SUMMARY Identifying predictors of subjective sleepiness and severity of sleep apnea are important yet challenging goals in sleep medicine. Classification algorithms may provide insights, especially when large data sets are available. We analyzed polysomnography and clinical features available from the Sleep Heart Health Study. The Epworth Sleepiness Scale and the apnea–hypopnea index were the targets of three classifiers: k-nearest neighbor, naive Bayes and support vector machine algorithms. Classification was based on up to 26 features including demographics, polysomnogram, and electrocardiogram (spectrogram). Naive Bayes was best for predicting abnormal Epworth class (0–10 versus 11–24), although prediction was weak: polysomnogram features had 16.7% sensitivity and 88.8% specificity; spectrogram features had 5.3% sensitivity and 96.5% specificity. The support vector machine performed similarly to naive Bayes for predicting sleep apnea class (0–5 versus >5): 59.0% sensitivity and 74.5% specificity using clinical features and 43.4% sensitivity and 83.5% specificity using spectrographic features compared with the naive Bayes classifier, which had 57.5% sensitivity and 73.7% specificity (clinical), and 39.0% sensitivity and 82.7% specificity (spectrogram). Mutual information analysis confirmed the minimal dependency of the Epworth score on any feature, while the apnea–hypopnea index showed modest dependency on body mass index, arousal index, oxygenation and spectrogram features. Apnea classification was modestly accurate, using either clinical or spectrogram features, and showed lower sensitivity and higher specificity than common sleep apnea screening tools. Thus, clinical prediction of sleep apnea may be feasible with easily obtained demographic and electrocardiographic analysis, but the utility of the Epworth is questioned by its minimal relation to clinical, electrocardiographic, or polysomnographic features.
Study Objectives: Determining the presence and severity of obstructive sleep apnea (OSA) is based on apnea and hypopnea event rates per hour of sleep. Making this determination presents a diagnostic challenge, given that summary metrics do not consider certain factors that infl uence severity, such as body position and the composition of sleep stages. Methods: We retrospectively analyzed 300 consecutive diagnostic PSGs performed at our center to determine the impact of body position and sleep stage on sleep apnea severity. Results:The median percent of REM sleep was 16% (reduced compared to a normal value of ~25%). The median percent supine sleep was 65%. Fewer than half of PSGs contained > 10 min in each of the 4 possible combinations of REM/NREM and supine/non-supine. Half of patients had > 2-fold worsening of the apnea-hypopnea index (AHI) in REM sleep, and 60% had > 2-fold worsening of AHI while supine. Adjusting for body position had greater impact on the AHI than adjusting for reduced REM%. Misclassifi cation-specifi cally underestimation of OSA severity-is attributed more commonly to body position (20% to 40%) than to sleep stage (~10%). Conclusions: Supine-dominance and REM-dominance commonly contribute to AHI underestimation in single-night PSGs. Misclassifi cation of OSA severity can be mitigated in a patientspecifi c manner by appropriate consideration of these vari- S C I E N T I F I C I N V E S T I G A T I O N ST he diagnosis of obstructive sleep apnea (OSA) and its severity categorization are typically based on the apneahypopnea index (AHI) obtained from a single overnight laboratory polysomnogram (PSG). Large studies have shown that OSA is associated with cerebrovascular and cardiovascular morbidity and mortality in proportion to severity.1-3 Accurate assignment of apnea severity is therefore important to establish the diagnosis and to motivate treatment decisions. In addition to patient-specifi c considerations for individual care, accurate assessment of OSA severity is important at the population level for establishing genetic, epidemiological, and medical associations with OSA. Despite the importance of accurate diagnostic assessment, obtaining this information is challenging given that sleep apnea is a complex process with multiple contributing factors, some of which vary over time. Providers may thus be left uncertain about how to interpret the presence or severity of OSA after a single night's examination of sleep.Current guidelines suggest offering treatment to patients with AHI values ≥ 5/h (with daytime symptoms or snoring), or > 15/h regardless of symptoms.4,5 The AHI does not capture other details about apnea physiology such as event duration or depth of desaturation but has been accepted as a gateway to diagnosis and treatment. One important question arises with respect to the AHI from a single night: what is the likelihood that an observed AHI value < 5/h or < 15/h would have been higher if more REM sleep or more supine sleep had occurred? A single "negative" study may not be suffi c...
Summary Sleep fragmentation of any cause is disruptive to the rejuvenating value of sleep. However, methods to quantify sleep architecture remain limited. We have previously shown that human sleep–wake stage distributions exhibit multi-exponential dynamics, which are fragmented by obstructive sleep apnea (OSA), suggesting that Markov models may be a useful method to quantify architecture in health and disease. Sleep stage data were obtained from two subsets of the Sleep Heart Health Study database: control subjects with no medications, no OSA, no medical co-morbidities and no sleepiness (n = 374); and subjects with severe OSA (n = 338). Sleep architecture was simplified into three stages: wake after sleep onset (WASO); non-rapid eye movement (NREM) sleep; and rapid eye movement (REM) sleep. The connectivity and transition rates among eight ‘generator’ states of a first-order continuous-time Markov model were inferred from the observed (‘phenotypic’) distributions: three exponentials each of NREM sleep and WASO; and two exponentials of REM sleep. Ultradian REM cycling was accomplished by imposing time-variation to REM state entry rates. Fragmentation in subjects with severe OSA involved faster transition probabilities as well as additional state transition paths within the model. The Markov models exhibit two important features of human sleep architecture: multi-exponential stage dynamics (accounting for observed bout distributions); and probabilistic transitions (an inherent source of variability). In addition, the model quantifies the fragmentation associated with severe OSA. Markov sleep models may prove important for quantifying sleep disruption to provide objective metrics to correlate with endpoints ranging from sleepiness to cardiovascular morbidity.
The goal of this work is to present information theory, specifically Claude Shannon's mathematical theory of communication, in a clinical context and elucidate its potential contributions to understanding the process of diagnostic inference. We use probability theory, information theory, and clinical examples to develop information theory as a means to examine uncertainty in diagnostic testing situations. We begin our discussion with a brief review of probability theory as it relates to diagnostic testing. An outline of Shannon's theory of communication theory and how it directly translates to the medical diagnostic process serves as the essential justification for this article. Finally, we introduce the mathematical tools of information theory that allow for an understanding of diagnostic uncertainty and test effectiveness in a variety of contexts. We show that information theory provides a quantitative framework for understanding uncertainty that readily extends to medical diagnostic contexts.
Diagnostic test interpretation remains a challenge in clinical practice. Most physicians receive training in the use of Bayes’ rule, which specifies how the sensitivity and specificity of a test for a given disease combine with the pre-test probability to quantify the change in disease probability incurred by a new test result. However, multiple studies demonstrate physicians’ deficiencies in probabilistic reasoning, especially with unexpected test results. Information theory, a branch of probability theory dealing explicitly with the quantification of uncertainty, has been proposed as an alternative framework for diagnostic test interpretation, but is even less familiar to physicians. We have previously addressed one key challenge in the practical application of Bayes theorem: the handling of uncertainty in the critical first step of estimating the pre-test probability of disease. This essay aims to present the essential concepts of information theory to physicians in an accessible manner, and to extend previous work regarding uncertainty in pre-test probability estimation by placing this type of uncertainty within a principled information theoretic framework. We address several obstacles hindering physicians’ application of information theoretic concepts to diagnostic test interpretation. These include issues of terminology (mathematical meanings of certain information theoretic terms differ from clinical or common parlance) as well as the underlying mathematical assumptions. Finally, we illustrate how, in information theoretic terms, one can understand the effect on diagnostic uncertainty of considering ranges instead of simple point estimates of pre-test probability.
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