FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3175769910> ?p ?o ?g. }

• W3175769910 endingPage "473" @default.
• W3175769910 startingPage "465" @default.
• W3175769910 abstract "•Sleep medicine is cataloged according to a conventional disease classification system. Disease models are rooted in the pathophysiology of sleep. Polysomnography and other tests are used to demonstrate pathophysiological mechanisms underlying the currently known sleep disorders.•Although many patients with sleep disorders may be adequately managed by this pathophysiological approach, therapeutic results are insufficient in some subjects, the causes of which lie in nonspecificity of symptoms, coincidental association between symptoms and pathophysiological endotype, as well as co-occurrence of two or more pathologic mechanisms affecting sleep.•As co-occurrence of different pathogenetic mechanisms may produce phenotypes that are at odds with the idealized description of classic sleep disorders, the result of standard therapeutic interventions may be disappointing.•The mechanisms underlying the expression of certain traits may be a substrate for targeted treatment. Treatable traits are characterized by biomarkers with predictive value as to beneficial treatment response.•The challenge for the future is to gradually embrace the principles of systems medicine and to shift gear toward managing treatable traits in sleep disorders surpassing the limits of the traditional nosologic approach. •Sleep medicine is cataloged according to a conventional disease classification system. Disease models are rooted in the pathophysiology of sleep. Polysomnography and other tests are used to demonstrate pathophysiological mechanisms underlying the currently known sleep disorders.•Although many patients with sleep disorders may be adequately managed by this pathophysiological approach, therapeutic results are insufficient in some subjects, the causes of which lie in nonspecificity of symptoms, coincidental association between symptoms and pathophysiological endotype, as well as co-occurrence of two or more pathologic mechanisms affecting sleep.•As co-occurrence of different pathogenetic mechanisms may produce phenotypes that are at odds with the idealized description of classic sleep disorders, the result of standard therapeutic interventions may be disappointing.•The mechanisms underlying the expression of certain traits may be a substrate for targeted treatment. Treatable traits are characterized by biomarkers with predictive value as to beneficial treatment response.•The challenge for the future is to gradually embrace the principles of systems medicine and to shift gear toward managing treatable traits in sleep disorders surpassing the limits of the traditional nosologic approach. Over the past decades, sleep medicine has evolved as a novel discipline in health care. The development of relevant medical specialties has invariably been preceded by major scientific advances in particular areas of interest. Medical and surgical specialties have traditionally been organized on anatomic or organ-based models in line with growing insight in organ-system physiology and pathology. The taxonomy of human disease dates back to the nineteenth century and is largely ascribed to the work of Sir William Osler, one of the founding fathers of modern medicine.1Loscalzo J. Barabasi A.L. Systems biology and the future of medicine.Wiley Interdiscip Rev Syst Biol Med. 2011; 3: 619-627Crossref PubMed Scopus (178) Google Scholar The classification of diseases by connecting the affected organ system with physiologic, anatomic, and histologic findings has been called the “Oslerian paradigm.”2Vanfleteren L.E. Kocks J.W. Stone I.S. et al.Moving from the Oslerian paradigm to the post-genomic era: are asthma and COPD outdated terms?.Thorax. 2014; 69: 72-79Crossref PubMed Scopus (50) Google Scholar Syndromic patterns and nosologic entities are the building blocks of the Oslerian taxonomy that still prevails in the contemporary classification of human diseases. Later in medical history, cross-sectional disciplines have emerged that are rooted in common biological settings and that integrate different organ systems in a particular context. Relevant “horizontal” disciplines have been developed in age domains (pediatrics and geriatrics), cell biology (oncology), microbiology (infectiology), to name a few. Sleep is an essential biological process that can be readily impaired by pathophysiological mechanisms. Evidently, the various sleep disorders have a common ground underpinning the concept of clinical sleep medicine as we know it today. The technological revolution over the past century has instigated sleep research, thereby disclosing a vast amount of scientific information and producing exquisite tools for diagnosing and treating sleep disorders. This evolution has paved the way for setting up sleep medicine as a medical discipline in its own right.3Shepard Jr., J.W. Buysse D.J. Chesson Jr., A.L. et al.History of the development of sleep medicine in the United States.J Clin Sleep Med. 2005; 1: 61-82Crossref PubMed Scopus (97) Google Scholar In line with this development, curricula in sleep medicine have been established uplifting it on a par with educational standards in other disciplines.4Penzel T. Pevernagie D. Bassetti C. et al.Sleep medicine catalogue of knowledge and skills - Revision.J Sleep Res. 2021; 30: e13394Crossref PubMed Scopus (1) Google Scholar In parallel with the creation of a professional title, textbooks, guidelines, and catalogs for disease classification have been published. The International Classification of Sleep Disorders (ICSD), issued by the American Academy of Sleep Medicine (AASM), is a concise reference book that systematically classifies the currently known disease entities of sleep.5Mayer G. Pevernagie D. Nosological classification and diagnostic strategy.in: Overeem S. Reading P. Sleep disorders in neurology. A practical approach. 2 ed. Wiley-Blackwell, Chichester, UK2018: 41-52Crossref Google Scholar In this manual, sleep disorders are categorized into various domains, including insomnia, sleep-disordered breathing, central hypersomnia, circadian rhythm disorders, parasomnia, sleep-related movement disorders, and miscellaneous conditions. The sleep disorders themselves are described by essential and associated features, predisposing and precipitating factors, natural course, pathophysiology, as well as results from polysomnography (PSG) and other objective tests. Basically, the items listed in the nosologic classification of the ICSD are modeled as disease entities. The disease model consists of a constellation of symptoms and signs complemented with characteristic pathophysiological findings on PSG (and other complementary tests). The merger of clinical findings and observations from diagnostic testing is deemed specific with respect to causality. The connotation of causality is reinforced by adding the term “syndrome” to certain disorders, for example, “sleep apnea syndrome” and “restless legs syndrome.” Moreover, the ICSD provides diagnostic criteria for each nosologic entity. These criteria commonly include a mixture of symptoms, signs, and objective PSG findings. Diagnostic cutoffs are typically based on frequency and/or severity ratings of symptoms and PSG characteristics. Inherently, a cause-consequence relationship is inferred for each sleep disorder listed in the ICSD, the cause being a pathophysiological process demonstrable by PSG or other methods and the consequence being the clinical presentation. However, symptoms and signs overlap between nosologic entities and often correlate poorly with the degree of pathophysiological abnormality assessed by objective tests.6Pevernagie D.A. Gnidovec-Strazisar B. Grote L. et al.On the rise and fall of the apnea-hypopnea index: a historical review and critical appraisal.J Sleep Res. 2020; 29: e13066Crossref PubMed Scopus (40) Google Scholar Not infrequently, the recommended therapy for sleep disorders fails to produce symptomatic relief or is not well tolerated, suggesting that the cause is not really affected by the treatment. In these cases, there are reasons to believe that causality is uncertain and that apparent or concealed confounders influence or determine the outcome. Thus, the question arises whether sleep medicine practice of the future should stick to the conventional “syndromic” approach or rather move to management of clinical traits that are likely to respond to targeted treatment? The AASM has revised the ICSD on several occasions. The nosologic classification of sleep disorders has been adjusted to integrate new scientific data. More emphasis has been placed on the role of pathophysiological observations on PSG. As a consequence, the theoretic concept of certain sleep disorders has evolved. To illustrate some conceptual adaptations over time, the present discussion will focus on the evolution of obstructive sleep apnea (OSA) as a model of chronic sleep disturbance across the consecutive editions of the ICSD, also highlighting some logical errors that inadvertently have crept in. The first edition of the ICSD was published in 1990 by the American Sleep Disorders Association (the predecessor of the AASM).7ASDAObstructive sleep apnea syndrome (780.53-0).in: The international classification of sleep disorders - diagnostic and coding manual. 1 ed. American Sleep Disorders Association, Rochester, MN, USA1990: 52-58Google Scholar The diagnostic criteria for OSA syndrome did not include any count of respiratory events nor the apnea-hypopnea index (AHI), but only a qualitative description: “frequent episodes of obstructed breathing.” The severity criterion was primarily based on the seriousness of symptoms, which was assumed would be reflected in the PSG findings. In 2005, The AASM published the second edition of the ICSD (ICSD-2).8AASMObstructive sleep apnea, adult.in: Sateia M. Hauri P. The international classification of sleep disorders - diagnostic and coding manual. 2 ed. American Academy of Sleep Medicine, Westchester, IL, USA2005: 51-55Google Scholar The new version was at odds with the previous one in that AHI cutoff points were presented as the primary criterion for the definition of OSA. A diagnosis of OSA could be established based on an AHI greater than or equal to 5/h in the presence of symptoms or an AHI greater than or equal to 15/h even without symptoms. The cutoff points were inspired by an earlier AASM publication on OSA syndrome definition and measurement techniques.9AASMSleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. The Report of an American Academy of Sleep Medicine Task Force.Sleep. 1999; 22: 667-689Crossref PubMed Scopus (4622) Google Scholar In this paper, the AHI was introduced as a metric for gauging OSA severity. This proposition was based on a single cross-sectional population survey that showed an association between AHI and prevalent hypertension.10Young T. Peppard P. Palta M. et al.Population-based study of sleep-disordered breathing as a risk factor for hypertension.Arch Intern Med. 1997; 157: 1746-1752Crossref PubMed Google Scholar However, because this study did not include any prospective data at the time of publication, causal inference was scientifically inappropriate. Nevertheless, AHI was from then on accepted as the primary measure of OSA severity. The AHI-driven remodeling of OSA has introduced a converse error (Box 1).11Damer T.E. Attacking faulty reasoning.6 ed. Wadsworth, Belmont, USA2009Google Scholar,12Heller J. Catch-22. Vintage, London, UK1994Google Scholar This error of reasoning reverses the logical order of premise and consequent: subjects with clinically relevant OSA have an increased AHI, but the converse is not necessarily true. The relevance of this error has become evident in epidemiologic research. In a Swiss survey on middle-aged to older people in an urban community, it was shown that the prevalence of an AHI greater than or equal to 15/h amounted to 49.7% in men and 23.4% in women.13Heinzer R. Vat S. Marques-Vidal P. et al.Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study.Lancet Respir Med. 2015; 3: 310-318Abstract Full Text Full Text PDF PubMed Scopus (1159) Google Scholar In most of these people, daytime sleepiness or other symptoms of OSA were absent. Because an increased AHI can be demonstrated in large percentage of asymptomatic subjects in the general population, doubt must be casted on the validity of the AHI as meaningful metric of OSA disease severity.6Pevernagie D.A. Gnidovec-Strazisar B. Grote L. et al.On the rise and fall of the apnea-hypopnea index: a historical review and critical appraisal.J Sleep Res. 2020; 29: e13066Crossref PubMed Scopus (40) Google Scholar Hence it follows that converse error will continue to distort the OSA disease model as long as the AHI is maintained as the prime predictor variable.Box 1Logical relation between clinical presentation and increased apnea-hypopnea index in obstructive sleep apneaA: symptoms and signs that suggest OSAB: increased AHIC: causally related A and B (true positive)D: coincidentally related A and B (false positive)E: A with normal AHIF: B without symptoms and signs that suggest OSAUsing B as a predictor for clinically relevant, treatment responsive OSA brings about fallacies:Set B is a source of the McNamara or quantitative fallacy: AHI is a test result. Although it is commonly used as a metric of disease state, it misrepresents the clinical characteristics and severity of the disorder.Set F is congruent with division fallacy: assuming that an increased AHI invariably represents symptomatic disease is erroneous. Many subjects are asymptomatic.Sets C, D, and F are related to converse error (aka affirming the consequent): patients with true OSA (set C) have an increased AHI—the opposite is not necessarily true.Set D represents association fallacy: false diagnosis of true OSA based on a coincidental association between A and B. A: symptoms and signs that suggest OSAB: increased AHIC: causally related A and B (true positive)D: coincidentally related A and B (false positive)E: A with normal AHIF: B without symptoms and signs that suggest OSAUsing B as a predictor for clinically relevant, treatment responsive OSA brings about fallacies:Set B is a source of the McNamara or quantitative fallacy: AHI is a test result. Although it is commonly used as a metric of disease state, it misrepresents the clinical characteristics and severity of the disorder.Set F is congruent with division fallacy: assuming that an increased AHI invariably represents symptomatic disease is erroneous. Many subjects are asymptomatic.Sets C, D, and F are related to converse error (aka affirming the consequent): patients with true OSA (set C) have an increased AHI—the opposite is not necessarily true.Set D represents association fallacy: false diagnosis of true OSA based on a coincidental association between A and B. Box 1 also illustrates other deficiencies in reasoning related to false associations and assumptions.11Damer T.E. Attacking faulty reasoning.6 ed. Wadsworth, Belmont, USA2009Google Scholar In the ICSD-2, nosologic entities are described as sets of symptoms, signs, and PSG findings representing disease profiles with a common pathophysiological denominator. However, several sleep disorders have heterogeneous manifestations that do not simply fit a disease model cast into a concise set of diagnostic criteria. This reductionist approach may surely apply to certain subgroups, but by no means can it apply to all patients of the entire target group. Division fallacy is wrongly assuming that an individual belonging to a group (eg, subjects with an AHI ≥ 15/h) necessarily show other key characteristics of that group (eg, suffering from daytime sleepiness). It is often assumed that symptoms of OSA and increased AHI are causally related. Because this association can be due to coincidence, it is also a misconception. The quantitative (aka McNamara) fallacy is yet another misconception in which decisions rely solely on one metric, thereby ignoring all other observations.14O'Mahony S. Medicine and the McNamara fallacy.J R Coll Physicians Edinb. 2017; 47: 281-287Crossref PubMed Scopus (24) Google Scholar The presumption that AHI by itself represents a disease state of OSA in a dose-dependent manner is not justified. The correlation between AHI and clinical manifestations is weak at best. Ascribing metric properties to the AHI is obviously an overqualification.6Pevernagie D.A. Gnidovec-Strazisar B. Grote L. et al.On the rise and fall of the apnea-hypopnea index: a historical review and critical appraisal.J Sleep Res. 2020; 29: e13066Crossref PubMed Scopus (40) Google Scholar The third edition of the ICSD (ICSD-3) has further expanded on OSA as a disease model, including mental, metabolic, and cardiovascular comorbidities as intrinsic components of the disorder.15AASMObstructive sleep apnea, adult.in: Sateia M. The international classification of sleep disorders. 3 ed. American Academy of Sleep Medicine, Darien, IL, USA2014: 53-62Google Scholar Yet, no additional evidence was put forward in this edition to support the assumption that the AHI reflects clinical disease severity. Despite this omission, the ICSD-3 has been quoted as a reference for OSA severity rating in a recent clinical guideline on diagnostic testing of OSA published by the AASM.16Kapur V.K. Auckley D.H. Chowdhuri S. et al.Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an American Academy of sleep medicine clinical practice guideline.J Clin Sleep Med. 2017; 13: 479-504Crossref PubMed Scopus (851) Google Scholar The ICSD-3 takes the disease model even one step further, deleting the ultimate criterion described in IDSC-2 that “the disorder [ie OSA] is not better explained by another current sleep disorder, medical or neurologic disorder, medication use, or substance use disorder.” The ICSD-3 clearly excludes the need for differential diagnosis and, as such, endorses association fallacy. The aforementioned fallacies may have far-reaching consequences for clinical research and daily practice. There is as yet no gold standard (or “ground truth”) to define the real disease state of OSA. The causative role of OSA in provoking symptoms and signs is hard to ascertain, even in the presence of high AHI values. As mentioned earlier, a coincidental association between an indicative clinical picture and increased AHI may be labeled as “false positive” OSA. In clinical practice, false-positively labeled patients with OSA will experience little benefit of therapy and may show poor adherence. Trying to optimize compliance to therapy in these individuals will not improve the clinical results. In clinical research, the outcomes of randomized controlled trials may be blurred by mixing up false-positive and true-positive OSA. The customary inclusion criterion of AHI greater than threshold will include both categories without knowing who’s who. Obviously, the AHI bias will have to be addressed to improve future results in both clinical practice and research.17Randerath W. Bassetti C.L. Bonsignore M.R. et al.Challenges and perspectives in obstructive sleep apnoea: Report by an ad hoc working group of the sleep disordered breathing group of the European respiratory Society and the European sleep research Society.Eur Respir J. 2018; 52: 1702616Crossref PubMed Scopus (88) Google Scholar Chronic diseases are hallmarked by multicomponent and nonlinear pathologic processes and are ill-suited to be comprehended by reductionist models.18Ahn A.C. Tewari M. Poon C.S. et al.The limits of reductionism in medicine: could systems biology offer an alternative?.Plos Med. 2006; 3: e208Crossref PubMed Scopus (301) Google Scholar Instead, systems science offers tools to effectively treat susceptible traits at an individual level. Alvar Agusti has pioneered such approach in asthma and chronic obstructive pulmonary disease (COPD), two highly prevalent and disabling chronic diseases.19Agusti A. Bel E. Thomas M. et al.Treatable traits: toward precision medicine of chronic airway diseases.Eur Respir J. 2016; 47: 410-419Crossref PubMed Scopus (484) Google Scholar Taking obstructive lung disease as a starting point, he has elegantly described the historical transition of medical reasoning. Disease management, traditionally based on pathology- and pathophysiology-oriented diagnosis, had at some point to be fine-tuned. The first step was to identify differential disease attributes in patients with a common diagnosis allowing stratification into clinical phenotypes. However, such subclassification also proved insufficient to predict therapeutic effects and prognosis within the stratified subgroups. Eventually, it became obvious that a subsequent step had to be taken and that assessment of disease characteristics was required at the individual level.20Agusti A. Phenotypes and disease characterization in chronic obstructive pulmonary disease. Toward the extinction of phenotypes?.Ann Am Thorac Soc. 2013; 10: S125-S130Crossref PubMed Scopus (37) Google Scholar At present, personalized (aka “precision”) medicine is proposed as the ultimate paradigm to overcome the limitations of the former strategies. It is defined as “treatments targeted to the needs of individual patients on the basis of genetic, biomarker, phenotypic or psychosocial characteristics that distinguish a given patient from other patients with similar clinical presentations.”21Agusti A. The path to personalised medicine in COPD.Thorax. 2014; 69: 857-864Crossref PubMed Scopus (111) Google Scholar The main objective of precision medicine is to “improve clinical outcomes for individual patients while minimizing unnecessary side effects for those less likely to respond to a given treatment.”22Jameson J.L. Longo D.L. Precision medicine--personalized, problematic, and promising.N Engl J Med. 2015; 372: 2229-2234Crossref PubMed Scopus (562) Google Scholar The rationale for assuming personalized medicine is the observation that chronic diseases are “complex” and “heterogeneous.” In this setting, “complex” means that they have several components with nonlinear dynamic interactions, whereas “heterogeneous” indicates that not all of these components are present in all patients or, in a given patient, at all timepoints.21Agusti A. The path to personalised medicine in COPD.Thorax. 2014; 69: 857-864Crossref PubMed Scopus (111) Google Scholar An explanation of these concepts is presented in Table 1.Table 1Evolution of the medical disease conceptType of Disease ManagementUnderlying ConceptClinical ImplicationsManagementTraditional medicineMonodimensional, uniform disease processesSyndromic approach: a common denominator of observed symptoms, signs, and pathologic markers defines the illness“One size fits all”Stratified medicineHeterogeneity within nosologic entitiesPhenotyping, stratification of subtypes“One size fits every subtype”Personalized medicineHeterogeneity plus complexity within nosologic entitiesMultiple causes or pathologic processes may underly discrete phenotypes. Discrimination between conventional nosologic entities becomes less obviousLabel-free, targeted therapy for treatment-responsive traits Open table in a new tab The transition to personalized medicine is rooted in systems biology.23Ahn A.C. Tewari M. Poon C.S. et al.The clinical applications of a systems approach.Plos Med. 2006; 3: e209Crossref PubMed Scopus (143) Google Scholar This scientific domain studies the complex time, space, and context-sensitive interactions of the vast amount of components that constitute a biological system. Information is lost by zooming in on the individual components. In order to gain new insights, the dynamics of the entire system must be analyzed at an integrated meta-level. Analytical methods are derived from systems engineering and big data science. The transposition of systems biology to the scientific domain of medicine is called “systems medicine” or “network medicine.”24Agusti A. Celli B. Faner R. What does endotyping mean for treatment in chronic obstructive pulmonary disease?.Lancet. 2017; 390: 980-987Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar The intricate interaction of processes at environmental, clinical, biological, and genetic levels ultimately defines the clinical outcomes. In brief, the study of these complex mechanisms is grouped into basic domains covering genotypes, endotypes, and phenotypes (Table 2).24Agusti A. Celli B. Faner R. What does endotyping mean for treatment in chronic obstructive pulmonary disease?.Lancet. 2017; 390: 980-987Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar Meanwhile, the principles of precision medicine have been reviewed and deemed appropriate for common practice by opinion leaders of the sleep medicine community.25Pack A.I. Application of personalized, predictive, Preventative, and Participatory (P4) medicine to obstructive sleep apnea. A Roadmap for improving Care?.Ann Am Thorac Soc. 2016; 13: 1456-1467Crossref PubMed Scopus (37) Google Scholar,26Martinez-Garcia M.A. Campos-Rodriguez F. Barbe F. et al.Precision medicine in obstructive sleep apnoea.Lancet Respir Med. 2019; 7: 456-464Abstract Full Text Full Text PDF PubMed Scopus (46) Google ScholarTable 2Definitions in systems medicineTermMeaningExposomeThe cumulative/lifelong environmental exposures including smoking, pollution, noxious substances, infections, and diet.GenomeThe total composition of genes in a cell defining the genetic make-up of an organism or individual.EpigeneticsMolecular mechanisms/multilevel biological networks that dynamically modulate the outcome of gene–environment interactions.GenotypeThe part of the genome (ie, a gene or set of genes) that codes for the characteristics of an organism or individual, determining the phenotype through the intermediary pathway of endotypes.EndotypeThe subtype of a condition that has a distinct molecular, functional, or pathobiological mechanism. Studying endotypes allows mechanistic approaches to disease stratification and treatment beyond the clinical presentation of the disease.PhenotypeObservable characteristics of an organism or individual in health and disease. A combination of disease features, in relation to clinically meaningful attributes (symptoms, response to therapy, health outcomes, quality of life).TraitA particular characteristic such as an endotype or clinical subtype. A treatable trait is a therapeutic target identified by recognition of phenotype or endotype through validated biomarkers.BiomarkerA measurable indicator (biological molecule in body fluids as well as physiologic phenomenon) used to gauge a particular biological or pathogenic process or response to treatment. Validated biomarkers may be reliable surrogates for certain endotypes or phenotypes.ClusterA set of characteristics that together point at a common cause or pathogenic mechanism Open table in a new tab Endotypes and phenotypes have been studied intensively over the past decade, especially in the domain of sleep-disordered breathing.27Edwards B.A. Redline S. Sands S.A. et al.More than the Sum of the respiratory events: personalized medicine approaches for obstructive sleep apnea.Am J Respir Crit Care Med. 2019; 200: 691-703Crossref PubMed Scopus (45) Google Scholar An endotype denotes a particular mechanism that causes a physiologic or metabolic disturbance in certain organ systems. In OSA, several physiologic endotypes have been observed that, together, shape the disturbed breathing process. Members from the Division of Sleep Medicine at the Brigham and Women’s Hospital (Harvard Medical School) have explored the pathophysiological mechanisms of sleep-disordered breathing.28Eckert D.J. White D.P. Jordan A.S. et al.Defining phenotypic causes of obstructive sleep apnea. Identification of novel therapeutic targets.Am J Respir Crit Care Med. 2013; 188: 996-1004Crossref PubMed Scopus (538) Google Scholar Briefly, they demonstrated that several factors play a role in upper airway obstruction, including an anatomic feature predisposing to collapsibility of the upper airway, and nonanatomical traits (genioglossus muscle responsiveness, arousal threshold, and respiratory control stability—loop gain). In subsequent studies it was found that the nonanatomical pathophysiological traits are suitable to therapeutic options other than the conventional application of positive airway pressure therapy or the use of oral appliances.29Carberry J.C. Amatoury J. Eckert D.J. Personalized management approach for OSA.Chest. 2018; 153: 744-755Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar More specifically, supplemental oxygen therapy or carbo-anhydrase inhibitors may be effective at reducing loop gain, whereas hypnotics may increase the arousal threshold, and upper airway muscle training or hypoglossal nerve stimulation may compensate for insufficient genioglossus muscle responsiveness.27Edwards B.A. Redline S. Sands S.A. et al.More than the Sum of the respiratory events: personalized medicine approaches for obstructive sleep apnea.Am J Respir Crit Care Med. 2019; 200: 691-703Crossref PubMed Scopus (45) Google Scholar,30Eckert D.J. Phenotypic approaches to obstructive sleep apnoea - new pathways for targeted therapy.Sleep Med Rev. 2018; 37: 45-59Crossref PubMed Scopus (161) Google Scholar,31Owens R.L. Edwards B.A. Eckert D.J. et al.An integrative model of physiological traits can be used to predict obstructive sleep apnea and response to non positive airway pressure therapy.Sleep. 2015; 38: 961-970PubMed Google Scholar Evidence is accumulating that physiologically targeted treatment of OSA may effectively decrease the AHI and thu" @default.
• W3175769910 created "2021-07-05" @default.
• W3175769910 creator A5005381750 @default.
• W3175769910 date "2021-09-01" @default.
• W3175769910 modified "2023-09-27" @default.
• W3175769910 title "Future Treatment of Sleep Disorders" @default.
• W3175769910 cites W1192179325 @default.
• W3175769910 cites W1859222707 @default.
• W3175769910 cites W1977209720 @default.
• W3175769910 cites W2011114870 @default.
• W3175769910 cites W2088546494 @default.
• W3175769910 cites W2100799391 @default.
• W3175769910 cites W2106065407 @default.
• W3175769910 cites W2126989218 @default.
• W3175769910 cites W2127030366 @default.
• W3175769910 cites W2148687419 @default.
• W3175769910 cites W2153401815 @default.
• W3175769910 cites W2155581408 @default.
• W3175769910 cites W2159528797 @default.
• W3175769910 cites W2263808047 @default.
• W3175769910 cites W2264784497 @default.
• W3175769910 cites W2520950502 @default.
• W3175769910 cites W2529555766 @default.
• W3175769910 cites W2530070824 @default.
• W3175769910 cites W2565665126 @default.
• W3175769910 cites W2612940703 @default.
• W3175769910 cites W2625656571 @default.
• W3175769910 cites W2750811425 @default.
• W3175769910 cites W2783096435 @default.
• W3175769910 cites W2783783250 @default.
• W3175769910 cites W2792713223 @default.
• W3175769910 cites W2792736409 @default.
• W3175769910 cites W2807328633 @default.
• W3175769910 cites W2902751862 @default.
• W3175769910 cites W2912348760 @default.
• W3175769910 cites W2923553331 @default.
• W3175769910 cites W2939643448 @default.
• W3175769910 cites W2941872426 @default.
• W3175769910 cites W2981981172 @default.
• W3175769910 cites W3011508341 @default.
• W3175769910 cites W3024624385 @default.
• W3175769910 cites W3123484882 @default.
• W3175769910 cites W3164466463 @default.
• W3175769910 cites W402335930 @default.
• W3175769910 cites W79074324 @default.
• W3175769910 doi "https://doi.org/10.1016/j.jsmc.2021.05.005" @default.
• W3175769910 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34325823" @default.
• W3175769910 hasPublicationYear "2021" @default.
• W3175769910 type Work @default.
• W3175769910 sameAs 3175769910 @default.
• W3175769910 citedByCount "9" @default.
• W3175769910 countsByYear W31757699102021 @default.
• W3175769910 countsByYear W31757699102022 @default.
• W3175769910 countsByYear W31757699102023 @default.
• W3175769910 crossrefType "journal-article" @default.
• W3175769910 hasAuthorship W3175769910A5005381750 @default.
• W3175769910 hasBestOaLocation W31757699101 @default.
• W3175769910 hasConcept C111919701 @default.
• W3175769910 hasConcept C177713679 @default.
• W3175769910 hasConcept C2775841894 @default.
• W3175769910 hasConcept C41008148 @default.
• W3175769910 hasConcept C71924100 @default.
• W3175769910 hasConceptScore W3175769910C111919701 @default.
• W3175769910 hasConceptScore W3175769910C177713679 @default.
• W3175769910 hasConceptScore W3175769910C2775841894 @default.
• W3175769910 hasConceptScore W3175769910C41008148 @default.
• W3175769910 hasConceptScore W3175769910C71924100 @default.
• W3175769910 hasFunder F4320320300 @default.
• W3175769910 hasFunder F4320332999 @default.
• W3175769910 hasFunder F4320335254 @default.
• W3175769910 hasIssue "3" @default.
• W3175769910 hasLocation W31757699101 @default.
• W3175769910 hasLocation W31757699102 @default.
• W3175769910 hasLocation W31757699103 @default.
• W3175769910 hasOpenAccess W3175769910 @default.
• W3175769910 hasPrimaryLocation W31757699101 @default.
• W3175769910 hasRelatedWork W1506200166 @default.
• W3175769910 hasRelatedWork W1995515455 @default.
• W3175769910 hasRelatedWork W2048182022 @default.
• W3175769910 hasRelatedWork W2080531066 @default.
• W3175769910 hasRelatedWork W2604872355 @default.
• W3175769910 hasRelatedWork W2748952813 @default.
• W3175769910 hasRelatedWork W2899084033 @default.
• W3175769910 hasRelatedWork W3031052312 @default.
• W3175769910 hasRelatedWork W3032375762 @default.
• W3175769910 hasRelatedWork W3108674512 @default.
• W3175769910 hasVolume "16" @default.
• W3175769910 isParatext "false" @default.
• W3175769910 isRetracted "false" @default.
• W3175769910 magId "3175769910" @default.
• W3175769910 workType "article" @default.