Enhancements to the multiple sleep latency test

Shorter respiratory event duration is related to prevalence of type 2 diabetes

BACKGROUND

Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder often comorbid with metabolic diseases, and type 2 diabetes (T2DM) is one of them. Although apnea hypopnea index (AHI) is currently the diagnostic criteria for OSA severity, a controversial relationship between AHI and T2DM has been found. On the other hand, the duration of apnea-hypopnea events has been shown to be a useful metric for predicting mortality. This study aimed to test whether average respiratory event duration was associated with prevalence of T2DM.

METHODS

Patients referred to the sleep clinic were recruited in the study. Baseline clinical characteristics and polysomnography parameters including average respiratory event duration were collected. The association of average respiratory event duration with the prevalence of T2DM was evaluated by univariate and multivariate logistic regression analyses.

RESULTS

A total of 260 participants were enrolled, and 92 (35.4%) had T2DM. Univariate analysis revealed that age, body mass index (BMI), total sleep time, sleep efficiency, history of hypertension, and shorter average respiratory event duration were associated with T2DM. In multivariate analysis, only age and BMI remained significant. While average respiratory event duration was insignificant in multivariate analysis, subtype event analysis showed that shorter average apnea duration was both significant in univariate (OR, 0.95; 95% CI, 0.92-0.98) and multivariate analyses (OR, 0.95; 95% CI, 0.91-0.99). Neither average hypopnea duration nor AHI was associated with T2DM. Significant association (OR, 1.19; 95% CI, 1.12-1.25) was observed between shorter average apnea duration and lower respiratory arousal threshold after multivariate adjustment. However, causal mediation analysis revealed no mediating effect of arousal threshold on average apnea duration and T2DM.

CONCLUSION

The average apnea duration may be a useful metric in the diagnosis of OSA comorbidity. Shorter average apnea duration indicating poor sleep quality and augmented autonomic nervous system responses might be the potential pathological mechanisms leading to T2DM.

An Ultra-Short Measure of Excessive Daytime Sleepiness Is Related to Circadian Biological Rhythms: The French Psychometric Validation of the Barcelona Sleepiness Index

The Barcelona Sleepiness Index (BSI) is an ultra-short instrument with several advantages for assessing excessive daytime sleepiness (EDS). The present study was performed to analyze the validity of the French version of the BSI in screening for EDS. We conducted a cross-sectional study on a population of students using an online questionnaire. The French version of the BSI was developed by a rigorous forward-backward translation process. We computed the discrimination properties of the BSI to predict EDS assessed by the Epworth Sleepiness Scale (ESS), as well as correlations with other sleep measures. In total, 662 students were enrolled in the study (mean age: 20.9 years, 76.0% women). The BSI score (mean = 1.5 ± 1.0) showed a strong correlation with the ESS score (r = 0.47, p < 10−4) and acceptable discrimination of EDS assessed by ESS score ≥ 11 (AUC = 0.742) with an optimal cutoff point of 2, as in the original study. The BSI score was significantly associated with sleep deprivation and social jetlag. Therefore, the French version of the BSI is a valid ultra-short instrument for EDS screening in individuals. In addition, the BSI score may be associated with both homeostatic and circadian processes. Further studies are needed to confirm these findings in general populations and in patients with sleep disorders.

Reinventing polysomnography in the age of precision medicine

For almost 50 years, sleep laboratories around the world have been collecting massive amounts of polysomnographic (PSG) physiological data to diagnose sleep disorders, the majority of which are not utilized in the clinical setting. Only a small fraction of the information available within these signals is utilized to generate indices. For example, the apnea-hypopnea index (AHI) remains the primary tool for diagnostic and therapeutic decision-making for obstructive sleep apnea (OSA) despite repeated studies showing it to be inadequate in predicting clinical consequences. Today, there are many novel approaches to PSG signals, making it possible to extract more complex metrics and analyses that are potentially more clinically relevant for individual patients. However, the pathway to implement novel PSG metrics/analyses into routine clinical practice is unclear. Our goal with this review is to highlight some of the novel PSG metrics/analyses that are becoming available. We suggest that stronger academic-industry relationships would facilitate the development of state-of-the-art clinical research to establish the value of novel PSG metrics/analyses in clinical sleep medicine. Collectively, as a sleep community, it is time to reinvent how we utilize the polysomnography to move us towards Precision Sleep Medicine.

Reliability of the American Academy of Sleep Medicine Rules for Assessing Sleep Depth in Clinical Practice

STUDY OBJECTIVES

The American Academy of Sleep Medicine has published manuals for scoring polysomnograms that recommend time spent in non-rapid eye movement sleep stages (stage N1, N2, and N3 sleep) be reported. Given the well-established large interrater variability in scoring stage N1 and N3 sleep, we determined the range of time in stage N1 and N3 sleep scored by a large number of technologists when compared to reasonably estimated true values.

METHODS

Polysomnograms of 70 females were scored by 10 highly trained sleep technologists, two each from five different academic sleep laboratories. Range and confidence interval (CI = difference between the 5th and 95th percentiles) of the 10 times spent in stage N1 and N3 sleep assigned in each polysomnogram were determined. Average values of times spent in stage N1 and N3 sleep generated by the 10 technologists in each polysomnogram were considered representative of the true values for the individual polysomnogram. Accuracy of different technologists in estimating delta wave duration was determined by comparing their scores to digitally determined durations.

RESULTS

The CI range of the ten N1 scores was 4 to 39 percent of total sleep time (% TST) in different polysomnograms (mean CI ± standard deviation = 11.1 ± 7.1 % TST). Corresponding range for N3 was 1 to 28 % TST (14.4 ± 6.1 % TST). For stage N1 and N3 sleep, very low or very high values were reported for virtually all polysomnograms by different technologists. Technologists varied widely in their assignment of stage N3 sleep, scoring that stage when the digitally determined time of delta waves ranged from 3 to 17 seconds.

CONCLUSIONS

Manual scoring of non-rapid eye movement sleep stages is highly unreliable among highly trained, experienced technologists. Measures of sleep continuity and depth that are reliable and clinically relevant should be a focus of clinical research.

Minimizing Interrater Variability in Staging Sleep by Use of Computer-Derived Features

STUDY OBJECTIVES

Inter-scorer variability in sleep staging of polysomnograms (PSGs) results primarily from difficulty in determining whether: (1) an electroencephalogram pattern of wakefulness spans > 15 sec in transitional epochs, (2) spindles or K complexes are present, and (3) duration of delta waves exceeds 6 sec in a 30-sec epoch. We hypothesized that providing digitally derived information about these variables to PSG scorers may reduce inter-scorer variability.

METHODS

Fifty-six PSGs were scored (five-stage) by two experienced technologists, (first manual, M1). Months later, the technologists edited their own scoring (second manual, M2). PSGs were then scored with an automatic system and the same two technologists and an additional experienced technologist edited them, epoch-by-epoch (Edited-Auto). This resulted in seven manual scores for each PSG. The two M2 scores were then independently modified using digitally obtained values for sleep depth and delta duration and digitally identified spindles and K complexes.

RESULTS

Percent agreement between scorers in M2 was 78.9 ± 9.0% before modification and 96.5 ± 2.6% after. Errors of this approach were defined as a change in a manual score to a stage that was not assigned by any scorer during the seven manual scoring sessions. Total errors averaged 7.1 ± 3.7% and 6.9 ± 3.8% of epochs for scorers 1 and 2, respectively, and there was excellent agreement between the modified score and the initial manual score of each technologist.

CONCLUSIONS

Providing digitally obtained information about sleep depth, delta duration, spindles and K complexes during manual scoring can greatly reduce interrater variability in sleep staging by eliminating the guesswork in scoring epochs with equivocal features.