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Vidigal TA, Haddad FLM, Guimaraes TM, Silva LO, Andersen ML, Schwab R, Cistulli PA, Pack AI, Tufik S, Bittencourt LRA. Can intraoral and facial photos predict obstructive sleep apnea in the general and clinical population? Sleep 2024; 47:zsad307. [PMID: 38038363 DOI: 10.1093/sleep/zsad307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/22/2023] [Indexed: 12/02/2023] Open
Abstract
STUDY OBJECTIVES This study aimed to evaluate and compare measurements of standardized craniofacial and intraoral photographs between clinical and general population samples, between groups of individuals with an apnea-hypopnea index (AHI) ≥ 15 and AHI < 15, and their interaction, as well as the relationship with the presence and severity of obstructive sleep apnea (OSA). METHODS We used data from 929 participants from Sleep Apnea Global Interdisciplinary Consortium, in which 309 patients from a clinical setting and 620 volunteers from a general population. RESULTS AHI ≥ 15 were observed in 30.3% of the total sample and there were some interactions between facial/intraoral measures with OSA and both samples. Mandibular volume (p < 0.01) and lateral face height (p = 0.04) were higher in the AHI ≥ 15 group in the clinical sample compared to the AHI ≥ 15 group in the general population and AHI < 15 group in the clinical sample. When adjusted for sex and age, greater mandible width (p < 0.01) differed both in the clinical and in the general population samples, reflecting AHI severity and the likelihood of OSA. The measure of smaller tongue curvature (p < 0.01) reflected the severity and probability of OSA in the clinical sample and the higher posterior mandibular height (p = 0.04) showed a relationship with higher AHI and higher risk of OSA in the general population. When adjusted for sex, age, and body mass index, only smaller tongue curvature (p < 0.01) was associated with moderate/severe OSA. CONCLUSIONS Measures of greater tongue and mandible were associated with increased OSA risk in the clinical sample and craniofacial measurement was associated in the general population sample.
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Affiliation(s)
- Tatiana A Vidigal
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Fernanda L M Haddad
- Departamento de Otorrinolaringologia, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Thaís M Guimaraes
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Luciana O Silva
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Monica L Andersen
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
- Sleep Institute, São Paulo, Brazil
| | - Richard Schwab
- Division of Sleep Medicine, Pulmonary, Allergy and Critical Care Division, Department of Medicine, Penn Sleep Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Peter A Cistulli
- Department of Respiratory and Sleep Medicine, Centre for Sleep Health and Research, Royal NorthShore Hospital, St Leonards, NSW, Australia
| | - Alan I Pack
- Division of Sleep Medicine/Department of Medicine, Center for Sleep and Circadian Neurobiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sergio Tufik
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil
- Sleep Institute, São Paulo, Brazil
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Qin H, Fietze I, Mazzotti DR, Steenbergen N, Kraemer JF, Glos M, Wessel N, Song L, Penzel T, Zhang X. Obstructive sleep apnea heterogeneity and autonomic function: a role for heart rate variability in therapy selection and efficacy monitoring. J Sleep Res 2024; 33:e14020. [PMID: 37709966 DOI: 10.1111/jsr.14020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 07/23/2023] [Accepted: 08/03/2023] [Indexed: 09/16/2023]
Abstract
Obstructive sleep apnea is a highly prevalent sleep-related breathing disorder, resulting in a disturbed breathing pattern, changes in blood gases, abnormal autonomic regulation, metabolic fluctuation, poor neurocognitive performance, and increased cardiovascular risk. With broad inter-individual differences recognised in risk factors, clinical symptoms, gene expression, physiological characteristics, and health outcomes, various obstructive sleep apnea subtypes have been identified. Therapeutic efficacy and its impact on outcomes, particularly for cardiovascular consequences, may also vary depending on these features in obstructive sleep apnea. A number of interventions such as positive airway pressure therapies, oral appliance, surgical treatment, and pharmaceutical options are available in clinical practice. Selecting an effective obstructive sleep apnea treatment and therapy is a challenging medical decision due to obstructive sleep apnea heterogeneity and numerous treatment modalities. Thus, an objective marker for clinical evaluation is warranted to estimate the treatment response in patients with obstructive sleep apnea. Currently, while the Apnea-Hypopnea Index is used for severity assessment of obstructive sleep apnea and still considered a major guide to diagnosis and managements of obstructive sleep apnea, the Apnea-Hypopnea Index is not a robust marker of symptoms, function, or outcome improvement. Abnormal cardiac autonomic modulation can provide additional insight to better understand obstructive sleep apnea phenotyping. Heart rate variability is a reliable neurocardiac tool to assess altered autonomic function and can also provide cardiovascular information in obstructive sleep apnea. Beyond the Apnea-Hypopnea Index, this review aims to discuss the role of heart rate variability as an indicator and predictor of therapeutic efficacy to different modalities in order to optimise tailored treatment for obstructive sleep apnea.
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Affiliation(s)
- Hua Qin
- Department of Otolaryngology, Head and Neck Surgery, State Key Laboratory of Respiratory Disease, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Interdisciplinary Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ingo Fietze
- Interdisciplinary Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
- The Fourth People's Hospital of Guangyuan, Guangyuan, China
| | - Diego R Mazzotti
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
- Division of Pulmonary Critical Care and Sleep Medicine, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
| | | | - Jan F Kraemer
- Department of Physics, Humboldt-Universität zu Berlin, Berlin, Germany
- Information Processing and Analytics Group, School of Library and Information Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Martin Glos
- Interdisciplinary Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Niels Wessel
- Department of Physics, Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Medicine, Medical School Berlin, Berlin, Germany
| | - Lijun Song
- Department of Otolaryngology, Head and Neck Surgery, State Key Laboratory of Respiratory Disease, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Thomas Penzel
- Interdisciplinary Center of Sleep Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Xiaowen Zhang
- Department of Otolaryngology, Head and Neck Surgery, State Key Laboratory of Respiratory Disease, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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Olszewska E, De Vito A, Baptista P, Heiser C, O’Connor-Reina C, Kotecha B, Vanderveken O, Vicini C. Consensus Statements among European Sleep Surgery Experts on Snoring and Obstructive Sleep Apnea: Part 1 Definitions and Diagnosis. J Clin Med 2024; 13:502. [PMID: 38256636 PMCID: PMC10816926 DOI: 10.3390/jcm13020502] [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: 12/06/2023] [Revised: 01/06/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Seeking consensus on definitions and diagnosis of snoring and obstructive sleep apnea (OSA) among sleep surgeons is important, particularly in this relatively new field with variability in knowledge and practices. A set of statements was developed based on the literature and circulated among eight panel members of European experts, utilizing the Delphi method. Responses in agreement and disagreement on each statement and the comments were used to assess the level of consensus and develop a revised version. The new version with the level of consensus and anonymized comments was sent to each panel member as the second round. This was repeated a total of five rounds. The total number of statements included in the initial set was 112. In the first round, of all eight panelists, the percentage of questions that had consensus among the eight, seven, and six panelists were 45%, 4.5%, and 7.1%, respectively. In the final set of statements consisting of 99, the percentage of questions that had consensus among the 8, 7, and 6 panelists went up to 66.7%, 24.2%, and 6.1%, respectively. Delphi's method demonstrated an efficient method of interaction among experts and the establishment of consensus on a specific set of statements.
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Affiliation(s)
- Ewa Olszewska
- Department of Otolaryngology, Sleep Apnea Surgery Center, Medical University of Bialystok, 15-276 Bialystok, Poland
| | - Andrea De Vito
- Department of Surgery, Morgagni-Pierantoni Hospital, Health Local Agency of Romagna, 47121 Forlì, Italy;
| | - Peter Baptista
- Clinica Universidad da Navarra, Departmento de Orl, 31008 Pamplona, Spain;
| | - Clemens Heiser
- Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerp, Belgium; (C.H.); (O.V.)
- Department of Otorhinolaryngology/Head and Neck Surgery, Klinikum Rechts der Isar, Technical University of Munich, 80333 Munich, Germany
| | | | - Bhik Kotecha
- Nuffield Health Brentwood, Essex, Brentwood CM15 8EH, UK;
- UME Health, 17 Harley Street, London W1G 9QH, UK
| | - Olivier Vanderveken
- Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerp, Belgium; (C.H.); (O.V.)
- Department of Otorhinolaryngology, Head and Neck Surgery, Antwerp University Hospital, 2650 Antwerp, Belgium
| | - Claudio Vicini
- GVM Care & Research ENT Consultant, GVM Primus Medica Center, GVM San Pier Damiano Hospital, 48018 Faenza, Italy;
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He S, Li Y, Zhang C, Li Z, Ren Y, Li T, Wang J. Deep learning technique to detect craniofacial anatomical abnormalities concentrated on middle and anterior of face in patients with sleep apnea. Sleep Med 2023; 112:12-20. [PMID: 37801860 DOI: 10.1016/j.sleep.2023.09.025] [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: 06/18/2023] [Revised: 09/17/2023] [Accepted: 09/23/2023] [Indexed: 10/08/2023]
Abstract
OBJECTIVES The aim of this study is to propose a deep learning-based model using craniofacial photographs for automatic obstructive sleep apnea (OSA) detection and to perform design explainability tests to investigate important craniofacial regions as well as the reliability of the method. METHODS Five hundred and thirty participants with suspected OSA are subjected to polysomnography. Front and profile craniofacial photographs are captured and randomly segregated into training, validation, and test sets for model development and evaluation. Photographic occlusion tests and visual observations are performed to determine regions at risk of OSA. The number of positive regions in each participant is identified and their associations with OSA is assessed. RESULTS The model using craniofacial photographs alone yields an accuracy of 0.884 and an area under the receiver operating characteristic curve of 0.881 (95% confidence interval, 0.839-0.922). Using the cutoff point with the maximum sum of sensitivity and specificity, the model exhibits a sensitivity of 0.905 and a specificity of 0.941. The bilateral eyes, nose, mouth and chin, pre-auricular area, and ears contribute the most to disease detection. When photographs that increase the weights of these regions are used, the performance of the model improved. Additionally, different severities of OSA become more prevalent as the number of positive craniofacial regions increases. CONCLUSIONS The results suggest that the deep learning-based model can extract meaningful features that are primarily concentrated in the middle and anterior regions of the face.
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Affiliation(s)
- Shuai He
- Department of Otolaryngology Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, China
| | - Yingjie Li
- School of Computer Science and Engineering, Beijing Technology and Business University, China
| | - Chong Zhang
- Department of Big Data Management and Application, School of International Economics and Management, Beijing Technology and Business University, China
| | - Zufei Li
- Department of Otolaryngology Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, China
| | - Yuanyuan Ren
- Department of Otolaryngology Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, China
| | - Tiancheng Li
- Department of Otolaryngology Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, China.
| | - Jianting Wang
- Department of Otolaryngology Head and Neck Surgery, Beijing Chaoyang Hospital, Capital Medical University, China.
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5
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Chang JL, Goldberg AN, Alt JA, Alzoubaidi M, Ashbrook L, Auckley D, Ayappa I, Bakhtiar H, Barrera JE, Bartley BL, Billings ME, Boon MS, Bosschieter P, Braverman I, Brodie K, Cabrera-Muffly C, Caesar R, Cahali MB, Cai Y, Cao M, Capasso R, Caples SM, Chahine LM, Chang CP, Chang KW, Chaudhary N, Cheong CSJ, Chowdhuri S, Cistulli PA, Claman D, Collen J, Coughlin KC, Creamer J, Davis EM, Dupuy-McCauley KL, Durr ML, Dutt M, Ali ME, Elkassabany NM, Epstein LJ, Fiala JA, Freedman N, Gill K, Boyd Gillespie M, Golisch L, Gooneratne N, Gottlieb DJ, Green KK, Gulati A, Gurubhagavatula I, Hayward N, Hoff PT, Hoffmann OM, Holfinger SJ, Hsia J, Huntley C, Huoh KC, Huyett P, Inala S, Ishman SL, Jella TK, Jobanputra AM, Johnson AP, Junna MR, Kado JT, Kaffenberger TM, Kapur VK, Kezirian EJ, Khan M, Kirsch DB, Kominsky A, Kryger M, Krystal AD, Kushida CA, Kuzniar TJ, Lam DJ, Lettieri CJ, Lim DC, Lin HC, Liu SY, MacKay SG, Magalang UJ, Malhotra A, Mansukhani MP, Maurer JT, May AM, Mitchell RB, Mokhlesi B, Mullins AE, Nada EM, Naik S, Nokes B, Olson MD, Pack AI, Pang EB, Pang KP, Patil SP, Van de Perck E, Piccirillo JF, Pien GW, Piper AJ, Plawecki A, Quigg M, Ravesloot MJ, Redline S, Rotenberg BW, Ryden A, Sarmiento KF, Sbeih F, Schell AE, Schmickl CN, Schotland HM, Schwab RJ, Seo J, Shah N, Shelgikar AV, Shochat I, Soose RJ, Steele TO, Stephens E, Stepnowsky C, Strohl KP, Sutherland K, Suurna MV, Thaler E, Thapa S, Vanderveken OM, de Vries N, Weaver EM, Weir ID, Wolfe LF, Tucker Woodson B, Won CH, Xu J, Yalamanchi P, Yaremchuk K, Yeghiazarians Y, Yu JL, Zeidler M, Rosen IM. International Consensus Statement on Obstructive Sleep Apnea. Int Forum Allergy Rhinol 2023; 13:1061-1482. [PMID: 36068685 PMCID: PMC10359192 DOI: 10.1002/alr.23079] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 08/12/2022] [Accepted: 08/18/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND Evaluation and interpretation of the literature on obstructive sleep apnea (OSA) allows for consolidation and determination of the key factors important for clinical management of the adult OSA patient. Toward this goal, an international collaborative of multidisciplinary experts in sleep apnea evaluation and treatment have produced the International Consensus statement on Obstructive Sleep Apnea (ICS:OSA). METHODS Using previously defined methodology, focal topics in OSA were assigned as literature review (LR), evidence-based review (EBR), or evidence-based review with recommendations (EBR-R) formats. Each topic incorporated the available and relevant evidence which was summarized and graded on study quality. Each topic and section underwent iterative review and the ICS:OSA was created and reviewed by all authors for consensus. RESULTS The ICS:OSA addresses OSA syndrome definitions, pathophysiology, epidemiology, risk factors for disease, screening methods, diagnostic testing types, multiple treatment modalities, and effects of OSA treatment on multiple OSA-associated comorbidities. Specific focus on outcomes with positive airway pressure (PAP) and surgical treatments were evaluated. CONCLUSION This review of the literature consolidates the available knowledge and identifies the limitations of the current evidence on OSA. This effort aims to create a resource for OSA evidence-based practice and identify future research needs. Knowledge gaps and research opportunities include improving the metrics of OSA disease, determining the optimal OSA screening paradigms, developing strategies for PAP adherence and longitudinal care, enhancing selection of PAP alternatives and surgery, understanding health risk outcomes, and translating evidence into individualized approaches to therapy.
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Affiliation(s)
- Jolie L. Chang
- University of California, San Francisco, California, USA
| | | | | | | | - Liza Ashbrook
- University of California, San Francisco, California, USA
| | | | - Indu Ayappa
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | | | - Maurits S. Boon
- Sidney Kimmel Medical Center at Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Pien Bosschieter
- Academic Centre for Dentistry Amsterdam, Amsterdam, The Netherlands
| | - Itzhak Braverman
- Hillel Yaffe Medical Center, Hadera Technion, Faculty of Medicine, Hadera, Israel
| | - Kara Brodie
- University of California, San Francisco, California, USA
| | | | - Ray Caesar
- Stone Oak Orthodontics, San Antonio, Texas, USA
| | | | - Yi Cai
- University of California, San Francisco, California, USA
| | | | | | | | | | | | | | | | | | - Susmita Chowdhuri
- Wayne State University and John D. Dingell VA Medical Center, Detroit, Michigan, USA
| | - Peter A. Cistulli
- Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - David Claman
- University of California, San Francisco, California, USA
| | - Jacob Collen
- Uniformed Services University, Bethesda, Maryland, USA
| | | | | | - Eric M. Davis
- University of Virginia, Charlottesville, Virginia, USA
| | | | | | - Mohan Dutt
- University of Michigan, Ann Arbor, Michigan, USA
| | - Mazen El Ali
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | | | | | | | - Kirat Gill
- Stanford University, Palo Alto, California, USA
| | | | - Lea Golisch
- University Hospital Mannheim, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | | | | | | | - Arushi Gulati
- University of California, San Francisco, California, USA
| | | | | | - Paul T. Hoff
- University of Michigan, Ann Arbor, Michigan, USA
| | - Oliver M.G. Hoffmann
- University Hospital Mannheim, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | | | - Jennifer Hsia
- University of Minnesota, Minneapolis, Minnesota, USA
| | - Colin Huntley
- Sidney Kimmel Medical Center at Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | | | | | - Sanjana Inala
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | | | | | | | | | | | | | - Meena Khan
- Ohio State University, Columbus, Ohio, USA
| | | | - Alan Kominsky
- Cleveland Clinic Head and Neck Institute, Cleveland, Ohio, USA
| | - Meir Kryger
- Yale School of Medicine, New Haven, Connecticut, USA
| | | | | | | | - Derek J. Lam
- Oregon Health and Science University, Portland, Oregon, USA
| | | | | | | | | | | | | | - Atul Malhotra
- University of California, San Diego, California, USA
| | | | - Joachim T. Maurer
- University Hospital Mannheim, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | - Anna M. May
- Case Western Reserve University, Cleveland, Ohio, USA
| | - Ron B. Mitchell
- University of Texas, Southwestern and Children’s Medical Center Dallas, Texas, USA
| | | | | | | | | | - Brandon Nokes
- University of California, San Diego, California, USA
| | | | - Allan I. Pack
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | | | | | | | | | | | | | - Mark Quigg
- University of Virginia, Charlottesville, Virginia, USA
| | | | - Susan Redline
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Armand Ryden
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | | | - Firas Sbeih
- Cleveland Clinic Head and Neck Institute, Cleveland, Ohio, USA
| | | | | | | | | | - Jiyeon Seo
- University of California, Los Angeles, California, USA
| | - Neomi Shah
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - Ryan J. Soose
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Erika Stephens
- University of California, San Francisco, California, USA
| | | | | | | | | | - Erica Thaler
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sritika Thapa
- Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Nico de Vries
- Academic Centre for Dentistry Amsterdam, Amsterdam, The Netherlands
| | | | - Ian D. Weir
- Yale School of Medicine, New Haven, Connecticut, USA
| | | | | | | | - Josie Xu
- University of Toronto, Ontario, Canada
| | | | | | | | | | | | - Ilene M. Rosen
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Cluster analysis of clinical phenotypic heterogeneity in obstructive sleep apnea assessed using photoplethysmography. Sleep Med 2023; 102:134-141. [PMID: 36641931 DOI: 10.1016/j.sleep.2022.12.023] [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: 09/04/2022] [Revised: 12/15/2022] [Accepted: 12/28/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND We evaluated heterogeneity in clinical phenotypes among patients with obstructive sleep apnea syndrome (OSAHS) using photoplethysmography (PPG) in cluster analysis. METHODS All enrolled patients underwent polysomnography (PSG) monitoring while wearing a PPG device. Pulse wave signals were recorded with a modified pulse oximetry probe in the PPG device. The pulse wave-derived cardiac risk composite parameter (CRI) and eight derived signal parameters were used to assess OSAHS phenotype. We defined a high cardiovascular risk OSAHS group (CRI ≥0.5) and low cardiovascular risk OSAHS group (CRI <0.5). K-means clustering was performed for analysis of clinical phenotype heterogeneity in OSAHS by combining the CRI and its derived signals. RESULTS The OSAHS group had high cardiovascular risk for sex, age, body mass index, systolic and diastolic blood pressure, apnea hypopnea index, and obstructive arousal index and higher risk of developing hypertension, diabetes, and cerebrovascular comorbidities. The low cardiovascular risk OSAHS group had higher blood oxygen levels. Three clinical phenotypes were identified in CRI clustering: 1) typical OSAHS with high risk of hypertension (characterized by middle age, obesity, hypertension with severe OSAHS); 2) older women and mild OSAHS; 3) older men and mild OSAHS. Three subtypes were obtained based on the eight cardiac risk-derived parameters: 1) hypoxia combined with decreased pulse wave amplitude variation; 2) decreased vascular pulse wave amplitude combined with decreased pulse frequency; 3) arrhythmia combined with hypoxia. CONCLUSIONS Establishing OSAHS clinical phenotypes with the CRI and derived parameters using PPG may help in establishing multi-dimensional assessment of cardiovascular risk in OSAHS.
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Obstructive Sleep Apnea in African Americans: A Literature Review. CURRENT PULMONOLOGY REPORTS 2023. [DOI: 10.1007/s13665-023-00300-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Daboul A, Krüger M, Ivanonvka T, Obst A, Ewert R, Stubbe B, Fietze I, Penzel T, Hosten N, Biffar R, Cardini A. Do brachycephaly and nose size predict the severity of obstructive sleep apnea (OSA)? A sample-based geometric morphometric analysis of craniofacial variation in relation to OSA syndrome and the role of confounding factors. J Sleep Res 2022; 32:e13801. [PMID: 36579627 DOI: 10.1111/jsr.13801] [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: 09/14/2022] [Revised: 10/27/2022] [Accepted: 11/25/2022] [Indexed: 12/30/2022]
Abstract
Obstructive sleep apnea is a common disorder that leads to sleep fragmentation and is potentially bidirectionally related to a variety of comorbidities, including an increased risk of heart failure and stroke. It is often considered a consequence of anatomical abnormalities, especially in the head and neck, but its pathophysiology is likely to be multifactorial in origin. With geometric morphometrics, and a large sample of adults from the Study for Health in Pomerania, we explore the association of craniofacial morphology to the apnea-hypopnea index used as an estimate of obstructive sleep apnea severity. We show that craniofacial size and asymmetry, an aspect of morphological variation seldom analysed in obstructive sleep apnea research, are both uncorrelated to apnea-hypopnea index. In contrast, as in previous analyses, we find evidence that brachycephaly and larger nasal proportions might be associated to obstructive sleep apnea severity. However, this correlational signal is weak and completely disappears when age-related shape variation is statistically controlled for. Our findings suggest that previous work might need to be re-evaluated, and urge researchers to take into account the role of confounders to avoid potentially spurious findings in association studies.
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Affiliation(s)
- Amro Daboul
- Department of Prosthodontics, Gerodontology and Biomaterials, University Medicine Greifswald, Greifswald, Germany
| | - Markus Krüger
- Department of Prosthodontics, Gerodontology and Biomaterials, University Medicine Greifswald, Greifswald, Germany
| | - Tatyana Ivanonvka
- Department of Electrical Engineering, Media and Computer Science East Bavarian Technical University of Applied Sciences Amberg-Weiden, Amberg, Germany
| | - Anne Obst
- Department of Prosthodontics, Gerodontology and Biomaterials, University Medicine Greifswald, Greifswald, Germany
| | - Ralf Ewert
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Beate Stubbe
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Ingo Fietze
- Interdisciplinary Sleep Medicine Center, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Penzel
- Interdisciplinary Sleep Medicine Center, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Norbert Hosten
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Reiner Biffar
- Department of Prosthodontics, Gerodontology and Biomaterials, University Medicine Greifswald, Greifswald, Germany
| | - Andrea Cardini
- Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, Modena, Italy.,School of Anatomy, Physiology and Human Biology, The University of Western Australia, Crawley, Western Australia, Australia
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Diagnostic Performance of Machine Learning-Derived OSA Prediction Tools in Large Clinical and Community-Based Samples. Chest 2022; 161:807-817. [PMID: 34717928 PMCID: PMC8941600 DOI: 10.1016/j.chest.2021.10.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/14/2021] [Accepted: 10/10/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Prediction tools without patient-reported symptoms could facilitate widespread identification of OSA. RESEARCH QUESTION What is the diagnostic performance of OSA prediction tools derived from machine learning using readily available data without patient responses to questionnaires? Also, how do they compare with STOP-BANG, an OSA prediction tool, in clinical and community-based samples? STUDY DESIGN AND METHODS Logistic regression and machine learning techniques, including artificial neural network (ANN), random forests (RF), and kernel support vector machine, were used to determine the ability of age, sex, BMI, and race to predict OSA status. A retrospective cohort of 17,448 subjects from sleep clinics within the international Sleep Apnea Global Interdisciplinary Consortium (SAGIC) were randomly split into training (n = 10,469) and validation (n = 6,979) sets. Model comparisons were performed by using the area under the receiver-operating curve (AUC). Trained models were compared with the STOP-BANG questionnaire in two prospective testing datasets: an independent clinic-based sample from SAGIC (n = 1,613) and a community-based sample from the Sleep Heart Health Study (n = 5,599). RESULTS The AUCs (95% CI) of the machine learning models were significantly higher than logistic regression (0.61 [0.60-0.62]) in both the training and validation datasets (ANN, 0.68 [0.66-0.69]; RF, 0.68 [0.67-0.70]; and kernel support vector machine, 0.66 [0.65-0.67]). In the SAGIC testing sample, the ANN (0.70 [0.68-0.72]) and RF (0.70 [0.68-0.73]) models had AUCs similar to those of the STOP-BANG (0.71 [0.68-0.72]). In the Sleep Heart Health Study testing sample, the ANN (0.72 [0.71-0.74]) had AUCs similar to those of STOP-BANG (0.72 [0.70-0.73]). INTERPRETATION OSA prediction tools using machine learning without patient-reported symptoms provide better diagnostic performance than logistic regression. In clinical and community-based samples, the symptomless ANN tool has diagnostic performance similar to that of a widely used prediction tool that includes patient symptoms. Machine learning-derived algorithms may have utility for widespread identification of OSA.
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Mazzotti DR. Landscape of biomedical informatics standards and terminologies for clinical sleep medicine research: A systematic review. Sleep Med Rev 2021; 60:101529. [PMID: 34455108 DOI: 10.1016/j.smrv.2021.101529] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/14/2021] [Accepted: 07/03/2021] [Indexed: 12/31/2022]
Abstract
A systematic literature review was conducted to understand the current landscape of standards and terminologies used in clinical sleep medicine. Literature search on PubMed, EMBASE, Medline and Web of Science was performed in March 2021 using terms related to sleep, terminologies, standards, harmonization, semantics, ontology, and electronic health records (EHR). Systematic review was carried out according to PRISMA. Among 128 included studies, 35 were eligible for review. Articles were broadly classified into six topics: standard terminology efforts, reporting standards, databases and resources, data integration efforts, EHR abstraction and standards for automated sleep scoring. This review highlights the progress and challenges related to establishing computable terminologies in sleep medicine, and identifies gaps, limitations and research opportunities related to data integration that could improve adoption of clinical research informatics in this field. There is a need for the systematic adoption of standardized terminologies in all areas of sleep medicine. Existing data aggregation resources could be leveraged to support the development of an integrated infrastructure and subsequent deployment in EHR systems within sleep centers. Ultimately, the adoption of standardized practices for documenting sleep disorders and related traits facilitates data sharing, thus accelerating discovery and clinical translation of informatics approaches applied to sleep medicine.
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Affiliation(s)
- Diego R Mazzotti
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA.
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Laharnar N, Herberger S, Prochnow LK, Chen NH, Cistulli PA, Pack AI, Schwab R, Keenan BT, Mazzotti DR, Fietze I, Penzel T. Simple and Unbiased OSA Prescreening: Introduction of a New Morphologic OSA Prediction Score. Nat Sci Sleep 2021; 13:2039-2049. [PMID: 34785967 PMCID: PMC8590840 DOI: 10.2147/nss.s333471] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/06/2021] [Indexed: 01/22/2023] Open
Abstract
PURPOSE An early prescreening in suspected obstructive sleep apnea (OSA) patients is desirable to expedite diagnosis and treatment. However, the accuracy and applicability of current prescreening tools is insufficient. We developed and tested an unbiased scoring system based solely on objective variables, which focuses on the diagnosis of severe OSA and exclusion of OSA. PATIENTS AND METHODS The OSA prediction score was developed (n = 150) and validated (n = 50) within German sleep center patients that were recruited as part of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Six objective variables that were easy to assess and highly correlated with the apnea-hypopnea index were chosen for the score, including some known OSA risk factors: body-mass index, neck circumference, waist circumference, tongue position, male gender, and age (for women only). To test the predictive ability of the score and identify score thresholds, the receiver-operating characteristics (ROC) and curve were calculated. RESULTS A score ≥8 for predicting severe OSA resulted in an area under the ROC curve (ROC-AUC) of 90% (95% confidence interval: 84%, 95%), test accuracy of 82% (75%, 88%), sensitivity of 82% (65%, 93%), specificity of 82% (74%, 88%), and positive likelihood ratio of 4.55 (3.00, 6.90). A score ≤5 for predicting the absence of OSA resulted in a ROC-AUC of 89% (83%, 94%), test accuracy of 80% (73%, 86%), sensitivity of 72% (55%, 85%), specificity of 83% (75%, 89%), and positive likelihood ratio of 4.20 (2.66, 6.61). Performance characteristics were comparable in the small validation sample. CONCLUSION We introduced a novel prescreening tool combining easily obtainable objective measures with predictive power and high general applicability. The proposed tool successfully predicted severe OSA (important due to its high risk of cardiovascular disease) and the exclusion of OSA (rarely a feature of previous screening instruments, but important for better differential diagnosis and treatment).
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Affiliation(s)
- Naima Laharnar
- Department of Internal Medicine and Dermatology, Interdisciplinary Center of Sleep Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Herberger
- Department of Internal Medicine and Dermatology, Interdisciplinary Center of Sleep Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lisa-Kristin Prochnow
- Department of Internal Medicine and Dermatology, Interdisciplinary Center of Sleep Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ning-Hung Chen
- Department of Pulmonary and Critical Care Medicine, Sleep Center, Chang Gung Memorial Hospital, Taipei, Taiwan
| | - Peter A Cistulli
- Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia.,Department of Respiratory Medicine, Royal North Shore Hospital, Sydney, NSW, Australia
| | - Allan I Pack
- Department of Medicine/Division of Sleep Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Richard Schwab
- Department of Medicine/Division of Sleep Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brendan T Keenan
- Department of Medicine/Division of Sleep Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Diego R Mazzotti
- Department of Medicine/Division of Sleep Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS, USA
| | - Ingo Fietze
- Department of Internal Medicine and Dermatology, Interdisciplinary Center of Sleep Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.,The Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov, First Moscow State Medical University of the Ministry of Health of the Russian Federation, Moscow, Russia
| | - Thomas Penzel
- Department of Internal Medicine and Dermatology, Interdisciplinary Center of Sleep Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Department of Biology, Saratov State University, Saratov, Russia
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