Recommended assessment and management of sleep disordered breathing in patients with atrial fibrillation, hypertension and heart failure: Taiwan Society of Cardiology/Taiwan Society of sleep Medicine/Taiwan Society of pulmonary and Critical Care Medicine joint consensus statement.

Sleep disordered breathing (SDB) is highly prevalent and may be linked to cardiovascular disease in a bidirectional manner. The Taiwan Society of Cardiology, Taiwan Society of Sleep Medicine and Taiwan Society of Pulmonary and Critical Care Medicine established a task force of experts to evaluate the evidence regarding the assessment and management of SDB in patients with atrial fibrillation (AF), hypertension and heart failure with reduced ejection fraction (HFrEF). The GRADE process was used to assess the evidence associated with 15 formulated questions. The task force developed recommendations and determined strength (Strong, Weak) and direction (For, Against) based on the quality of evidence, balance of benefits and harms, patient values and preferences, and resource use. The resulting 11 recommendations are intended to guide clinicians in determining which the specific patient-care strategy should be utilized by clinicians based on the needs of individual patients.

Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm.

STUDY OBJECTIVES: Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in how technicians use the standards. Because organizing meetings to evaluate this discrepancy and/or reach a consensus among multiple sleep centers is time-consuming, we developed an artificial intelligence system to efficiently evaluate the reliability and consistency of sleep scoring and hence the sleep center quality. METHODS: An interpretable machine learning algorithm was used to evaluate the interrater reliability (IRR) of sleep stage annotation among sleep centers. The artificial intelligence system was trained to learn raters from 1 hospital and was applied to patients from the same or other hospitals. The results were compared with the experts' annotation to determine IRR. Intracenter and intercenter assessments were conducted on 679 patients without sleep apnea from 6 sleep centers in Taiwan. Centers with potential quality issues were identified by the estimated IRR. RESULTS: In the intracenter assessment, the median accuracy ranged from 80.3%-83.3%, with the exception of 1 hospital, which had an accuracy of 72.3%. In the intercenter assessment, the median accuracy ranged from 75.7%-83.3% when the 1 hospital was excluded from testing and training. The performance of the proposed method was higher for the N2, awake, and REM sleep stages than for the N1 and N3 stages. The significant IRR discrepancy of the 1 hospital suggested a quality issue. This quality issue was confirmed by the physicians in charge of the 1 hospital. CONCLUSIONS: The proposed artificial intelligence system proved effective in assessing IRR and hence the sleep center quality.

Comorbidities and risk of mortality in patients with sleep apnea.

BACKGROUND: A variety of disorders, most notably cardiovascular diseases, was linked to sleep apnea (SA), but their impact on mortality of SA patients had not been systematically investigated. We aimed to develop a composite index based on the comorbidity burden to predict mortality risk. METHODS: Using Taiwan National Health Insurance Research Database, 9853 adult SA patients were enrolled and their comorbidity profile at baseline was recorded. The subjects were followed from 1995 till death or the end of 2011. A Cox regression model was used for multivariable adjustment to identify independent predictors for mortality. RESULTS: During an average follow-up period of 5.3 ± 3.1 years, 311 (3.2%) subjects died. SA patients with any comorbidity had a higher risk for death compared to those without comorbidity (HR: 11.01, 95% CI 4.00-30.33, p < 0.001). Age and 10 comorbidities related to increased overall mortality were identified, from which the CoSA (Comorbidities of Sleep Apnea) index was devised. The corresponding hazard ratios for patients with CoSA index scores of 0, 1-3, 4-6, and >6 were 1 (reference), 3.29 (95% CI, 2.04-5.28, p < 0.001), 13.56 (95% CI, 8.63-21.33, p < 0.001), and 38.47 (95% CI, 24.92-59.38, p < 0.001), respectively. CONCLUSIONS: Based on the comorbidity burden, we developed an easy-to-use tool to evaluate mortality risk in SA. Key messages: Sleep apnea (SA) is linked to a variety of disorders, particularly cardiovascular diseases. SA patients with any comorbidity may experience a higher risk of death in comparison to those without comorbidity. Comorbidities related to increased mortality are identified and converted into a simple risk indicator, the CoSA (Comorbidities of Sleep Apnea) index scores, which may help to stratify risk of death in daily practice.