetitive collapse of the upper airway during sleep, which leads to intermittent hypoxic events overnight, adverse neurocognitive, metabolic complications, and ultimately an increased risk of cardiovascular disease (CVD). The standard diagnostic parameter for OSA, apnoea-hypopnoea index (AHI), is inadequate to predict CVD morbidity and mortality, because it focuses only on the frequency of apnoea and hypopnoea events, and fails to reveal other physiological information for the prediction of CVD events. Novel parameters have been introduced to compensate for the deficiencies of AHI. However, the calculation methods and criteria for these parameters are unclear, hindering their use in cross-study analysis and studies. This review aims to discuss novel parameters for predicting CVD events from oximetry signals and to summarise the corresponding computational methods.
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Affiliation(s)
- Siying He
- Charles Perkins Centre, Faculty of Engineering, Sydney University, Camperdown, NSW 2050, Australia;
| | - Peter A. Cistulli
- Charles Perkins Centre, Faculty of Medicine and Health, Sydney University, Camperdown, NSW 2050, Australia;
- Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, St Leonards, NSW 2065, Australia
| | - Philip de Chazal
- Charles Perkins Centre, Faculty of Engineering, Sydney University, Camperdown, NSW 2050, Australia;
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Ramamurthy MB, Jayaprakash S. Introduction to Pediatric Sleep Medicine. Indian J Pediatr 2023; 90:927-933. [PMID: 37378884 DOI: 10.1007/s12098-023-04697-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/19/2023] [Indexed: 06/29/2023]
Abstract
Sleep is a key component of life to maintain our health, performance, safety and quality of life. In fact, sleep has been implicated in the optimal functioning of all organ systems - brain, heart, lung, metabolism, immune function, and hormonal balance too. One of the most common reasons for poor-quality sleep in children is a group of conditions termed sleep disordered breathing (SDB). Obstructive sleep apnea (OSA) is the most severe form of SDB. A good history and clinical examination is likely to reveal features of SDB including snoring, restless sleep, morning sleepiness, irritability or exhibit signs of hyperactivity. Examination may also reveal evidence of underlying pathology e.g., Craniofacial abnormalities, obesity and neuromuscular disorders that creates higher risk of developing SDB. Further investigation using polysomnography (PSG) is considered a gold-standard assessment of SDB and allows for scoring using Obstructive Apnoea-Hypopnea scale. Adenotonsillectomy is used as the first-line management in patients who otherwise have normal anatomy. Parents often approach their pediatricians with concerns regarding their child's sleeping habits and given the significant role sleep has on child development, it is essential that doctors are equipped to provide good care and advice for this population. This article aims to summarise presentation of SDB and common risk factors, investigations and management options to aid clinicians in managing SDB.
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Affiliation(s)
- Mahesh Babu Ramamurthy
- Department of Pediatrics, KTP-National University Children's Medical Institute, National University Hospital, Singapore, Singapore.
- Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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Mesolella M, Allosso S, Coronella V, Massimilla EA, Mansi N, Motta G, Salerno G, Motta G. Extracapsular Tonsillectomy versus Intracapsular Tonsillotomy in Paediatric Patients with OSAS. J Pers Med 2023; 13:jpm13050806. [PMID: 37240976 DOI: 10.3390/jpm13050806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/30/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
OBJECTIVE The objective of our study was to compare our experience of intracapsular tonsillotomy performed with the help of a microdebrider usually used for adenoidectomy with results obtained from extracapsular surgery through dissection and from adenoidectomy in cases of people affected with OSAS, linked to adeno-tonsil hypertrophy, observed and treated in the last 5 years. METHODS 3127 children with adenotonsillar hyperplasia and OSAS-related clinical symptoms (aged between 3 and 12 years) underwent tonsillectomy and/or adenoidectomy. A total of 1069 patients (Group A) underwent intracapsular tonsillotomy, while 2058 patients (Group B) underwent extracapsular tonsillectomy, from January 2014 to June 2018. The parameters considered in order to evaluate the effectiveness of the two different surgery techniques taken into consideration were as follows: the presence of possible postoperative complications, represented mainly by pain and perioperative bleeding; the level of postoperative respiratory obstruction compared with the original obstruction through night pulse oximetry, performed 6 months before and after the surgery; tonsillar hypertrophy relapse in Group A and/or the presence of residues in Group B with clinical evaluation performed 1 month, 6 months, and 1 year after the surgery; and postoperative life quality, evaluated through submitting to parents the same survey proposed before the surgery 1 month, 6 months, and 1 year after the surgery. RESULTS Regardless of the technique used (extracapsular tonsillectomy or intracapsular tonsillotomy), there was a clear improvement in both the obstructive respiratory symptomatology and quality of life in both patient groups, as highlighted by the pulse oximetry and the OSA-18 survey submitted later. CONCLUSIONS Intracapsular tonsillotomy surgery has improved in terms of a reduction in postoperative bleeding cases and pain reduction, with an earlier return to patients' usual lifestyle. Lastly, using a microdebrider with the intracapsular technique seems to be particularly effective in removing most of the tonsillar lymphatic tissue, leaving only a thin border of pericapsular lymphoid tissue and preventing lymphoid tissue regrowth during one year of follow-up.
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Affiliation(s)
- Massimo Mesolella
- Unit of Otorhinolaryngology, Department of Neuroscience, Reproductive Sciences and Dentistry, University Federico II of Naples, 80138 Napoli, Italy
| | - Salvatore Allosso
- Unit of Otorhinolaryngology, Department of Neuroscience, Reproductive Sciences and Dentistry, University Federico II of Naples, 80138 Napoli, Italy
| | - Valentina Coronella
- Unit of Otorhinolaryngology, Department of Neuroscience, Reproductive Sciences and Dentistry, University Federico II of Naples, 80138 Napoli, Italy
| | | | - Nicola Mansi
- Otorhinolaryngology Unit, AORN Santobono-Pausilipon, 80112 Naples, Italy
| | - Giovanni Motta
- Unit of Otorhinolaryngology, University Luigi Vanvitelli, 80138 Napoli, Italy
| | - Grazia Salerno
- Unit of Otorhinolaryngology, Department of Neuroscience, Reproductive Sciences and Dentistry, University Federico II of Naples, 80138 Napoli, Italy
| | - Gaetano Motta
- Unit of Otorhinolaryngology, University Luigi Vanvitelli, 80138 Napoli, Italy
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Bravo-Quelle N, Sáez-Ansotegui A, Bellón-Alonso S, Lowy-Benoliel A, García-Santiago S, Ribeiro-Arold C, Prieto-Montalvo J. [Prevalence of childhood obstructive sleep apnoea syndrome in a referral sleep unit]. Rev Neurol 2023; 76:279-285. [PMID: 37102252 PMCID: PMC10478145 DOI: 10.33588/rn.7609.2023076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Indexed: 04/28/2023]
Abstract
INTRODUCTION Obstructive sleep apnoea syndrome (OSAS) affects between 1% and 6% of children. Its diagnosis includes: a) snoring and/or apnoea; and b) an apnoea and hypopnoea index >3/hour obtained by polysomnography (PSG). The main aim of this work is to determine the prevalence of OSAS in our study population. PATIENTS AND METHODS We conducted a descriptive study with a sample of 151 children aged between 1 and 12 years, who had been referred to the sleep unit of the Hospital General Universitario Gregorio Maranon for a PSG. We analysed the demographic variables sex and age; the clinical variables snoring, apnoeas and tonsillar hypertrophy; and the presence of OSAS based on the polysomnographic diagnostic criterion of an apnoea and hypopnoea index >3/hour. RESULTS The mean age of the sample was 5.37 years (standard deviation: 3.05) and 64.9% were males. In 90.1% of cases, the reason for the visit was suspected OSAS. Snoring, apnoeas and tonsillar hypertrophy were observed in 73.5, 48.7 and 60% of cases, respectively. OSAS was diagnosed en 19 children (12.6%); in 13.5% of snorers; in 15.1% of those with apnoeas; and in 15.6% of the children with tonsillar hypertrophy. CONCLUSIONS In our study, the prevalence of OSAS in children was 12.6%, which is higher than that reported in most epidemiological studies that include PSG for the diagnosis of OSAS.
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Affiliation(s)
| | - Aiala Sáez-Ansotegui
- Servicio de Neurofisiología ClínicaServicio de Neurofisiología ClínicaServicio de Neurofisiología ClínicaMadridEspaña
| | - Sara Bellón-Alonso
- Unidad de Neumología Pediátrica. Servicio de Pediatría. Hospital Materno Infantil Gregorio Marañón. Madrid, EspañaHospital Materno Infantil Gregorio MarañónHospital Materno Infantil Gregorio MarañónMadridEspaña
| | - Alejandro Lowy-Benoliel
- Sección de Otorrinolaringología Pediátrica. Servicio de Otorrinolaringología. Hospital General Universitario Gregorio MarañónHospital General Universitario Gregorio MarañónHospital General Universitario Gregorio MarañónMadridEspaña
| | | | | | - Julio Prieto-Montalvo
- Servicio de Neurofisiología ClínicaServicio de Neurofisiología ClínicaServicio de Neurofisiología ClínicaMadridEspaña
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Martín-Montero A, Armañac-Julián P, Gil E, Kheirandish-Gozal L, Álvarez D, Lázaro J, Bailón R, Gozal D, Laguna P, Hornero R, Gutiérrez-Tobal GC. Pediatric sleep apnea: Characterization of apneic events and sleep stages using heart rate variability. Comput Biol Med 2023; 154:106549. [PMID: 36706566 DOI: 10.1016/j.compbiomed.2023.106549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 12/19/2022] [Accepted: 01/11/2023] [Indexed: 01/16/2023]
Abstract
Heart rate variability (HRV) is modulated by sleep stages and apneic events. Previous studies in children compared classical HRV parameters during sleep stages between obstructive sleep apnea (OSA) and controls. However, HRV-based characterization incorporating both sleep stages and apneic events has not been conducted. Furthermore, recently proposed novel HRV OSA-specific parameters have not been evaluated. Therefore, the aim of this study was to characterize and compare classic and pediatric OSA-specific HRV parameters while including both sleep stages and apneic events. A total of 1610 electrocardiograms from the Childhood Adenotonsillectomy Trial (CHAT) database were split into 10-min segments to extract HRV parameters. Segments were characterized and grouped by sleep stage (wake, W; non-rapid eye movement, NREMS; and REMS) and presence of apneic events (under 1 apneic event per segment, e/s; 1-5 e/s; 5-10 e/s; and over 10 e/s). NREMS showed significant changes in HRV parameters as apneic event frequency increased, which were less marked in REMS. In both NREMS and REMS, power in BW2, a pediatric OSA-specific frequency domain, allowed for the optimal differentiation among segments. Moreover, in the absence of apneic events, another defined band, BWRes, resulted in best differentiation between sleep stages. The clinical usefulness of segment-based HRV characterization was then confirmed by two ensemble-learning models aimed at estimating apnea-hypopnea index and classifying sleep stages, respectively. We surmise that basal sympathetic activity during REMS may mask apneic events-induced sympathetic excitation, thus highlighting the importance of incorporating sleep stages as well as apneic events when evaluating HRV in pediatric OSA.
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Affiliation(s)
- Adrián Martín-Montero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.
| | - Pablo Armañac-Julián
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Eduardo Gil
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Jesús Lázaro
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Raquel Bailón
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - David Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Pablo Laguna
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain; Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
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7
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Crowson MG, Gipson KS, Kadosh OK, Hartnick E, Grealish E, Keamy DG, Kinane TB, Hartnick CJ. Paediatric sleep apnea event prediction using nasal air pressure and machine learning. J Sleep Res 2023:e13851. [PMID: 36807952 PMCID: PMC10363180 DOI: 10.1111/jsr.13851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/23/2023]
Abstract
Sleep-disordered breathing is an important health issue for children. The objective of this study was to develop a machine learning classifier model for the identification of sleep apnea events taken exclusively from nasal air pressure measurements acquired during overnight polysomnography for paediatric patients. A secondary objective of this study was to differentiate site of obstruction exclusively from hypopnea event data using the model. Computer vision classifiers were developed via transfer learning to either normal breathing while asleep, obstructive hypopnea, obstructive apnea or central apnea. A separate model was trained to identify site of obstruction as either adeno-tonsillar or tongue base. In addition, a survey of board-certified and board-eligible sleep physicians was completed to compare clinician versus model classification performance of sleep events, and indicated very good performance of our model relative to human raters. The nasal air pressure sample database available for modelling comprised 417 normal, 266 obstructive hypopnea, 122 obstructive apnea and 131 central apnea events derived from 28 paediatric patients. The four-way classifier achieved a mean prediction accuracy of 70.0% (95% confidence interval [67.1-72.9]). Clinician raters correctly identified sleep events from nasal air pressure tracings 53.8% of the time, whereas the local model was 77.5% accurate. The site of obstruction classifier achieved a mean prediction accuracy of 75.0% (95% confidence interval [68.7-81.3]). Machine learning applied to nasal air pressure tracings is feasible and may exceed the diagnostic performance of expert clinicians. Nasal air pressure tracings of obstructive hypopneas may "encode" information regarding the site of obstruction, which may only be discernable by machine learning.
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Affiliation(s)
- Matthew G Crowson
- Department of Otolaryngology-Head & Neck Surgery, Mass Eye & Ear, Boston, Massachusetts, USA.,Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, USA
| | - Kevin S Gipson
- Department of Pediatric Pulmonary Medicine, Mass General Hospital for Children, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Orna Katz Kadosh
- Department of Otolaryngology-Head & Neck Surgery, Mass Eye & Ear, Boston, Massachusetts, USA.,Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Ellen Grealish
- Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Donald G Keamy
- Department of Otolaryngology-Head & Neck Surgery, Mass Eye & Ear, Boston, Massachusetts, USA.,Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, USA
| | - Thomas Bernard Kinane
- Department of Pediatric Pulmonary Medicine, Mass General Hospital for Children, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Christopher J Hartnick
- Department of Otolaryngology-Head & Neck Surgery, Mass Eye & Ear, Boston, Massachusetts, USA.,Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, USA
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8
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Polytarchou A, Ohler A, Moudaki A, Koltsida G, Kanaka-Gantenbein C, Kheirandish-Gozal L, Gozal D, Kaditis AG. Nocturnal oximetry parameters as predictors of sleep apnea severity in resource-limited settings. J Sleep Res 2023; 32:e13638. [PMID: 35624085 DOI: 10.1111/jsr.13638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 04/30/2022] [Accepted: 05/02/2022] [Indexed: 02/03/2023]
Abstract
Nocturnal oximetry is an alternative modality for evaluating obstructive sleep apnea syndrome (OSAS) severity when polysomnography is not available. The Oxygen Desaturation (≥3%) Index (ODI3) and McGill Oximetry Score (MOS) are used as predictors of moderate-to-severe OSAS (apnea-hypopnea index-AHI >5 episodes/h), an indication for adenotonsillectomy. We hypothesised that ODI3 is a better predictive parameter for AHI >5 episodes/h than the MOS. All polysomnograms performed in otherwise healthy, snoring children with tonsillar hypertrophy in a tertiary hospital (November 2014 to May 2019) were analysed. The ODI3 and MOS were derived from the oximetry channel of each polysomnogram. Logistic regression was applied to assess associations of ODI3 or MOS (predictors) with an AHI >5 episodes/h (primary outcome). Receiver operating characteristic (ROC) curves and areas under ROC curves were used to compare the ODI3 and MOS as predictors of moderate-to-severe OSAS. The optimal cut-off value for each oximetry parameter was determined using Youden's index. Polysomnograms of 112 children (median [interquartile range] age 6.1 [3.9-9.1] years; 35.7% overweight) were analysed. Moderate-to-severe OSAS prevalence was 49.1%. The ODI3 and MOS were significant predictors of moderate-to-severe OSAS after adjustment for overweight, sex, and age (odds ratio [OR] 1.34, 95% confidence interval [CI] 1.19-1.51); and OR 4.10, 95% CI 2.06-8.15, respectively; p < 0.001 for both). Area under the ROC curve was higher for the ODI3 than for MOS (0.903 [95% CI 0.842-0.964] versus 0.745 [95% CI 0.668-0.821]; p < 0.001). Optimal cut-off values for the ODI3 and MOS were ≥4.3 episodes/h and ≥2, respectively. The ODI3 emerges as preferable or at least a complementary oximetry parameter to MOS for detecting moderate-to-severe OSAS in snoring children when polysomnography is not available.
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Affiliation(s)
- Anastasia Polytarchou
- Division of Pediatric Pulmonology, First Department of Pediatrics, National and Kapodistrian University of Athens School of Medicine and Agia Sofia Children's Hospital, Athens, Greece
| | - Adrienne Ohler
- Child Health Research Institute, University of Missouri School of Medicine, Columbia, Missouri, USA
| | - Aggeliki Moudaki
- Division of Pediatric Pulmonology, First Department of Pediatrics, National and Kapodistrian University of Athens School of Medicine and Agia Sofia Children's Hospital, Athens, Greece
| | - Georgia Koltsida
- Division of Pediatric Pulmonology, First Department of Pediatrics, National and Kapodistrian University of Athens School of Medicine and Agia Sofia Children's Hospital, Athens, Greece
| | - Christina Kanaka-Gantenbein
- Division of Pediatric Pulmonology, First Department of Pediatrics, National and Kapodistrian University of Athens School of Medicine and Agia Sofia Children's Hospital, Athens, Greece
| | - Leila Kheirandish-Gozal
- Child Health Research Institute, University of Missouri School of Medicine, Columbia, Missouri, USA.,Division of Pediatric Pulmonology and Pediatric Sleep Center, Department of Child Health, University of Missouri School of Medicine and MUHC Children's Hospital, Columbia, Missouri, USA
| | - David Gozal
- Child Health Research Institute, University of Missouri School of Medicine, Columbia, Missouri, USA.,Division of Pediatric Pulmonology and Pediatric Sleep Center, Department of Child Health, University of Missouri School of Medicine and MUHC Children's Hospital, Columbia, Missouri, USA
| | - Athanasios G Kaditis
- Division of Pediatric Pulmonology, First Department of Pediatrics, National and Kapodistrian University of Athens School of Medicine and Agia Sofia Children's Hospital, Athens, Greece.,Child Health Research Institute, University of Missouri School of Medicine, Columbia, Missouri, USA.,Division of Pediatric Pulmonology and Pediatric Sleep Center, Department of Child Health, University of Missouri School of Medicine and MUHC Children's Hospital, Columbia, Missouri, USA
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9
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Zaffanello M, Pietrobelli A, Gozal D, Nosetti L, La Grutta S, Cilluffo G, Ferrante G, Piazza M, Piacentini G. Cluster Analysis of Home Polygraphic Recordings in Symptomatic Habitually-Snoring Children: A Precision Medicine Perspective. J Clin Med 2022; 11:jcm11195960. [PMID: 36233827 PMCID: PMC9571925 DOI: 10.3390/jcm11195960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/30/2022] [Accepted: 10/04/2022] [Indexed: 11/04/2022] Open
Abstract
(1) Background: Sleep-disordered breathing (SDB) is a frequent problem in children. Cluster analyses offer the possibility of identifying homogeneous groups within a large clinical database. The application of cluster analysis to anthropometric and polysomnographic measures in snoring children would enable the detection of distinctive clinically-relevant phenotypes; (2) Methods: We retrospectively collected the results of nocturnal home-based cardiorespiratory polygraphic recordings and anthropometric measurements in 326 habitually-snoring otherwise healthy children. K-medoids clustering was applied to standardized respiratory and anthropometric measures, followed by Silhouette-based statistics. Respiratory Disturbance Index (RDI) and oxygen desaturation index (≤3%) were included in determining the optimal number of clusters; (3) Results: Mean age of subjects was 8.1 ± 4.1 years, and 57% were males. Cluster analyses uncovered an optimal number of three clusters. Cluster 1 comprised 59.5% of the cohort (mean age 8.69 ± 4.14 years) with a mean RDI of 3.71 ± 3.23 events/hour of estimated sleep (e/ehSleep). Cluster 2 included 28.5% of the children (mean age 6.92 ± 3.43 years) with an RDI of 6.38 ± 3.92 e/ehSleep. Cluster 3 included 12% of the cohort (mean age 7.58 ± 4.73 years) with a mean RDI of 25.5 ± 19.4 e/ehSleep. Weight z-score was significantly lower in cluster 3 [-0.14 ± 1.65] than in cluster 2 [0.86 ± 1.78; p = 0.015] and cluster 1 [1.04 ± 1.78; p = 0.002]. Similar findings emerged for BMI z scores. However, the height z-score was not significantly different among the 3 clusters; (4) Conclusions: Cluster analysis of children who are symptomatic habitual snorers and are referred for clinical polygraphic evaluation identified three major clusters that differed in age, RDI, and anthropometric measures. An increased number of children in the cluster with the highest RDI had reduced body weight. We propose that the implementation of these approaches to a multicenter-derived database of home-based polygraphic recordings may enable the delineation of objective unbiased severity categories of pediatric SDB. Our findings could be useful for clinical implementation, formulation of therapeutic decision guidelines, clinical management, prevision of complications, and long-term follow-up.
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Affiliation(s)
- Marco Zaffanello
- Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, 37129 Verona, Italy
- Correspondence:
| | - Angelo Pietrobelli
- Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, 37129 Verona, Italy
| | - David Gozal
- Departments of Child Health, and Medical Pharmacology and Physiology, School of Medicine, University of Missouri, Columbia, MO 65212, USA
| | - Luana Nosetti
- Pediatric Sleep Disorders Center, Division of Pediatrics, F. Del Ponte Hospital, Insubria University, 21100 Varese, Italy
| | - Stefania La Grutta
- Institute of Translational Pharmacology (IFT), National Research Council, 90146 Palermo, Italy
| | - Giovanna Cilluffo
- Department of Earth and Marine Sciences, University of Palermo, 90133 Palermo, Italy
| | - Giuliana Ferrante
- Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, 37129 Verona, Italy
| | - Michele Piazza
- Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, 37129 Verona, Italy
| | - Giorgio Piacentini
- Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, 37129 Verona, Italy
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10
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Gutiérrez-Tobal GC, Álvarez D, Kheirandish-Gozal L, Del Campo F, Gozal D, Hornero R. Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta-analysis. Pediatr Pulmonol 2022; 57:1931-1943. [PMID: 33856128 DOI: 10.1002/ppul.25423] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 04/07/2021] [Accepted: 04/10/2021] [Indexed: 12/27/2022]
Abstract
BACKGROUND Machine-learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice. OBJECTIVE To assess the reliability of machine-learning-based methods to detect pediatric OSA. DATA SOURCES Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references. ELIGIBILITY CRITERIA Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard. APPRAISAL AND SYNTHESIS METHODS Pooled sensitivities and specificities were computed for three apnea-hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random-effect models were assumed. Summary receiver-operating characteristics (SROC) analyses were also conducted. Heterogeneity (I 2 ) was evaluated, and publication bias was corrected (trim and fill). RESULTS Nineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.
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Affiliation(s)
- Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Zaragoza, Spain
| | - Daniel Álvarez
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Zaragoza, Spain.,Department of Pneumology, Río Hortega University Hospital, Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health, Child Health Research Institute, The University of Missouri School of Medicine, Columbia, Missouri, USA
| | - Félix Del Campo
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Zaragoza, Spain.,Department of Pneumology, Río Hortega University Hospital, Valladolid, Spain
| | - David Gozal
- Department of Child Health, Child Health Research Institute, The University of Missouri School of Medicine, Columbia, Missouri, USA
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Zaragoza, Spain.,Department of Pneumology, Río Hortega University Hospital, Valladolid, Spain
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11
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Jiménez-García J, García M, Gutiérrez-Tobal GC, Kheirandish-Gozal L, Vaquerizo-Villar F, Álvarez D, del Campo F, Gozal D, Hornero R. A 2D convolutional neural network to detect sleep apnea in children using airflow and oximetry. Comput Biol Med 2022; 147:105784. [DOI: 10.1016/j.compbiomed.2022.105784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/19/2022] [Accepted: 06/26/2022] [Indexed: 11/03/2022]
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12
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Martinot JB, Cuthbert V, Le-Dong NN, Coumans N, De Marneffe D, Letesson C, Pépin JL, Gozal D. Clinical validation of a mandibular movement signal based system for the diagnosis of pediatric sleep apnea. Pediatr Pulmonol 2022; 57:1904-1913. [PMID: 33647188 DOI: 10.1002/ppul.25320] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Given the high prevalence and risk for outcomes associated with pediatric obstructive sleep apnea (OSA), there is a need for simplified diagnostic approaches. A prospective study in 140 children undergoing in-laboratory polysomnography (PSG) evaluates the accuracy of a recently developed system (Sunrise) to estimate respiratory efforts by monitoring sleep mandibular movements (MM) for the diagnosis of OSA (Sunrise™). METHODS Diagnosis and severity were defined by an obstructive apnea/hypopnea index (OAHI) ≥ 1 (mild), ≥ 5 (moderate), and ≥ 10 events/h (severe). Agreement between PSG and Sunrise™ was assessed by Bland-Altman method comparing respiratory disturbances hourly index (RDI) (obstructive apneas, hypopneas, and respiratory effort-related arousals) during PSG (PSG_RDI), and Sunrise RDI (Sr_RDI). Performance of Sr_RDI was determined via ROC curves evaluating the device sensitivity and specificity at PSG_OAHI ≥ 1, 5, and 15 events/h. RESULTS A median difference of 1.57 events/h, 95% confidence interval: -2.49 to 8.11 was found between Sr_RDI and PSG_RDI. Areas under the ROC curves of Sr_RDI were 0.75 (interquartile range [IQR]: 0.72-0.78), 0.90 (IQR: 0.86-0.92) and 0.95 (IQR: 0.90-0.99) for detecting children with PSG_OAHI ≥ 1, PSG_OAHI ≥ 5, or PSG_ OAHI ≥ 10, respectively. CONCLUSION MM automated analysis shows significant promise to diagnose moderate-to-severe pediatric OSA.
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Affiliation(s)
- Jean-Benoit Martinot
- Sleep Laboratory, CHU UCL Namur Site Sainte-Elisabeth, Belgium
- Institute of Experimental and Clinical Research, UCL, Bruxelles Woluwe, Belgium
| | | | | | | | | | | | - Jean L Pépin
- Inserm, CHU Grenoble Alpes, HP2, Université Grenoble Alpes, Grenoble, France
| | - David Gozal
- Department of Child Health, University of Missouri, Columbia, Missouri, USA
- Child Health Research Institute, University of Missouri, Columbia, Missouri, USA
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13
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Sutherland K, Sadr N, Bin YS, Cook K, Dissanayake HU, Cistulli PA, de Chazal P. Comparative associations of oximetry patterns in Obstructive Sleep Apnea with incident cardiovascular disease. Sleep 2022; 45:6650848. [PMID: 35896039 PMCID: PMC9742894 DOI: 10.1093/sleep/zsac179] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 06/27/2022] [Indexed: 12/15/2022] Open
Abstract
STUDY OBJECTIVES Intermittent hypoxia is a key mechanism linking Obstructive Sleep Apnea (OSA) to cardiovascular disease (CVD). Oximetry analysis could enhance understanding of which OSA phenotypes are associated with CVD risk. The aim of this study was to compare associations of different oximetry patterns with incident CVD in men and women with OSA. METHODS Sleep Heart Health Study data were used for analysis. n = 2878 Participants (51.8% female; mean age 63.5 ± 10.5 years) with OSA (Apnea Hypopnea Index [AHI] ≥ 5 events/h) and no pre-existing CVD at baseline or within the first 2 years of follow-up were included. Four oximetry analysis approaches were applied: desaturation characteristics, time series analysis, power spectral density, and non-linear analysis. Thirty-one resulting oximetry patterns were compared to incident CVD using proportional hazards regression models adjusted for age, race, smoking, BMI, and sex. RESULTS There were no associations between OSA oximetry patterns and incident CVD in the total sample or in men. In women, there were some associations between incident CVD and time series analysis (e.g. SpO2 distribution standard deviation, HR 0.81, 95% CI 0.68-0.96, p = 0.014) and power spectral density oximetry patterns (e.g. Full frequency band mean HR 0.75; 95% CI 0.59-0.95; p = 0.015). CONCLUSIONS Comprehensive comparison of baseline oximetry patterns in OSA found none were related to development of CVD. There were no standout individual oximetry patterns that appear to be candidates for CVD risk phenotyping in OSA, but some showed marginal relationships with CVD risk in women. Further work is required to understand whether OSA phenotypes can be used to predict susceptibility to cardiovascular disease.
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Affiliation(s)
- Kate Sutherland
- Sleep Research Group, Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia,Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Nadi Sadr
- Sleep Research Group, Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia,Department of Biomedical Informatics, Emory University, Atlanta, Georgia, USA
| | - Yu Sun Bin
- Sleep Research Group, Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia,Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Kristina Cook
- Sleep Research Group, Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia,Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Hasthi U Dissanayake
- Sleep Research Group, Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia,Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Peter A Cistulli
- Sleep Research Group, Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia,Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia,Department of Respiratory and Sleep Medicine, Royal North Shore Hospital, St Leonards, New South Wales, Australia
| | - Philip de Chazal
- Corresponding author. Philip de Chazal, Sleep Research Group, Level 3, D17 Charles Perkins Centre, University of Sydney Camperdown, New South Wales 2006, Australia.
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14
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Detection of pediatric obstructive sleep apnea using a multilayer perception model based on single-channel oxygen saturation or clinical features. Methods 2022; 204:361-367. [DOI: 10.1016/j.ymeth.2022.04.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/14/2022] [Accepted: 04/29/2022] [Indexed: 11/22/2022] Open
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15
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Abstract
Pediatric obstructive sleep apnea (OSA) is a common entity that can cause both daytime and nighttime issues. Children with symptoms should be screened for OSA. If possible, polysomnography should be performed to evaluate symptomatic children. Depending on the severity, first-line options for treatment of pediatric OSA may include observation, weight loss, medication, or surgery. Even after adenotonsillectomy, about 20% of children will have persistent OSA. Sleep endoscopy and cine MRI are tools that may be used to identify sites of obstruction, which in turn can help in the selection of site-specific treatment.
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Affiliation(s)
- Pakkay Ngai
- Division of Pediatric Pulmonology, Joseph M. Sanzari Children's Hospital, Hackensack Meridian Children's Health, 30 Prospect Avenue, WFAN 3rd Floor, Hackensack, NJ 07601, USA
| | - Michael Chee
- Division of Pediatric Otolaryngology, Joseph M. Sanzari Children's Hospital, Hackensack Meridian Children's Health, 30 Prospect Avenue, WFAN PC-311, Hackensack, NJ 07601, USA.
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16
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Gutiérrez-Tobal GC, Álvarez D, Vaquerizo-Villar F, Barroso-García V, Gómez-Pilar J, Del Campo F, Hornero R. Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:131-146. [PMID: 36217082 DOI: 10.1007/978-3-031-06413-5_8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
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.
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Affiliation(s)
- Gonzalo C Gutiérrez-Tobal
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain.
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.
| | - Daniel Álvarez
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Fernando Vaquerizo-Villar
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Verónica Barroso-García
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Javier Gómez-Pilar
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Félix Del Campo
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- Sleep Unit, Pneumology Service, Hospital Universitario Rio Hortega, Valladolid, Spain
| | - Roberto Hornero
- Centro de Investigación Biomédica en Red, Bioingeniería, Biomateriales, Nanomedicina, Madrid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
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17
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Damian A, Gozal D. Pediatric Obstructive Sleep Apnea: What’s in a Name? ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:63-78. [PMID: 36217079 DOI: 10.1007/978-3-031-06413-5_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Obstructive sleep apnea is a highly prevalent disease across the lifespan and imposes substantial morbidities, some of which may become irreversible if the condition is not diagnosed and treated in a timely fashion. Here, we focus on the clinical and epidemiological characteristics of pediatric obstructive sleep apnea, describe some of the elements that by virtue of their presence facilitate the emergence of disrupted sleep and breathing and its downstream consequences, and also discuss the potential approaches to diagnosis in at-risk children.
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Affiliation(s)
- Allan Damian
- Departments of Neurology, University of Missouri School of Medicine, Columbia, MO, USA
- Comprehensive Sleep Medicine Program, University of Missouri School of Medicine, Columbia, MO, USA
| | - David Gozal
- Comprehensive Sleep Medicine Program, University of Missouri School of Medicine, Columbia, MO, USA.
- Department of Child Health and the Child Health Research Institute, University of Missouri School of Medicine, Columbia, MO, USA.
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18
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Mlauzi R, McGuire J, Zampoli M, Takuva S, Lawrenson J, Singh Y, Peer S. Clinical correlations to distinguish severe from milder forms of obstructive sleep apnoea syndrome using overnight oximetry for prioritising adenotonsillectomy in a limited-resource setting. Int J Pediatr Otorhinolaryngol 2022; 152:110988. [PMID: 34871949 DOI: 10.1016/j.ijporl.2021.110988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 05/14/2021] [Accepted: 11/21/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND In resource-poor settings with limited surgical services, it is essential to identify and prioritise children with severe and very severe obstructive sleep apnoea syndrome (OSAS) to expedite surgery. McGill's Oximetry Score (MOS) has been validated against polysomnography for OSAS and is affordable and easy to use. AIMS The aim of this study was to assess the correlation of tonsillar size and clinical symptoms with MOS grade 3 or 4, to identify who requires overnight oximetry and who to prioritise for adenotonsillectomy. METHODS Children with suspected OSAS were recruited from the otolaryngology clinic at the Red Cross War Memorial Children's Hospital. Demographics, symptom screening scores (SSS), patient characteristics, overnight oximetry (OO), echocardiography and MOS scores (graded 1-4) were recorded. Multivariate modified-Poisson regression models were used to examine correlations of patient characteristics 'with grade 3 or 4 MOS. RESULTS One-hundred-and-three children were analysed, 38% were female, and median (IQR) age was 3.8 (2.5-5.3) years. Increased tonsil size was associated with a 60% increased risk of grade 3 or 4 MOS, risk ratio (RR) 1.59, 95% CI 1.10-2.29 (p = 0.014). Children with witnessed apnoeic events during sleep had 1.3 times increased risk of MOS Grade 3 or 4, RR 1.31, 95% CI (p = 0.033). A significant correlation was shown with grade 3 or 4 MOS, RR 1.15, 95% CI 1.03-1.27 (p = 0.010) by combining tonsillar size with the following symptoms: apnoeic events; struggling to breathe during sleep and needing to stimulate the child to breathe. CONCLUSION Identifying children with suspected OSAS who require overnight oximetry can be performed using a simple 3-question screening tool: witnessed apnoeic events, struggling to breathe and the need to shake them awake to breathe. This is more precise with an additional clinical finding of grade 3 or 4 tonsils. These children should have surgery expedited. Any child with a MOS 3 or 4 score on OO needs to have expedited surgery.
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Affiliation(s)
- Raphael Mlauzi
- Division of Otorhinolaryngology-Head and Neck Surgery, University of Cape Town, Cape Town, South Africa; Red Cross War Memorial Children's Hospital, Cape Town, South Africa.
| | - Jessica McGuire
- Division of Otorhinolaryngology-Head and Neck Surgery, University of Cape Town, Cape Town, South Africa; Red Cross War Memorial Children's Hospital, Cape Town, South Africa
| | - Marco Zampoli
- Red Cross War Memorial Children's Hospital, Cape Town, South Africa; Department of Paediatrics and Child Health, Division of Paediatric Pulmonology, University of Cape Town, Cape Town, South Africa
| | - Simbarashe Takuva
- Perinatal HIV Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - John Lawrenson
- Department of Paediatrics and Child Health, Division of Paediatric Cardiology, University of Cape Town, Cape Town, South Africa
| | - Yanita Singh
- Department of Paediatrics and Child Health, Division of Paediatric Cardiology, University of Cape Town, Cape Town, South Africa
| | - Shazia Peer
- Division of Otorhinolaryngology-Head and Neck Surgery, University of Cape Town, Cape Town, South Africa; Red Cross War Memorial Children's Hospital, Cape Town, South Africa
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19
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Zhao R, Xue J, Zhang X, Peng M, Li J, Zhou B, Zhao L, Penzel T, Kryger M, Dong XS, Gao Z, Han F. Comparison of Ring Pulse Oximetry Using Reflective Photoplethysmography and PSG in the Detection of OSA in Chinese Adults: A Pilot Study. Nat Sci Sleep 2022; 14:1427-1436. [PMID: 36003191 PMCID: PMC9394522 DOI: 10.2147/nss.s367400] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/27/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE A novel ring-worn oximeter (Circul) uses reflective photoplethysmography and automated signal processing to calculate oxygen desaturations. We evaluated the ability of Circul to detect obstructive sleep apnea in Chinese adults. METHODS We recruited 207 Chinese Han subjects: 70% males, mean age 48.2±14.7 years, mean BMI 27.6±4.8 kg/m2 and mean AHI 28.6±25.2 events/h. All participants underwent simultaneous polysomnography (PSG) and Circul testing in a sleep laboratory. Oxygen desaturation index (ODI), mean oxygen saturation (MSpO2), cumulative time at SpO2<90% (CT90), cumulative percentage of sleep time spent with SpO2<90% (CT90/TST) were derived and compared for the Circul and the PSG. RESULTS The ODI was 25.3±24.5 events/h using PSG and 22.2±24.5 events/h using Circul (P<0.0001), with an intraclass correlation coefficient (ICC) of 0.884. CT90 and CT90/TST between the two methods were not different; the MSpO2 level calculated by PSG was slightly lower than Circul, 95.0% (93.0-96.0%) vs 95.3% (93.9-96.6%), P<0.0001. Circul-ODI had a good correlation (r=0.91, p<0.0001) and close agreement with PSG-AHI (Bland-Altman analysis: Mean Difference 6.4, 95% CI -14.8 to 27.5 events/h). Using a threshold of AHI ≥5 events/h, the Circul had 87% sensitivity, 83% specificity, 5.09 positive likelihood ratio (LR+), 86% accuracy, and 0.929 area under the curve (AUC). CONCLUSION Circul ring pulse oximetry can detect OSA with reasonable reliability. The Circul system is a reliable and comfortable choice for OSA assessment.
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Affiliation(s)
- Rui Zhao
- Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China
| | - Jianbo Xue
- Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China
| | - Xueli Zhang
- Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China
| | - Maohuan Peng
- Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China
| | - Jing Li
- Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China
| | - Bing Zhou
- Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China
| | - Long Zhao
- Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China
| | - Thomas Penzel
- Sleep Medicine Center, Charité-Universitätsmedizin, Berlin, Germany
| | - Meir Kryger
- Division of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Xiao Song Dong
- Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China
| | - Zhancheng Gao
- Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China
| | - Fang Han
- Department of Pulmonary and Critical Care Medicine, Peking University People's Hospital, Beijing, People's Republic of China
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20
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Álvarez D, Gutiérrez-Tobal GC, Vaquerizo-Villar F, Moreno F, Del Campo F, Hornero R. Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:219-239. [PMID: 36217087 DOI: 10.1007/978-3-031-06413-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
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.
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Affiliation(s)
- Daniel Álvarez
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain.
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Fernando Moreno
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Félix Del Campo
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
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Gozal D. Diagnostic approaches to respiratory abnormalities in craniofacial syndromes. Semin Fetal Neonatal Med 2021; 26:101292. [PMID: 34556443 DOI: 10.1016/j.siny.2021.101292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Craniofacial syndromes are a complex cluster of genetic conditions characterized by embryonic perturbations in the developmental trajectory of the upper airway and related structures. The presence of reduced airway size and maladaptive neuromuscular responses, particularly during sleep, leads to significant alterations in sleep architecture and overall detrimental gas exchange abnormalities that can be life-threatening. The common need for multi-stage therapeutic interventions for these craniofacial problems requires careful titration of anatomy and function, and the latter is currently evaluated by overnight polysomnography in sleep laboratories. The cost, inconvenience, and scarcity of pediatric sleep laboratories preclude the frequent evaluations that could optimize the overall process of treatment and corresponding outcomes. Here, we critically examine reductionist approaches to polysomnography in children to establish the parallel approximation of such techniques to infant with craniofacial disorders. The need for prospective longitudinal multicenter studies with side-by-side comparisons aimed at identifying an optimal diagnostic and long-term monitoring paradigm for these potentially life-threatening conditions is emphasized.
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Affiliation(s)
- David Gozal
- Department of Child Health, University of Missouri, Columbia, MO, USA.
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22
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Gutiérrez-Tobal GC, Álvarez D, Vaquerizo-Villar F, Crespo A, Kheirandish-Gozal L, Gozal D, del Campo F, Hornero R. Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107827] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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23
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Gao X, Li Y, Xu W, Han D. Diagnostic accuracy of level IV portable sleep monitors versus polysomnography for pediatric obstructive sleep apnea: a systematic review and meta-analysis. Sleep Med 2021; 87:127-137. [PMID: 34597954 DOI: 10.1016/j.sleep.2021.08.029] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 08/21/2021] [Accepted: 08/26/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Obstructive sleep apnea (OSA) is one of the common sleep-related breathing disorders in children. However, polysomnography (PSG) is an expensive and labor-intensive diagnostic modality that may not always be feasible, especially in low-income countries or in non-tertiary hospitals. Portable monitors (PMs), a new approach for OSA diagnosis, have become more widely used with lower intolerance and cost in recent years. We aimed to analyze the diagnostic performance of Level IV PMs compared with PSG for the diagnosis of pediatric OSA. METHODS PubMed and Embase databases were searched for studies published in English up to December 31, 2020 evaluating the diagnostic accuracy of Level IV PMs against the apnea-hypopnea index (AHI) measured using overnight in-laboratory polysomnography (PSG) in children and adolescents. A random-effects bivariate model was used to estimate the summary sensitivity and specificity of oximetry-based statistical classifiers. A qualitative evaluation of studies was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) rating. RESULTS In total, 20 studies involving 7062 participants were included in this systematic review. Among these articles, seven studies (oximetry based on new mathematical classifiers) involving 5098 individuals satisfied the criteria for quantitative synthesis. Compared with AHI evaluation measured by PSG, different PM systems achieved diagnostic accuracy with variable degrees of success. A meta-analysis showed a pooled sensitivity of 74% (95% confidence interval [CI]: 66-80%) and pooled specificity of 90% (95% CI: 85-94%). The area under the summary receiver operating characteristic (SROC) curve was 0.89 (95% CI: 0.86-0.92). CONCLUSION This study showed the potential of Level IV PMs for screening pediatric OSA patients. Oximetry based on new mathematical classifiers may provide a simple and effective alternative to PSG in the diagnosis of pediatric OSA especially in the context of appropriate clinical evaluation.
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Affiliation(s)
- Xiang Gao
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Yanru Li
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Wen Xu
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Demin Han
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China.
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24
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Gozal D, Ismail M, Brockmann PE. Alternatives to surgery in children with mild OSA. World J Otorhinolaryngol Head Neck Surg 2021; 7:228-235. [PMID: 34430830 PMCID: PMC8356096 DOI: 10.1016/j.wjorl.2021.03.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 02/17/2021] [Accepted: 03/18/2021] [Indexed: 12/01/2022] Open
Abstract
Precision medicine requires coordinated and integrated evidence-based combinatorial approaches so that diagnosis and treatment can be tailored to the individual patient. In this context, the treatment approach to mild obstructive sleep apnea (OSA) is fraught with substantial debate as to what is mild OSA, and as to what constitutes appropriate treatment. As such, it is necessary to first establish a proposed consensus of what criteria need to be employed to reach the diagnosis of mild OSA, and then examine the circumstances under which treatment is indicated, and if so, whether and when anti-inflammatory therapy (AIT), rapid maxillary expansion (RME), and/or myofunctional therapy (MFT) may be indicated.
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Affiliation(s)
- David Gozal
- Department of Child Health and Child Health Research Institute, and MU Women and Children's Hospital, University of Missouri School of Medicine, Columbia, MO, USA
| | - Mahmoud Ismail
- Department of Neurology and Sleep Medicine, University of Missouri School of Medicine, Columbia, MO, USA
| | - Pablo E Brockmann
- Department of Pediatric Cardiology and Pulmonology, Division of Pediatrics, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.,Pediatric Sleep Center, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
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25
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Martín-Montero A, Gutiérrez-Tobal GC, Gozal D, Barroso-García V, Álvarez D, del Campo F, Kheirandish-Gozal L, Hornero R. Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1016. [PMID: 34441156 PMCID: PMC8394544 DOI: 10.3390/e23081016] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 07/28/2021] [Accepted: 08/03/2021] [Indexed: 12/28/2022]
Abstract
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.
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Affiliation(s)
- Adrián Martín-Montero
- Biomedical Engineering Group, University of Valladolid, 47002 Valladolid, Spain; (G.C.G.-T.); (V.B.-G.); (D.Á.); (F.d.C.); (R.H.)
| | - Gonzalo C. Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, 47002 Valladolid, Spain; (G.C.G.-T.); (V.B.-G.); (D.Á.); (F.d.C.); (R.H.)
- CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, 28029 Madrid, Spain
| | - David Gozal
- Department of Child Health and the Child Health Research Institute, The University of Missouri School of Medicine, Columbia, MO 65212, USA; (D.G.); (L.K.-G.)
| | - Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, 47002 Valladolid, Spain; (G.C.G.-T.); (V.B.-G.); (D.Á.); (F.d.C.); (R.H.)
- CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, 28029 Madrid, Spain
| | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, 47002 Valladolid, Spain; (G.C.G.-T.); (V.B.-G.); (D.Á.); (F.d.C.); (R.H.)
- CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, 28029 Madrid, Spain
| | - Félix del Campo
- Biomedical Engineering Group, University of Valladolid, 47002 Valladolid, Spain; (G.C.G.-T.); (V.B.-G.); (D.Á.); (F.d.C.); (R.H.)
- CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, 28029 Madrid, Spain
- Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, 47012 Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health and the Child Health Research Institute, The University of Missouri School of Medicine, Columbia, MO 65212, USA; (D.G.); (L.K.-G.)
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, 47002 Valladolid, Spain; (G.C.G.-T.); (V.B.-G.); (D.Á.); (F.d.C.); (R.H.)
- CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, 28029 Madrid, Spain
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Vaquerizo-Villar F, Alvarez D, Kheirandish-Gozal L, Gutierrez-Tobal GC, Barroso-Garcia V, Santamaria-Vazquez E, Campo FD, Gozal D, Hornero R. A Convolutional Neural Network Architecture to Enhance Oximetry Ability to Diagnose Pediatric Obstructive Sleep Apnea. IEEE J Biomed Health Inform 2021; 25:2906-2916. [PMID: 33406046 PMCID: PMC8460136 DOI: 10.1109/jbhi.2020.3048901] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This study aims at assessing the usefulness of deep learning to enhance the diagnostic ability of oximetry in the context of automated detection of pediatric obstructive sleep apnea (OSA). A total of 3196 blood oxygen saturation (SpO2) signals from children were used for this purpose. A convolutional neural network (CNN) architecture was trained using 20-min SpO2 segments from the training set (859 subjects) to estimate the number of apneic events. CNN hyperparameters were tuned using Bayesian optimization in the validation set (1402 subjects). This model was applied to three test sets composed of 312, 392, and 231 subjects from three independent databases, in which the apnea-hypopnea index (AHI) estimated for each subject (AHICNN) was obtained by aggregating the output of the CNN for each 20-min SpO2 segment. AHICNN outperformed the 3% oxygen desaturation index (ODI3), a clinical approach, as well as the AHI estimated by a conventional feature-engineering approach based on multi-layer perceptron (AHIMLP). Specifically, AHICNN reached higher four-class Cohen's kappa in the three test databases than ODI3 (0.515 vs 0.417, 0.422 vs 0.372, and 0.423 vs 0.369) and AHIMLP (0.515 vs 0.377, 0.422 vs 0.381, and 0.423 vs 0.306). In addition, our proposal outperformed state-of-the-art studies, particularly for the AHI severity cutoffs of 5 e/h and 10 e/h. This suggests that the information automatically learned from the SpO2 signal by deep-learning techniques helps to enhance the diagnostic ability of oximetry in the context of pediatric OSA.
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Screening Severe Obstructive Sleep Apnea in Children with Snoring. Diagnostics (Basel) 2021; 11:diagnostics11071168. [PMID: 34206981 PMCID: PMC8304319 DOI: 10.3390/diagnostics11071168] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 06/18/2021] [Accepted: 06/22/2021] [Indexed: 12/11/2022] Open
Abstract
Efficient screening for severe obstructive sleep apnea (OSA) is important for children with snoring before time-consuming standard polysomnography. This retrospective cross-sectional study aimed to compare clinical variables, home snoring sound analysis, and home sleep pulse oximetry on their predictive performance in screening severe OSA among children who habitually snored. Study 1 included 9 (23%) girls and 30 (77%) boys (median age of 9 years). Using univariate logistic regression models, 3% oxygen desaturation index (ODI3) ≥ 6.0 events/h, adenoidal-nasopharyngeal ratio (ANR) ≥ 0.78, tonsil size = 4, and snoring sound energy of 801–1000 Hz ≥ 22.0 dB significantly predicted severe OSA in descending order of odds ratio. Multivariate analysis showed that ODI3 ≥ 6.0 events/h independently predicted severe pediatric OSA. Among several predictive models, the combination of ODI3, tonsil size, and ANR more optimally screened for severe OSA with a sensitivity of 91% and a specificity of 94%. In Study 2 (27 (27%) girls and 73 (73%) boys; median age, 7 years), this model was externally validated to predict severe OSA with an accuracy of 76%. Our results suggested that home sleep pulse oximetry, combined with ANR, can screen for severe OSA more optimally than ANR and tonsil size among children with snoring.
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28
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Martín-Montero A, Gutiérrez-Tobal GC, Kheirandish-Gozal L, Jiménez-García J, Álvarez D, del Campo F, Gozal D, Hornero R. Heart rate variability spectrum characteristics in children with sleep apnea. Pediatr Res 2021; 89:1771-1779. [PMID: 32927472 PMCID: PMC7956022 DOI: 10.1038/s41390-020-01138-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 08/04/2020] [Accepted: 08/10/2020] [Indexed: 01/04/2023]
Abstract
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.
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Affiliation(s)
| | - Gonzalo C. Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health and The Child Health Research Institute, The University of Missouri School of Medicine, Columbia, Missouri
| | | | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain.,Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Félix del Campo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain.,Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - David Gozal
- Department of Child Health and The Child Health Research Institute, The University of Missouri School of Medicine, Columbia, Missouri
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
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29
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Barroso-García V, Gutiérrez-Tobal GC, Gozal D, Vaquerizo-Villar F, Álvarez D, del Campo F, Kheirandish-Gozal L, Hornero R. Wavelet Analysis of Overnight Airflow to Detect Obstructive Sleep Apnea in Children. SENSORS 2021; 21:s21041491. [PMID: 33669996 PMCID: PMC7926995 DOI: 10.3390/s21041491] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/16/2021] [Accepted: 02/18/2021] [Indexed: 01/08/2023]
Abstract
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.
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Affiliation(s)
- Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - Gonzalo C. Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
- Correspondence: ; Tel.: +34-983-423000 (ext. 4713)
| | - David Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO 65212, USA; (D.G.); (L.K.-G.)
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
- Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain
| | - Félix del Campo
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
- Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO 65212, USA; (D.G.); (L.K.-G.)
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (V.B.-G.); (F.V.-V.); (D.Á.); (F.d.C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
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Oceja E, Rodríguez P, Jurado MJ, Luz Alonso M, del Río G, Villar MÁ, Mediano O, Martínez M, Juarros S, Merino M, Corral J, Luna C, Kheirandish-Gozal L, Gozal D, Durán-Cantolla J. Validity and Cost-Effectiveness of Pediatric Home Respiratory Polygraphy for the Diagnosis of Obstructive Sleep Apnea in Children: Rationale, Study Design, and Methodology. Methods Protoc 2021; 4:9. [PMID: 33477929 PMCID: PMC7838960 DOI: 10.3390/mps4010009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 01/14/2021] [Accepted: 01/15/2021] [Indexed: 12/15/2022] Open
Abstract
Obstructive sleep apnea (OSA) in children is a prevalent, albeit largely undiagnosed disease associated with a large spectrum of morbidities. Overnight in-lab polysomnography remains the gold standard diagnostic approach, but is time-consuming, inconvenient, and expensive, and not readily available in many places. Simplified Home Respiratory Polygraphy (HRP) approaches have been proposed to reduce costs and facilitate the diagnostic process. However, evidence supporting the validity of HRP is still scarce, hampering its implementation in routine clinical use. The objectives were: Primary; to establish the diagnostic and therapeutic decision validity of a simplified HRP approach compared to PSG among children at risk of OSA. Secondary: (a) Analyze the cost-effectiveness of the HRP versus in-lab PSG in evaluation and treatment of pediatric OSA; (b) Evaluate the impact of therapeutic interventions based on HRP versus PSG findings six months after treatment using sleep and health parameters and quality of life instruments; (c) Discovery and validity of the urine biomarkers to establish the diagnosis of OSA and changes after treatment.
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Affiliation(s)
- Esther Oceja
- Domiciliary Hospitalization, Sleep Unit, OSI Araba University Hospital, 01004 Vitoria, Spain;
| | - Paula Rodríguez
- Research Service and Bioaraba Research Institute, OSI Araba University Hospital, UPV/EHU, 01004 Vitoria, Spain;
| | - María José Jurado
- Sleep Unit, Hospital Universitario Valle de Hebrón, 08035 Barcelona, Spain;
| | - Maria Luz Alonso
- Sleep Unit, Complejo Hospitalario de Burgos, 09006 Burgos, Spain
| | | | | | - Olga Mediano
- Sleep Unit, Hospital de Guadalajara, 19002 Guadalajara, Spain;
| | - Marian Martínez
- Sleep Unit, Hospital Universitario Marqués de Valdecilla, 39008 Santander, Spain;
| | - Santiago Juarros
- Sleep Unit, Hospital Universitario de Valladolid, 47012 Valladolid, Spain;
| | - Milagros Merino
- Sleep Unit, Hospital Universitario La Paz, 28046 Madrid, Spain;
| | - Jaime Corral
- Sleep Unit, Complejo Hospitalario de Cáceres, 100003 Cáceres, Spain;
| | - Carmen Luna
- Sleep Unit, Hospital Universitario 12 de Octubre, 280035 Madrid, Spain;
| | - Leila Kheirandish-Gozal
- Department of Child Health and Child Health Research Institute, School of Medicine, University of Missouri, Columbia, MO 65201, USA; (L.K.-G.); (D.G.)
| | - David Gozal
- Department of Child Health and Child Health Research Institute, School of Medicine, University of Missouri, Columbia, MO 65201, USA; (L.K.-G.); (D.G.)
| | - Joaquín Durán-Cantolla
- Research Service and Bioaraba Research Institute, OSI Araba University Hospital, UPV/EHU, 01004 Vitoria, Spain;
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31
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Leino A, Nikkonen S, Kainulainen S, Korkalainen H, Töyräs J, Myllymaa S, Leppänen T, Ylä-Herttuala S, Westeren-Punnonen S, Muraja-Murro A, Jäkälä P, Mervaala E, Myllymaa K. Neural network analysis of nocturnal SpO 2 signal enables easy screening of sleep apnea in patients with acute cerebrovascular disease. Sleep Med 2020; 79:71-78. [PMID: 33482455 DOI: 10.1016/j.sleep.2020.12.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 12/16/2020] [Accepted: 12/28/2020] [Indexed: 10/22/2022]
Abstract
Current diagnostics of sleep apnea relies on the time-consuming manual analysis of complex sleep registrations, which is impractical for routine screening in hospitalized patients with a high probability for sleep apnea, e.g. those experiencing acute stroke or transient ischemic attacks (TIA). To overcome this shortcoming, we aimed to develop a convolutional neural network (CNN) capable of estimating the severity of sleep apnea in acute stroke and TIA patients based solely on the nocturnal oxygen saturation (SpO2) signal. The CNN was trained with SpO2 signals derived from 1379 home sleep apnea tests (HSAT) of suspected sleep apnea patients and tested with SpO2 signals of 77 acute ischemic stroke or TIA patients. The CNN's performance was tested by comparing the estimated respiratory event index (REI) and oxygen desaturation index (ODI) with manually obtained values. Median estimation errors for REI and ODI in patients with stroke or TIA were 1.45 events/hour and 0.61 events/hour, respectively. Furthermore, based on estimated REI and ODI, 77.9% and 88.3% of these patients were classified into the correct sleep apnea severity categories. The sensitivity and specificity to identify sleep apnea (REI > 5 events/hour) were 91.8% and 78.6%, respectively. Moderate-to-severe sleep apnea was detected (REI > 15 events/hour) with sensitivity of 92.3% and specificity of 96.1%. The CNN analysis of the SpO2 signal has great potential as a simple screening tool for sleep apnea. This novel automatic method accurately detects sleep apnea in acute cerebrovascular disease patients and facilitates their referral for a differential diagnostic HSAT or polysomnography evaluation.
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Affiliation(s)
- Akseli Leino
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - Sami Nikkonen
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Samu Kainulainen
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Henri Korkalainen
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Juha Töyräs
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Sami Myllymaa
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Timo Leppänen
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Salla Ylä-Herttuala
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Susanna Westeren-Punnonen
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Anu Muraja-Murro
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Pekka Jäkälä
- Department of Neurology, NeuroCenter, Kuopio University Hospital, Kuopio, Finland; Department of Neurology, University of Eastern Finland, Kuopio, Finland
| | - Esa Mervaala
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Department of Clinical Neurophysiology, University of Eastern Finland, Kuopio, Finland
| | - Katja Myllymaa
- Department of Clinical Neurophysiology, Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
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Barroso-García V, Gutiérrez-Tobal GC, Kheirandish-Gozal L, Vaquerizo-Villar F, Álvarez D, Del Campo F, Gozal D, Hornero R. Bispectral analysis of overnight airflow to improve the pediatric sleep apnea diagnosis. Comput Biol Med 2020; 129:104167. [PMID: 33385706 DOI: 10.1016/j.compbiomed.2020.104167] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 11/19/2020] [Accepted: 12/04/2020] [Indexed: 12/15/2022]
Abstract
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.
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Affiliation(s)
- Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain.
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
| | - Leila Kheirandish-Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
| | - Daniel Álvarez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Félix Del Campo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain; Sleep-Ventilation Unit, Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - David Gozal
- Department of Child Health, The University of Missouri School of Medicine, Columbia, MO, USA
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, Valladolid, Spain
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Calderón JM, Álvarez-Pitti J, Cuenca I, Ponce F, Redon P. Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome. Bioengineering (Basel) 2020; 7:E131. [PMID: 33086521 PMCID: PMC7712243 DOI: 10.3390/bioengineering7040131] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/14/2020] [Accepted: 10/17/2020] [Indexed: 01/20/2023] Open
Abstract
Obstructive sleep apnea syndrome is a reduction of the airflow during sleep which not only produces a reduction in sleep quality but also has major health consequences. The prevalence in the obese pediatric population can surpass 50%, and polysomnography is the current gold standard method for its diagnosis. Unfortunately, it is expensive, disturbing and time-consuming for experienced professionals. The objective is to develop a patient-friendly screening tool for the obese pediatric population to identify those children at higher risk of suffering from this syndrome. Three supervised learning classifier algorithms (i.e., logistic regression, support vector machine and AdaBoost) common in the field of machine learning were trained and tested on two very different datasets where oxygen saturation raw signal was recorded. The first dataset was the Childhood Adenotonsillectomy Trial (CHAT) consisting of 453 individuals, with ages between 5 and 9 years old and one-third of the patients being obese. Cross-validation was performed on the second dataset from an obesity assessment consult at the Pediatric Department of the Hospital General Universitario of Valencia. A total of 27 patients were recruited between 5 and 17 years old; 42% were girls and 63% were obese. The performance of each algorithm was evaluated based on key performance indicators (e.g., area under the curve, accuracy, recall, specificity and positive predicted value). The logistic regression algorithm outperformed (accuracy = 0.79, specificity = 0.96, area under the curve = 0.9, recall = 0.62 and positive predictive value = 0.94) the support vector machine and the AdaBoost algorithm when trained with the CHAT datasets. Cross-validation tests, using the Hospital General de Valencia (HG) dataset, confirmed the higher performance of the logistic regression algorithm in comparison with the others. In addition, only a minor loss of performance (accuracy = 0.75, specificity = 0.88, area under the curve = 0.85, recall = 0.62 and positive predictive value = 0.83) was observed despite the differences between the datasets. The proposed minimally invasive screening tool has shown promising performance when it comes to identifying children at risk of suffering obstructive sleep apnea syndrome. Moreover, it is ideal to be implemented in an outpatient consult in primary and secondary care.
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Affiliation(s)
- José Miguel Calderón
- Fundación Investigación Hospital Clínico (INCLIVA), Avda. Menedez Pelayo 4, 46010 Valencia, Spain; (J.M.C.); (I.C.)
| | - Julio Álvarez-Pitti
- Pediatric Department, Consorcio Hospital General Universitario de Valencia, Avda. Tres Cruces s/n, 46014 Valencia, Spain; (J.Á.-P.); (F.P.)
| | - Irene Cuenca
- Fundación Investigación Hospital Clínico (INCLIVA), Avda. Menedez Pelayo 4, 46010 Valencia, Spain; (J.M.C.); (I.C.)
| | - Francisco Ponce
- Pediatric Department, Consorcio Hospital General Universitario de Valencia, Avda. Tres Cruces s/n, 46014 Valencia, Spain; (J.Á.-P.); (F.P.)
- CIBEROBN, Health Institute Carlos III, Av. Monforte de Lemos, 3-5. Pavilion 11, 28029 Madrid, Spain
| | - Pau Redon
- Pediatric Department, Consorcio Hospital General Universitario de Valencia, Avda. Tres Cruces s/n, 46014 Valencia, Spain; (J.Á.-P.); (F.P.)
- CIBEROBN, Health Institute Carlos III, Av. Monforte de Lemos, 3-5. Pavilion 11, 28029 Madrid, Spain
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Hunter RB, Jiang S, Nishisaki A, Nickel AJ, Napolitano N, Shinozaki K, Li T, Saeki K, Becker LB, Nadkarni VM, Masino AJ. Supervised Machine Learning Applied to Automate Flash and Prolonged Capillary Refill Detection by Pulse Oximetry. Front Physiol 2020; 11:564589. [PMID: 33117190 PMCID: PMC7574820 DOI: 10.3389/fphys.2020.564589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 09/01/2020] [Indexed: 11/29/2022] Open
Abstract
Objective Develop an automated approach to detect flash (<1.0 s) or prolonged (>2.0 s) capillary refill time (CRT) that correlates with clinician judgment by applying several supervised machine learning (ML) techniques to pulse oximeter plethysmography data. Materials and Methods Data was collected in the Pediatric Intensive Care Unit (ICU), Cardiac ICU, Progressive Care Unit, and Operating Suites in a large academic children’s hospital. Ninety-nine children and 30 adults were enrolled in testing and validation cohorts, respectively. Patients had 5 paired CRT measurements by a modified pulse oximeter device and a clinician, generating 485 waveform pairs for model training. Supervised ML models using gradient boosting (XGBoost), logistic regression (LR), and support vector machines (SVMs) were developed to detect flash (<1 s) or prolonged CRT (≥2 s) using clinician CRT assessment as the reference standard. Models were compared using Area Under the Receiver Operating Curve (AUC) and precision-recall curve (positive predictive value vs. sensitivity) analysis. The best performing model was externally validated with 90 measurement pairs from adult patients. Feature importance analysis was performed to identify key waveform characteristics. Results For flash CRT, XGBoost had a greater mean AUC (0.79, 95% CI 0.75–0.83) than logistic regression (0.77, 0.71–0.82) and SVM (0.72, 0.67–0.76) models. For prolonged CRT, XGBoost had a greater mean AUC (0.77, 0.72–0.82) than logistic regression (0.73, 0.68–0.78) and SVM (0.75, 0.70–0.79) models. Pairwise testing showed statistically significant improved performance comparing XGBoost and SVM; all other pairwise model comparisons did not reach statistical significance. XGBoost showed good external validation with AUC of 0.88. Feature importance analysis of XGBoost identified distinct key waveform characteristics for flash and prolonged CRT, respectively. Conclusion Novel application of supervised ML to pulse oximeter waveforms yielded multiple effective models to identify flash and prolonged CRT, using clinician judgment as the reference standard. Tweet Supervised machine learning applied to pulse oximeter waveform features predicts flash or prolonged capillary refill.
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Affiliation(s)
- Ryan Brandon Hunter
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Shen Jiang
- Nihon Kohden Innovation Center, Cambridge, MA, United States
| | - Akira Nishisaki
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Amanda J Nickel
- Department of Respiratory Therapy, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Natalie Napolitano
- Department of Respiratory Therapy, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Koichiro Shinozaki
- Department of Emergency Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Timmy Li
- Department of Emergency Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Kota Saeki
- Nihon Kohden Innovation Center, Cambridge, MA, United States
| | - Lance B Becker
- Department of Emergency Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Vinay M Nadkarni
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Aaron J Masino
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, United States
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Predicting polysomnographic severity thresholds in children using machine learning. Pediatr Res 2020; 88:404-411. [PMID: 32386396 DOI: 10.1038/s41390-020-0944-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 04/03/2020] [Accepted: 04/28/2020] [Indexed: 11/08/2022]
Abstract
BACKGROUND Approximately 500,000 children undergo tonsillectomy and adenoidectomy (T&A) annually for treatment of obstructive sleep disordered breathing (oSDB). Although polysomnography is beneficial for preoperative risk stratification in these children, its expanded use is limited by the associated costs and resources needed. Therefore, we used machine learning and data from potentially wearable sensors to identify children needing postoperative overnight monitoring based on the polysomnographic severity of oSDB. METHODS Children aged 2-17 years undergoing polysomnography were included. Six machine learning models were created using (i) clinical parameters and (ii) nocturnal actigraphy and oxygen desaturation index. The prediction performance for polysomnography-derived severity of oSDB measured by apnea hypopnea index (AHI) >2 and >10 were evaluated. RESULTS One hundred and ninety children were included. One hundred and eight were male (57%), mean age was 6.7 years [95% confidence interval; 6.1, 7.2], and mean AHI was 10.6 [7.8, 13.4]. Predictive performance utilizing clinical parameters was poor for both AHI > 2 (accuracy range: 48-56% for all models) and AHI > 10 (50-61%). Combining oximetry and actigraphy improved the accuracy to 87-89% for AHI > 2 and 95-96% for AHI > 10. CONCLUSIONS Machine learning with oximetry and actigraphy identifies most children needing overnight monitoring as determined by polysomnographic severity of oSDB, supporting a potential resource-conscious screening pathway for children undergoing T&A. IMPACT We provide proof of principle for the utility of machine learning, oximetry, and actigraphy to screen for severe obstructive sleep apnea syndrome (OSAS) in children. Clinical parameters perform poorly in predicting the severity of OSAS, which is confirmed in the current study. The predictive accuracy for severe OSAS was improved by a smaller subset of quantifiable physiologic parameters, such as oximetry. The results of this study support a lower cost, patient-friendly screening pathway to identify children in need of in-hospital observation after surgery.
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Afolabi-Brown O, Tapia IE. Pediatric pulmonology year in review 2019: Sleep medicine. Pediatr Pulmonol 2020; 55:1885-1891. [PMID: 32445539 DOI: 10.1002/ppul.24865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 05/17/2020] [Indexed: 11/07/2022]
Abstract
Pediatric Pulmonology publishes original research, review articles as well as case reports on a wide variety of pediatric respiratory disorders. In this article, we summarize the past year's publications in sleep medicine and we review selected literature from other journals within this field. Articles highlighted are topics on risk factors of sleep-disordered breathing, diagnosis, and treatment of obstructive sleep apnea as well as the utility of polysomnography in various complex conditions.
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Affiliation(s)
- Olufunke Afolabi-Brown
- Division of Pulmonary Medicine, Sleep Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Ignacio E Tapia
- Division of Pulmonary Medicine, Sleep Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
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Ehsan Z, He S, Huang G, Hossain MM, Simakajornboon N. Can overnight portable pulse oximetry be used to stratify obstructive sleep apnea risk in infants? A correlation analysis. Pediatr Pulmonol 2020; 55:2082-2088. [PMID: 32501635 DOI: 10.1002/ppul.24887] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 06/03/2020] [Indexed: 11/08/2022]
Abstract
INTRODUCTION There is limited evidence on the accuracy of oximetry in the evaluation of infant obstructive sleep apnea (OSA). We aimed to determine the utility of overnight oximetry to stratify infants at risk for OSA, to determine urgency for definitive screening with an overnight in-laboratory polysomnogram (PSG). METHODS Retrospective single-institution cohort study of infants undergoing PSG and a separate overnight oximetry over an 8-year period. Correlations, using oximetry in both in-hospital (attended) or at-home (unattended) settings, for ODI410 (decrease in oxygen saturation ≥ 4% from baseline, duration ≥ 10 seconds) and ODI40 (duration > 0 second) with the obstructive apnea-hypopnea index (AHIo) were obtained. The area under the curve was calculated, and sensitivity and specificity values have been presented as receiver operating characteristic curves. RESULTS Thirty-eight infants were included. The mean (SD) age (months) was 5.7 (3.9) at diagnostic PSG and 5.5 (3.7) at the time of oximetry. The mean AHIo for the entire cohort was 6.7 (6.2). The mean (SD) ODI40 was 8.6 (9.0) and the mean (SD) ODI410 was 5.4 (5.1).The correlation between ODI and AHIo was statistically significant for the cohort (ODI40 vs. AHIo [r = .59, P < .001] and ODI410 vs AHIo [r = .55, P = .0003]). Using an ODI40 cutoff of 3, the sensitivity, specificity, negative predictive value and positive predictive value for diagnosing OSA was: 86%, 40%, 50%, and 80% respectively for an AHIo greater than 2, and 100%, 35%, 100%, and 58% respectively for an AHIo greater than or equal to 5. CONCLUSION There is a significant positive correlation between the ODI4 obtained from oximetry and the AHIo obtained from PSG in infants at risk for OSA. An ODI40 greater than 3 may be useful to stratify infants at risk for moderate to severe OSA when used in attended (in-hospital) or unattended (in-home) settings.
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Affiliation(s)
- Zarmina Ehsan
- Division of Pulmonary and Sleep Medicine, Children's Mercy-Kansas City, Kansas City, Missouri.,School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri
| | - Shan He
- Department of Otolaryngology, Shanghai Children's Medical Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Guixia Huang
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Division of Pulmonary and Sleep Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Md M Hossain
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Division of Pulmonary and Sleep Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Narong Simakajornboon
- Division of Pulmonary and Sleep Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio
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Wu CR, Tu YK, Chuang LP, Gordon C, Chen NH, Chen PY, Hasan F, Kurniasari MD, Susanty S, Chiu HY. Diagnostic meta-analysis of the Pediatric Sleep Questionnaire, OSA-18, and pulse oximetry in detecting pediatric obstructive sleep apnea syndrome. Sleep Med Rev 2020; 54:101355. [PMID: 32750654 DOI: 10.1016/j.smrv.2020.101355] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 03/12/2020] [Accepted: 05/06/2020] [Indexed: 11/27/2022]
Abstract
The aim of this meta-analysis was to compare the pooled sensitivity and specificity of the Pediatric Sleep Questionnaire (PSQ), Obstructive Sleep Apnea Questionnaire (OSA-18), and pulse oximetry (PO) according to OSAS severity. Three electronic databases were searched for studies evaluating sensitivity and specificity of the three tools against the apnea-hypopnea index measured using overnight in-laboratory or in-home polysomnography in children and adolescents from inception until January 11, 2020. A random-effects bivariate model was used to estimate the summary sensitivity and specificity of the tools. We identified 39 studies involving 6131 clinical and community children (aged 2.9-16.7 y). The PSQ exhibited the highest sensitivity (74%) for detecting symptoms of mild pediatric OSAS. The PSQ and PO had comparable sensitivity in screening moderate and severe pediatric OSAS (0.82 and 0.89 vs 0.83 and 0.83, respectively). PO yielded superior specificity in detecting mild, moderate, and severe pediatric OSAS (86%, 75%, and 83%, respectively) than did the PSQ and OSA-18 (all p < 0.05). Age, percentage of girls, index test criteria, methodology quality, and sample size significantly moderated sensitivity and specificity. For early detection of pediatric OSAS, the combined use of PSQ and PO is recommended when polysomnography is not available. PROSPERO REGISTRATION NUMBER: CRD42018090571.
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Affiliation(s)
- Chia-Rung Wu
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Department of Nursing, Far Eastern Memorial Hospital, New Taipei, Taiwan
| | - Yu-Kang Tu
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Dentistry, National Taiwan University Hospital, Taipei, Taiwan
| | - Li-Pang Chuang
- School of Medicine, Chang Gung University, Taoyuan, Taiwan; Sleep Center, Department of Pulmonary and Critical Care Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Christopher Gordon
- Susan Wakil School of Nursing and Midwifery, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
| | - Ning-Hung Chen
- School of Medicine, Chang Gung University, Taoyuan, Taiwan; Sleep Center, Department of Pulmonary and Critical Care Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Pin-Yuan Chen
- School of Medicine, Chang Gung University, Taoyuan, Taiwan; Neurosurgical Department, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Faizul Hasan
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; School of Nursing, Politeknik Kesehatan Kemenkes Malang, Indonesia
| | - Maria D Kurniasari
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Faculty of Medicine and Health Science, Universitas Kristen Satya Wacana, Salatiga, Indonesia
| | - Sri Susanty
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Faculty of Medicine, Universitas Halu Oleo, Indonesia
| | - Hsiao-Yean Chiu
- School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Research Center of Sleep Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
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Vaquerizo-Villar F, Alvarez D, Kheirandish-Gozal L, Gutierrez-Tobal GC, Gomez-Pilar J, Crespo A, Del Campo F, Gozal D, Hornero R. Automatic Assessment of Pediatric Sleep Apnea Severity Using Overnight Oximetry and Convolutional Neural Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:633-636. [PMID: 33018067 DOI: 10.1109/embc44109.2020.9176342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this study, we use the overnight blood oxygen saturation (SpO2) signal along with convolutional neural networks (CNN) for the automatic estimation of pediatric sleep apnea-hypopnea syndrome (SAHS) severity. The few preceding studies have focused on the application of conventional feature extraction methods to obtain information from the SpO2 signal, which may omit relevant data related to the illness. In contrast, deep learning techniques are able to automatically learn features from raw input signal. Thus, we propose to assess whether CNN, a deep learning algorithm, could automatically estimate the apnea-hypopnea index (AHÍ) from nocturnal oximetry to help establish pediatric SAHS presence and severity. A database of 746 SpO2 recordings is involved in the study. CNN was trained using 20-min segments from the SpO2 signal in the training set (400 subjects). Hyperparameters of the CNN architecture were tuned using a validation set (100 subjects). This model was applied to a test set (246 subjects), in which the final AHI of each patient was obtained as the average of the output of the CNN for all the segments of the corresponding SpO2 signal. The AHI estimated by the CNN showed a promising diagnostic performance, with 74.8%, 90.7%, and 95.1% accuracies for the common AHI severity thresholds of 1, 5, and 10 events per hour (e/h), respectively. Furthermore, this model reached 28.6, 32.9, and 120.0 positive likelihood ratios for the above-mentioned AHI thresholds. This suggests that the information extracted from the oximetry signal by deep learning techniques may be useful to both establish pediatric SAHS and its severity.
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Assessment of Airflow and Oximetry Signals to Detect Pediatric Sleep Apnea-Hypopnea Syndrome Using AdaBoost. ENTROPY 2020; 22:e22060670. [PMID: 33286442 PMCID: PMC7517204 DOI: 10.3390/e22060670] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/09/2020] [Accepted: 06/15/2020] [Indexed: 12/17/2022]
Abstract
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.
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Yanney MP, Prayle AP, Rowbotham NJ, Kurc M, Tilbrook S, Ali N. Observational Study of Pulse Transit Time in Children With Sleep Disordered Breathing. Front Neurol 2020; 11:316. [PMID: 32457689 PMCID: PMC7225317 DOI: 10.3389/fneur.2020.00316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 03/31/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Pulse transit time (PTT) is a non-invasive measure of arousals and respiratory effort for which we aim to identify threshold values that detect sleep disordered breathing (SDB) in children. We also compare the sensitivity and specificity of oximetry with the findings of a multi-channel study. Methods: We performed a cross-sectional observational study of 521 children with SDB admitted for multi-channel sleep studies (pulse oximetry, ECG, video, sound, movement, PTT) in a secondary care centre. PTT data was available in 368 children. Studies were categorised as normal; primary snoring; upper airway resistance syndrome (UARS); obstructive sleep apnoea (OSA), and "abnormal other." Receiver operator characteristic curves were constructed for different PTT (Respiratory swing; Arousal index) thresholds using a random sample of 50% of children studied (training set); calculated thresholds of interest were validated against the other 50% (test set). Study findings were compared with oximetry categories (normal, inconclusive, abnormal) using data (mean and minimum oxygen saturations; oxygen desaturations > 4%) obtained during the study. Results: Respiratory swing of 17.92 ms identified SDB (OSA/UARS) with sensitivity: 0.80 (C.I. 0.62-0.90) and specificity 0.79 (C.I. 0.49-0.87). PTT arousal index of 16.06/ hour identified SDB (OSA/UARS) with sensitivity: 0.85 (95% C.I. 0.67-0.92) and specificity 0.37 (95% C.I. 0.17-0.48). Oximetry identified SDB (OSA) with sensitivity: 0.38 (C.I. 0.31-0.46) and specificity 0.98 (C.I. 0.97-1.00). Conclusions: PTT is more sensitive but less specific than oximetry at detecting SDB in children. The additional use of video and sound enabled detection of SDB in twice as many children as oximetry alone.
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Affiliation(s)
- Michael P Yanney
- Sherwood Forest Hospitals Foundation Trust, Mansfield, United Kingdom
| | - Andrew P Prayle
- Division of Child Health, Obstetrics and Gynaecology, Queens Medical Centre, Nottingham University Hospitals, Nottingham, United Kingdom.,NIHR Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham University Hospitals, Nottingham, United Kingdom
| | - Nicola J Rowbotham
- Division of Child Health, Obstetrics and Gynaecology, Queens Medical Centre, Nottingham University Hospitals, Nottingham, United Kingdom
| | - Miguel Kurc
- Sherwood Forest Hospitals Foundation Trust, Mansfield, United Kingdom
| | - Sean Tilbrook
- Sherwood Forest Hospitals Foundation Trust, Mansfield, United Kingdom
| | - Nabeel Ali
- Sherwood Forest Hospitals Foundation Trust, Mansfield, United Kingdom
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Vaquerizo-Villar F, Alvarez D, Kheirandish-Gozal L, Gutierrez-Tobal GC, Barroso-Garcia V, Campo FD, Gozal D, Hornero R. Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:3555-3558. [PMID: 31946646 DOI: 10.1109/embc.2019.8857934] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children's health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagnosis (overnight polysomnography) has several limitations, which has led to the search for alternative tests. In this sense, automated analysis of overnight oximetry has emerged as a simplified technique. Previous studies have focused on the extraction of ad-hoc features from the blood oxygen saturation (SpO2) signal, which may miss useful information related to apnea and hypopnea (AH) events. In order to overcome this limitation of traditional approaches, we propose the use of convolutional neural networks (CNN), a deep learning technique, to automatically detect AH events from the SpO2 raw data. CHAT-baseline dataset, composed of 453 SpO2 recordings, was used for this purpose. A CNN model was trained using 60-s segments from the SpO2 signal using a training set (50% of subjects). Optimum hyperparameters of the CNN architecture were obtained using a validation set (25% of subjects). This model was applied to a third test set (25% of subjects), reaching 93.6% accuracy to detect AH events. These results suggest that the application of CNN may be useful to detect changes produced in the oximetry signal by AH events in pediatric SAHS patients.
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43
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Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S, Westover MB. Artificial intelligence in sleep medicine: background and implications for clinicians. J Clin Sleep Med 2020; 16:609-618. [PMID: 32065113 PMCID: PMC7161463 DOI: 10.5664/jcsm.8388] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 02/14/2020] [Accepted: 02/14/2020] [Indexed: 12/14/2022]
Abstract
None Polysomnography remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelligence (AI)-based tools. Combined with other sources of health data, AI is expected to provide new insights to inform the clinical care of sleep disorders and advance our understanding of the integral role sleep plays in human health. Additionally, AI has the potential to streamline day-to-day operations and therefore optimize direct patient care by the sleep disorders team. However, clinicians, scientists, and other stakeholders must develop best practices to integrate this rapidly evolving technology into our daily work while maintaining the highest degree of quality and transparency in health care and research. Ultimately, when harnessed appropriately in conjunction with human expertise, AI will improve the practice of sleep medicine and further sleep science for the health and well-being of our patients.
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Affiliation(s)
- Cathy A. Goldstein
- Sleep Disorders Center, Department of Neurology, University of Michigan, Ann Arbor, Michigan
| | - Richard B. Berry
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Florida, Gainesville, Florida
| | - David T. Kent
- Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Azizi A. Seixas
- Department of Population Health, Department of Psychiatry, NYU Langone Health, New York, New York
| | - Susan Redline
- Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
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Bitners AC, Arens R. Evaluation and Management of Children with Obstructive Sleep Apnea Syndrome. Lung 2020; 198:257-270. [PMID: 32166426 PMCID: PMC7171982 DOI: 10.1007/s00408-020-00342-5] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 02/24/2020] [Indexed: 02/08/2023]
Abstract
Obstructive sleep apnea syndrome (OSAS) is a common pediatric disorder characterized by recurrent events of partial or complete upper airway obstruction during sleep which result in abnormal ventilation and sleep pattern. OSAS in children is associated with neurobehavioral deficits and cardiovascular morbidity which highlights the need for prompt recognition, diagnosis, and treatment. The purpose of this state-of-the-art review is to provide an update on the evaluation and management of children with OSAS with emphasis on children with complex medical comorbidities and those with residual OSAS following first-line treatment. Proposed treatment strategies reflecting recommendations from a variety of professional societies are presented. All children should be screened for OSAS and those with typical symptoms (e.g., snoring, restless sleep, and daytime hyperactivity) or risk factors (e.g., neurologic, genetic, and craniofacial disorders) should undergo further evaluation including referral to a sleep specialist or pediatric otolaryngologist and overnight polysomnography, which provides a definitive diagnosis. A cardiology and/or endocrinology evaluation should be considered in high-risk children. For the majority of children, first-line treatment is tonsillectomy with or without adenoidectomy; however, some children exhibit multiple levels of airway obstruction and may require additional evaluation and management. Anti-inflammatory medications, weight loss, and oral appliances may be appropriate in select cases, particularly for mild OSAS. Following initial treatment, all children should be monitored for residual symptoms and polysomnography may be repeated to identify persistent disease, which can be managed with positive airway pressure ventilation and additional surgical approaches if required.
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Affiliation(s)
| | - Raanan Arens
- Division of Respiratory and Sleep Medicine, Department of Pediatrics, Albert Einstein College of Medicine, Children's Hospital at Montefiore, 3415 Bainbridge Avenue, Bronx, NY, 10467-2490, USA.
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45
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Álvarez D, Cerezo-Hernández A, Crespo A, Gutiérrez-Tobal GC, Vaquerizo-Villar F, Barroso-García V, Moreno F, Arroyo CA, Ruiz T, Hornero R, Del Campo F. A machine learning-based test for adult sleep apnoea screening at home using oximetry and airflow. Sci Rep 2020; 10:5332. [PMID: 32210294 PMCID: PMC7093547 DOI: 10.1038/s41598-020-62223-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 03/09/2020] [Indexed: 02/05/2023] Open
Abstract
The most appropriate physiological signals to develop simplified as well as accurate screening tests for obstructive sleep apnoea (OSA) remain unknown. This study aimed at assessing whether joint analysis of at-home oximetry and airflow recordings by means of machine-learning algorithms leads to a significant diagnostic performance increase compared to single-channel approaches. Consecutive patients showing moderate-to-high clinical suspicion of OSA were involved. The apnoea-hypopnoea index (AHI) from unsupervised polysomnography was the gold standard. Oximetry and airflow from at-home polysomnography were parameterised by means of 38 time, frequency, and non-linear variables. Complementarity between both signals was exhaustively inspected via automated feature selection. Regression support vector machines were used to estimate the AHI from single-channel and dual-channel approaches. A total of 239 patients successfully completed at-home polysomnography. The optimum joint model reached 0.93 (95%CI 0.90–0.95) intra-class correlation coefficient between estimated and actual AHI. Overall performance of the dual-channel approach (kappa: 0.71; 4-class accuracy: 81.3%) significantly outperformed individual oximetry (kappa: 0.61; 4-class accuracy: 75.0%) and airflow (kappa: 0.42; 4-class accuracy: 61.5%). According to our findings, oximetry alone was able to reach notably high accuracy, particularly to confirm severe cases of the disease. Nevertheless, oximetry and airflow showed high complementarity leading to a remarkable performance increase compared to single-channel approaches. Consequently, their joint analysis via machine learning enables accurate abbreviated screening of OSA at home.
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Affiliation(s)
- Daniel Álvarez
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain. .,Biomedical Engineering Group, University of Valladolid, Valladolid, Spain. .,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain.
| | | | - Andrea Crespo
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.,Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | | | | | - Fernando Moreno
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - C Ainhoa Arroyo
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Tomás Ruiz
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
| | - Félix Del Campo
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.,Biomedical Engineering Group, University of Valladolid, Valladolid, Spain.,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Valladolid, Spain
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Ohn M, Eastwood P, von Ungern-Sternberg BS. Preoperative identification of children at high risk of obstructive sleep apnea. Paediatr Anaesth 2020; 30:221-231. [PMID: 31841240 DOI: 10.1111/pan.13788] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 12/08/2019] [Accepted: 12/10/2019] [Indexed: 12/24/2022]
Abstract
Obstructive sleep apnea is a common childhood disorder which can lead to serious health problems if left untreated. Enlarged adenoid and tonsils are the commonest causes, and adenotonsillectomy is the recommended first line of treatment. Obstructive sleep apnea poses as an anesthetic challenge, and it is a well-known risk factor for perioperative adverse events. The presence and severity of an obstructive sleep apnea diagnosis will influence anesthesia, pain management, and level of monitoring in recovery period. Preoperative obstructive sleep apnea assessment is necessary, and anesthetists are ideally placed to do so. Currently, there is no standardized approach to the best method of preoperative screening for obstructive sleep apnea. Focused history, clinical assessments, and knowledge regarding the strengths and limitations of available obstructive sleep apnea assessment tools will help recognize a child with obstructive sleep apnea in the preoperative setting.
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Affiliation(s)
- Mon Ohn
- Department of Respiratory and Sleep Medicine, Perth Children's Hospital, Nedlands, WA, Australia.,Medical School, The University of Western Australia, Crawley, WA, Australia.,Telethon Kids Institute, Nedlands, WA, Australia
| | - Peter Eastwood
- Centre for Sleep Science, School of Human Sciences, The University of Western Australia, Crawley, WA, Australia.,West Australian Sleep Disorders Research Institute, Sir Charles Gairdner Hospital, Nedlands, WA, Australia
| | - Britta S von Ungern-Sternberg
- Medical School, The University of Western Australia, Crawley, WA, Australia.,Telethon Kids Institute, Nedlands, WA, Australia.,Department of Anaesthesia and Pain Management, Perth Children's Hospital, Nedlands, WA, Australia
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Barroso-Garcia V, Gutierrez-Tobal GC, Kheirandish-Gozal L, Alvarez D, Vaquerizo-Villar F, Del Campo F, Gozal D, Hornero R. Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4580-4583. [PMID: 31946884 DOI: 10.1109/embc.2019.8857719] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The sleep apnea-hypopnea syndrome (SAHS) is a chronic respiratory disorder of high prevalence among children (up to 4%). Nocturnal polysomnography (PSG) is the gold standard method to diagnose SAHS, which is a complex, expensive, and time-consuming test. Consequently, alternative simplified methods are demanded. We propose the analysis of the respiratory rate variability (RRV) signal, directly obtained from the airflow (AF) signals. The aim of our study is to evaluate the usefulness of the spectral information obtained from RRV in the diagnosis of pediatric SAHS. A database composed of 946 AF and blood oxygen saturation (SpO2) recordings from children between 0 and 13 years old was used. Our database was divided into four severity groups according to the apnea-hipopnea index (AHI): no-SAHS (AHI <; 1 events/h), mild (1 events/h ≤ AHI <; 5 events/h), moderate (5 events/h ≤ AHI <; 10 events/h), and severe SAHS (AHI ≥ 10 events/h). RRV and 3% oxygen desaturation index (ODI3) were obtained from AF and SpO2 recordings, respectively. A spectral band of interest was determined (0.09-0.20 Hz.) and a total of 12 spectral features were extracted. Nine of these features showed statistically significant differences (p-value <; 0.05) among the four severity groups. The spectral features from RRV along with ODI3 were used as inputs to binary logistic regression (LR) classifiers. The diagnostic performance of LR models were evaluated for the AHI cut-off points of 1, 5, and 10 e/h, achieving 66.5%, 84.0%, and 88.5% accuracy, respectively. These results outperformed those obtained by single ODI3. The joint use of the spectral information from RRV and ODI3 achieved a high diagnostic capability in the most severely-affected children, thus showing their complementarity. These results suggest that the information contained in RRV spectrum together with ODI3 is useful to help identify moderate-to-severe SAHS.
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48
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Pépin JL, Letesson C, Le-Dong NN, Dedave A, Denison S, Cuthbert V, Martinot JB, Gozal D. Assessment of Mandibular Movement Monitoring With Machine Learning Analysis for the Diagnosis of Obstructive Sleep Apnea. JAMA Netw Open 2020; 3:e1919657. [PMID: 31968116 PMCID: PMC6991283 DOI: 10.1001/jamanetworkopen.2019.19657] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
IMPORTANCE Given the high prevalence of obstructive sleep apnea (OSA), there is a need for simpler and automated diagnostic approaches. OBJECTIVE To evaluate whether mandibular movement (MM) monitoring during sleep coupled with an automated analysis by machine learning is appropriate for OSA diagnosis. DESIGN, SETTING, AND PARTICIPANTS Diagnostic study of adults undergoing overnight in-laboratory polysomnography (PSG) as the reference method compared with simultaneous MM monitoring at a sleep clinic in an academic institution (Sleep Laboratory, Centre Hospitalier Universitaire Université Catholique de Louvain Namur Site Sainte-Elisabeth, Namur, Belgium). Patients with suspected OSA were enrolled from July 5, 2017, to October 31, 2018. MAIN OUTCOMES AND MEASURES Obstructive sleep apnea diagnosis required either evoking signs or symptoms or related medical or psychiatric comorbidities coupled with a PSG-derived respiratory disturbance index (PSG-RDI) of at least 5 events/h. A PSG-RDI of at least 15 events/h satisfied the diagnosis criteria even in the absence of associated symptoms or comorbidities. Patients who did not meet these criteria were classified as not having OSA. Agreement analysis and diagnostic performance were assessed by Bland-Altman plot comparing PSG-RDI and the Sunrise system RDI (Sr-RDI) with diagnosis threshold optimization via receiver operating characteristic curves, allowing for evaluation of the device sensitivity and specificity in detecting OSA at 5 events/h and 15 events/h. RESULTS Among 376 consecutive adults with suspected OSA, the mean (SD) age was 49.7 (13.2) years, the mean (SD) body mass index was 31.0 (7.1), and 207 (55.1%) were men. Reliable agreement was found between PSG-RDI and Sr-RDI in patients without OSA (n = 46; mean difference, 1.31; 95% CI, -1.05 to 3.66 events/h) and in patients with OSA with a PSG-RDI of at least 5 events/h with symptoms (n = 107; mean difference, -0.69; 95% CI, -3.77 to 2.38 events/h). An Sr-RDI underestimation of -11.74 (95% CI, -20.83 to -2.67) events/h in patients with OSA with a PSG-RDI of at least 15 events/h was detected and corrected by optimization of the Sunrise system diagnostic threshold. The Sr-RDI showed diagnostic capability, with areas under the receiver operating characteristic curve of 0.95 (95% CI, 0.92-0.96) and 0.93 (95% CI, 0.90-0.93) for corresponding PSG-RDIs of 5 events/h and 15 events/h, respectively. At the 2 optimal cutoffs of 7.63 events/h and 12.65 events/h, Sr-RDI had accuracy of 0.92 (95% CI, 0.90-0.94) and 0.88 (95% CI, 0.86-0.90) as well as posttest probabilities of 0.99 (95% CI, 0.99-0.99) and 0.89 (95% CI, 0.88-0.91) at PSG-RDIs of at least 5 events/h and at least 15 events/h, respectively, corresponding to positive likelihood ratios of 14.86 (95% CI, 9.86-30.12) and 5.63 (95% CI, 4.92-7.27), respectively. CONCLUSIONS AND RELEVANCE Automatic analysis of MM patterns provided reliable performance in RDI calculation. The use of this index in OSA diagnosis appears to be promising.
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Affiliation(s)
- Jean-Louis Pépin
- Pôle Thorax et Vaisseaux, Centre Hospitalier Universitaire (CHU) de Grenoble-Alpes (CHUGA), Université Grenoble Alpes, Institut National de la Santé et de la Recherche Medicale, Grenoble, France
| | | | | | | | | | - Valérie Cuthbert
- Sleep Laboratory, CHU Université Catholique de Louvain (UCL) Namur Site Sainte-Elisabeth, Namur, Belgium
| | - Jean-Benoît Martinot
- Sleep Laboratory, CHU Université Catholique de Louvain (UCL) Namur Site Sainte-Elisabeth, Namur, Belgium
- Institute of Experimental and Clinical Research, UCL Bruxelles Woluwe, Brussels, Belgium
| | - David Gozal
- Department of Child Health, University of Missouri, Columbia
- Child Health Research Institute, University of Missouri, Columbia
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49
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Mediano O, Cano-Pumarega I, Sánchez-de-la-Torre M, Alonso-Álvarez ML, Troncoso MF, García-Río F, Egea C, Durán-Cantolla J, Terán-Santos J, Barbé F, Fernando Masa J, Montserrat JM. Upcoming Scenarios for the Comprehensive Management of Obstructive Sleep Apnea: An Overview of the Spanish Sleep Network. Arch Bronconeumol 2020; 56:35-41. [DOI: 10.1016/j.arbres.2019.05.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/29/2019] [Accepted: 05/31/2019] [Indexed: 12/11/2022]
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50
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Pinheiro GDL, Cruz AF, Domingues DM, Genta PR, Drager LF, Strollo PJ, Lorenzi-Filho G. Validation of an Overnight Wireless High-Resolution Oximeter plus Cloud-Based Algorithm for the Diagnosis of Obstructive Sleep Apnea. Clinics (Sao Paulo) 2020; 75:e2414. [PMID: 33263626 PMCID: PMC7654954 DOI: 10.6061/clinics/2020/e2414] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 09/17/2020] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES Obstructive sleep apnea (OSA) is a common but largely underdiagnosed condition. This study aimed to test the hypothesis that the oxygen desaturation index (ODI) obtained using a wireless high-resolution oximeter with a built-in accelerometer linked to a smartphone with automated cloud analysis, Overnight Digital Monitoring (ODM), is a reliable method for the diagnosis of OSA. METHODS Consecutive patients referred to the sleep laboratory with suspected OSA underwent in-laboratory polysomnography (PSG) and simultaneous ODM. The PSG apnea-hypopnea index (AHI) was analyzed using the criteria recommended and accepted by the American Academy of Sleep Medicine (AASM) for the definition of hypopnea: arousal or ≥3% O2 desaturation (PSG-AHI3%) and ≥4% O2 desaturation (PSG-AHI4%), respectively. The results of PSG and ODM were compared by drawing parallels between the PSG-AHI3% and PSG-AHI4% with ODM-ODI3% and ODM-ODI4%, respectively. Bland-Altman plots, intraclass correlation, receiver operating characteristics (ROC) and area under the curve (AUC) analyses were conducted for statistical evaluation. ClinicalTrial.gov: NCT03526133. RESULTS This study included 304 participants (men: 55%; age: 55±14 years; body mass index: 30.9±5.7 kg/m2; PSG-AHI3%: 35.3±30.1/h, ODM-ODI3%: 30.3±25.9/h). The variability in the AASM scoring bias (PSG-AHI3% vs PSG-AHI4%) was significantly higher than that for PSG-AHI3% vs ODM-ODI3% (3%) and PSG-AHI4% vs ODM-ODI4% (4%) (9.7, 5.0, and 2.9/h, respectively; p<0.001). The limits of agreement (2±SD, derived from the Bland-Altman plot) of AASM scoring variability were also within the same range for (PSG vs ODM) 3% and 4% variability: 18.9, 21.6, and 16.5/h, respectively. The intraclass correlation/AUC for AASM scoring variability and PSG vs ODM 3% or 4% variability were also within the same range (0.944/0.977 and 0.953/0.955 or 0.971/0.964, respectively). CONCLUSION Our results showed that ODM is a simple and accurate method for the diagnosis of OSA.
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Affiliation(s)
- George do Lago Pinheiro
- Laboratorio do Sono, Divisao de Pneumologia, Instituto do Coraçao (InCor), Hospital das Clinicas (HCFMUSP), Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | | | | | - Pedro Rodrigues Genta
- Laboratorio do Sono, Divisao de Pneumologia, Instituto do Coraçao (InCor), Hospital das Clinicas (HCFMUSP), Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Luciano F. Drager
- Laboratorio do Sono, Divisao de Pneumologia, Instituto do Coraçao (InCor), Hospital das Clinicas (HCFMUSP), Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
- Unidade de Hipertensao, Instituto do Coracao (InCor), Hospital das Clinicas (HCFMUSP), Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
- Unidade de Hipertensao, Divisao Renal, Hospital das Clinicas (HCFMUSP), Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Patrick J. Strollo
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Geraldo Lorenzi-Filho
- Laboratorio do Sono, Divisao de Pneumologia, Instituto do Coraçao (InCor), Hospital das Clinicas (HCFMUSP), Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
- Corresponding author. E-mail:
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