1
|
Del Campo F, Arroyo CA, Zamarrón C, Álvarez D. Diagnosis of Obstructive Sleep Apnea in Patients with Associated Comorbidity. Advances in the Diagnosis and Treatment of Sleep Apnea 2022; 1384:43-61. [PMID: 36217078 DOI: 10.1007/978-3-031-06413-5_4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Obstructive sleep apnea (OSA) is a heterogeneous disease with many physiological implications. OSA is associated with a great diversity of diseases, with which it shares common and very often bidirectional pathophysiological mechanisms, leading to significantly negative implications on morbidity and mortality. In these patients, underdiagnosis of OSA is high. Concerning cardiorespiratory comorbidities, several studies have assessed the usefulness of simplified screening tests for OSA in patients with hypertension, COPD, heart failure, atrial fibrillation, stroke, morbid obesity, and in hospitalized elders.The key question is whether there is any benefit in the screening for the existence of OSA in patients with comorbidities. In this regard, there are few studies evaluating the performance of the various diagnostic procedures in patients at high risk for OSA. The purpose of this chapter is to review the existing literature about diagnosis in those diseases with a high risk for OSA, with special reference to artificial intelligence-related methods.
Collapse
Affiliation(s)
- Félix Del Campo
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN). Instituto de Salud Carlos III, Madrid, Spain
| | - C Ainhoa Arroyo
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Carlos Zamarrón
- Division of Respiratory Medicine, Hospital Clínico Universitario, Santiago de Compostela, Spain
| | - Daniel Álvarez
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain.
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN). Instituto de Salud Carlos III, Madrid, Spain.
| |
Collapse
|
2
|
Álvarez D, Arroyo CA, de Frutos JF, Crespo A, Cerezo-Hernández A, Gutiérrez-Tobal GC, Vaquerizo-Villar F, Barroso-García V, Moreno F, Ruiz T, Hornero R, del Campo F. Assessment of Nocturnal Autonomic Cardiac Imbalance in Positional Obstructive Sleep Apnea. A Multiscale Nonlinear Approach. Entropy (Basel) 2020; 22:E1404. [PMID: 33322747 PMCID: PMC7764670 DOI: 10.3390/e22121404] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 12/07/2020] [Accepted: 12/09/2020] [Indexed: 12/24/2022]
Abstract
Positional obstructive sleep apnea (POSA) is a major phenotype of sleep apnea. Supine-predominant positional patients are frequently characterized by milder symptoms and less comorbidity due to a lower age, body mass index, and overall apnea-hypopnea index. However, the bradycardia-tachycardia pattern during apneic events is known to be more severe in the supine position, which could affect the cardiac regulation of positional patients. This study aims at characterizing nocturnal heart rate modulation in the presence of POSA in order to assess potential differences between positional and non-positional patients. Patients showing clinical symptoms of suffering from a sleep-related breathing disorder performed unsupervised portable polysomnography (PSG) and simultaneous nocturnal pulse oximetry (NPO) at home. Positional patients were identified according to the Amsterdam POSA classification (APOC) criteria. Pulse rate variability (PRV) recordings from the NPO readings were used to assess overnight cardiac modulation. Conventional cardiac indexes in the time and frequency domains were computed. Additionally, multiscale entropy (MSE) was used to investigate the nonlinear dynamics of the PRV recordings in POSA and non-POSA patients. A total of 129 patients (median age 56.0, interquartile range (IQR) 44.8-63.0 years, median body mass index (BMI) 27.7, IQR 26.0-31.3 kg/m2) were classified as POSA (37 APOC I, 77 APOC II, and 15 APOC III), while 104 subjects (median age 57.5, IQR 49.0-67.0 years, median BMI 29.8, IQR 26.6-34.7 kg/m2) comprised the non-POSA group. Overnight PRV recordings from positional patients showed significantly higher disorderliness than non-positional subjects in the smallest biological scales of the MSE profile (τ = 1: 0.25, IQR 0.20-0.31 vs. 0.22, IQR 0.18-0.27, p < 0.01) (τ = 2: 0.41, IQR 0.34-0.48 vs. 0.37, IQR 0.29-0.42, p < 0.01). According to our findings, nocturnal heart rate regulation is severely affected in POSA patients, suggesting increased cardiac imbalance due to predominant positional apneas.
Collapse
Affiliation(s)
- Daniel Álvarez
- Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain; (C.A.A.); (J.F.d.F.); (A.C.); (A.C.-H.); (F.M.); (T.R.)
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (G.C.G.-T.); (F.V.-V.); (V.B.-G.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - C. Ainhoa Arroyo
- Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain; (C.A.A.); (J.F.d.F.); (A.C.); (A.C.-H.); (F.M.); (T.R.)
| | - Julio F. de Frutos
- Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain; (C.A.A.); (J.F.d.F.); (A.C.); (A.C.-H.); (F.M.); (T.R.)
| | - Andrea Crespo
- Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain; (C.A.A.); (J.F.d.F.); (A.C.); (A.C.-H.); (F.M.); (T.R.)
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (G.C.G.-T.); (F.V.-V.); (V.B.-G.); (R.H.)
| | - Ana Cerezo-Hernández
- Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain; (C.A.A.); (J.F.d.F.); (A.C.); (A.C.-H.); (F.M.); (T.R.)
| | - Gonzalo C. Gutiérrez-Tobal
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (G.C.G.-T.); (F.V.-V.); (V.B.-G.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (G.C.G.-T.); (F.V.-V.); (V.B.-G.); (R.H.)
| | - Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (G.C.G.-T.); (F.V.-V.); (V.B.-G.); (R.H.)
| | - Fernando Moreno
- Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain; (C.A.A.); (J.F.d.F.); (A.C.); (A.C.-H.); (F.M.); (T.R.)
| | - Tomás Ruiz
- Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain; (C.A.A.); (J.F.d.F.); (A.C.); (A.C.-H.); (F.M.); (T.R.)
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (G.C.G.-T.); (F.V.-V.); (V.B.-G.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| | - Félix del Campo
- Pneumology Department, Río Hortega University Hospital, 47012 Valladolid, Spain; (C.A.A.); (J.F.d.F.); (A.C.); (A.C.-H.); (F.M.); (T.R.)
- Biomedical Engineering Group, University of Valladolid, 47011 Valladolid, Spain; (G.C.G.-T.); (F.V.-V.); (V.B.-G.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 47011 Valladolid, Spain
| |
Collapse
|
3
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
4
|
Andrés-Blanco AM, Álvarez D, Crespo A, Arroyo CA, Cerezo-Hernández A, Gutiérrez-Tobal GC, Hornero R, del Campo F. Assessment of automated analysis of portable oximetry as a screening test for moderate-to-severe sleep apnea in patients with chronic obstructive pulmonary disease. PLoS One 2017; 12:e0188094. [PMID: 29176802 PMCID: PMC5703515 DOI: 10.1371/journal.pone.0188094] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 10/31/2017] [Indexed: 11/19/2022] Open
Abstract
Background The coexistence of obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) leads to increased morbidity and mortality. The development of home-based screening tests is essential to expedite diagnosis. Nevertheless, there is still very limited evidence on the effectiveness of portable monitoring to diagnose OSAS in patients with pulmonary comorbidities. Objective To assess the influence of suffering from COPD in the performance of an oximetry-based screening test for moderate-to-severe OSAS, both in the hospital and at home. Methods A total of 407 patients showing moderate-to-high clinical suspicion of OSAS were involved in the study. All subjects underwent (i) supervised portable oximetry simultaneously to in-hospital polysomnography (PSG) and (ii) unsupervised portable oximetry at home. A regression-based multilayer perceptron (MLP) artificial neural network (ANN) was trained to estimate the apnea-hypopnea index (AHI) from portable oximetry recordings. Two independent validation datasets were analyzed: COPD versus non-COPD. Results The portable oximetry-based MLP ANN reached similar intra-class correlation coefficient (ICC) values between the estimated AHI and the actual AHI for the non-COPD and the COPD groups either in the hospital (non-COPD: 0.937, 0.909–0.956 CI95%; COPD: 0.936, 0.899–0.960 CI95%) and at home (non-COPD: 0.731, 0.631–0.808 CI95%; COPD: 0.788, 0.678–0.864 CI95%). Regarding the area under the receiver operating characteristics curve (AUC), no statistically significant differences (p >0.01) between COPD and non-COPD groups were found in both settings, particularly for severe OSAS (AHI ≥30 events/h): 0.97 (0.92–0.99 CI95%) non-COPD vs. 0.98 (0.92–1.0 CI95%) COPD in the hospital, and 0.87 (0.79–0.92 CI95%) non-COPD vs. 0.86 (0.75–0.93 CI95%) COPD at home. Conclusion The agreement and the diagnostic performance of the estimated AHI from automated analysis of portable oximetry were similar regardless of the presence of COPD both in-lab and at-home. Particularly, portable oximetry could be used as an abbreviated screening test for moderate-to-severe OSAS in patients with COPD.
Collapse
Affiliation(s)
| | - Daniel Álvarez
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Andrea Crespo
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - C. Ainhoa Arroyo
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
| | | | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Félix del Campo
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
- * E-mail:
| |
Collapse
|