Recognizable clinical subtypes of obstructive sleep apnea across international sleep centers: a cluster analysis

Keenan, Brendan T.; Kim, Jin‐Young; Singh, Bhajan; Bittencourt, Lia; Chen, Ning Hung; Cistulli, Peter A.; Magalang, Ulysses J.; McArdle, Nigel; Mindel, Jesse; Benediktsdóttir, Bryndís; Arnardóttir, Erna Sif; Prochnow, Lisa; Penzel, Thomas; Sanner, Bernd; Schwab, Richard J.; Shin, Chol; Sutherland, Kate; Tufik, Sérgio; Maislin, Greg; Gíslason, Þórarinn; Pack, Allan I.

doi:10.1093/sleep/zsx214

Cited by 176 publications

(164 citation statements)

References 49 publications

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“…Unsupervised learning draws inferences from data sets without labelled responses. They identify intrinsic groupings in data and have provided evidence of the existence of different OSA subtypes . Supervised classification machine learning methods require labelled data sets to train the prediction models.…”

Section: Key Enablers For the Development Of Novel Analysis Methodsmentioning

confidence: 99%

“…They identify intrinsic groupings in data and have provided evidence of the existence of different OSA subtypes. 35,77 Supervised classification machine learning methods require labelled data sets to train the prediction models. A standard approach for supervised machine learning methods is to first use a data preprocessing (or feature extraction) step which processes the available signals (e.g.…”

Section: Machine Learningmentioning

confidence: 99%

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Advanced polysomnographic analysis for OSA: A pathway to personalized management?

2019

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Obstructive sleep apnea (OSA) is a highly heterogeneous disorder, with diverse pathways to disease, expression of disease, susceptibility to co‐morbidities and response to therapy, and is ideally suited to precision medicine approaches. Clinically, the content of the information‐rich polysomnogram (PSG) is not currently fully utilized in determining patient management. Novel PSG parameters such as hypoxic burden, pulse transit time, cardiopulmonary coupling and the frequency representations of PSG sensor signals could predict a variety of cardiovascular disease, cancer and neurodegeneration co‐morbidities. The PSG can also be used to identify key pathophysiological parameters such as loop gain, arousal threshold and muscle compensation which can enhance understanding of the causes of OSA in an individual, and thereby guide choices on therapy. Machine learning methods performing their own parameter extraction coupled with large PSG data sets offer an exciting opportunity for discovering new links between the PSG variables and disease outcomes. By exploiting existing and emerging analytical methods, the PSG may offer a pathway to personalized management for OSA.

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Section: Key Enablers For the Development Of Novel Analysis Methodsmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

2019

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“…OSA is known to be related to many serious health problems including cardiovascular and metabolic morbidity and mortality [22,25,34]. Both symptoms and comorbidities vary significantly at diagnosis but data regarding longitudinal clustering are scarce [21]. Thus, identifying subgroups of patients that share the similar disease characteristics such as comorbidities, indices of OSA severity, and symptoms is an important step for personalizing OSA treatments [1].…”

Section: Extensionsmentioning

confidence: 99%

“…OSA is associated with many health problems such as cardiovascular and metabolic diseases [22] including diabetes [35], coronary heart diseases [16], cancer [5] with finally an increased risk of mortality [5]. It is also known a heterogeneous disease with different symptoms and comorbidities for patients exhibiting the same level of OSA severity [21]. Thus, recent studies aim at better allocate patients into well-defined subgroups (i.e., phenotypes) based on clinical information such as symptoms, comorbidities, and demographics using clustering methods [20,39,41].…”

Section: Applicationmentioning

confidence: 99%

“…For example, in [44], a Latent Class Analysis (LCA) is used to identify groups of patients in the Icelandic Sleep Apnea Cohort including 822 patients with moderate-to-severe OSA. A similar strategy is employed for 922 patients recruited from the Sleep Apnea Global Interdisciplinary Consortium (SAGIC) [21]. Hierarchical clustering is employed in [1] for analyzing diagnosis data of 18,263 OSA patients.…”

Section: Applicationmentioning

confidence: 99%

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Evolutionary Active Constrained Clustering for Obstructive Sleep Apnea Analysis

Amer-Yahia

Bailly

et al. 2018

Data Sci. Eng.

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We introduce a novel interactive framework to handle both instance-level and temporal smoothness constraints for clustering large longitudinal data and for tracking the cluster evolutions over time. It consists of a constrained clustering algorithm, called CVQE+, which optimizes the clustering quality, constraint violation and the historical cost between consecutive data snapshots. At the center of our framework is a simple yet effective active learning technique, named Border, for iteratively selecting the most informative pairs of objects to query users about, and updating the clustering with new constraints. Those constraints are then propagated inside each data snapshot and between snapshots via two schemes, called constraint inheritance and constraint propagation, to further enhance the results. Moreover, a historical constraint is enforced between consecutive snapshots to ensure the consistency of results among them. Experiments show better or comparable clustering results than state-of-the-art techniques as well as high scalability for large datasets. Finally, we apply our algorithm for clustering phenotypes in patients with Obstructive Sleep Apnea as well as for tracking how these clusters evolve over time.

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International Consensus Statement on Obstructive Sleep Apnea

Chang

Goldberg

Alt

et al. 2023

Int Forum Allergy Rhinol

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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 evidencebased 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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Recognizable clinical subtypes of obstructive sleep apnea across international sleep centers: a cluster analysis

Abstract: Results confirm and extend previously identified clinical clusters in OSA. These clusters provide an opportunity for a more personalized approach to the management of OSA.

Cited by 176 publications

References 49 publications

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

Evolutionary Active Constrained Clustering for Obstructive Sleep Apnea Analysis

International Consensus Statement on Obstructive Sleep Apnea

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