Mortality‐risk‐based apnea–hypopnea index thresholds for diagnostics of obstructive sleep apnea

Abstract

Summary The severity of obstructive sleep apnea is clinically assessed mainly using the apnea–hypopnea index. Based on the apnea–hypopnea index, patients are classified into four severity groups: non‐obstructive sleep apnea (apnea–hypopnea index < 5); mild (5 ≤ apnea–hypopnea index < 15); moderate (15 ≤ apnea–hypopnea index < 30); and severe obstructive sleep apnea (apnea–hypopnea index ≥ 30). However, these thresholds lack solid clinical and scientific evidence. We hypothesize that the current apnea–hypopnea index thresholds are not optimal despite their global use, and aim to assess this clinical shortcoming by optimizing the thresholds with respect to the risk of all‐cause mortality. We analysed ambulatory polygraphic recordings of 1,783 patients with suspected obstructive sleep apnea (mean follow‐up 18.3 years). We simulated 79,079 different threshold combinations in 100 randomized subgroups of the population and studied the relative risk of all‐cause mortality corresponding to each combination and randomization. The optimal thresholds were chosen according to three criteria: (a) the hazard ratios increase linearly between severity groups towards more severe obstructive sleep apnea; (b) each group includes at least 15% of the study population; (c) group sizes decrease with increasing obstructive sleep apnea severity. The risk of all‐cause mortality varied greatly across simulations; the threshold defining non‐obstructive sleep apnea group having the largest effect on the hazard ratios. The apnea–hypopnea index threshold combination of 3‐9‐24 was optimal in most of the subgroups. In conclusion, the assessment of obstructive sleep apnea severity based on the current apnea–hypopnea index thresholds is not optimal. Our novel approach provides methods for optimizing apnea–hypopnea index‐based severity classification, and the revised thresholds better differentiate patients into severity groups, ensuring that an increase in the severity corresponds to an increase in the risk of all‐cause mortality.

Severe desaturations increase psychomotor vigilance task-based median reaction time and number of lapses in obstructive sleep apnoea patients

“…Furthermore, these results correspond with previous studies [8,9,[18][19][20][21][22], by showing that conventional AHI-based assessments of OSA have little connection to poor PVT performance. These findings highlight, together with recent studies [7,24,29,31], the clinical importance of a more detailed analysis of PSG recordings when assessing OSA severity, symptoms and daytime functioning. As the presented novel parameters are based on routine manual scoring of PSG, they can be easily implemented to clinics without any extra work required of technical or medical staff [32].…”

“…Even though patients suffering from severe OSA have on average inferior PVT performance compared to healthy individuals, no significant differences in PVT performance between OSA severity groups have been reported [9,22,23]. This lack of differences in OSA patients could, however, be related to arbitrary, outdated and sub-optimal severity classification thresholds for the AHI [24,25].…”

Detailed Assessment of Sleep Architecture With Deep Learning and Shorter Epoch-to-Epoch Duration Reveals Sleep Fragmentation of Patients With Obstructive Sleep Apnea

“…A similar effect was also seen by Norman et al who reported that no significant differences exist between the normal and mild OSA groups in traditional sleep parameters and differences only emerge when considering the sleep continuity with survival analysis [23]. However, it has to be noted that the division into OSA severity groups is highly artificial and simplistic and the severity assessment with AHI might not sufficiently reflect the physiological effects of OSA [24][25][26]. Therefore, it could be beneficial to study how sleep fragmentation varies when defining the OSA severity differently or even attempt to define the severity of OSA by using sleep fragmentation as a metric.…”

<p>The Evaluation of Autonomic Arousals in Scoring Sleep Respiratory Disturbances with Polysomnography and Portable Monitor Devices: A Proof of Concept Study</p>

“…9 Those previously mentioned events are characterized by repetitive episodes of total or partial upper airway closure and by increased respiratory efforts driven by chemoreceptor and mechanoreceptor stimuli, culminating in a micro-arousal from sleep. 10 However, recent literature evaluating the association between AHI and mortality risks suggests that the assessment of OSA severity based on the current AHI thresholds is not optimal, 11 highlighting the need of adjusting the current thresholds (accounting for age, sex, presence of comorbidities, etc.) or investigating the use of other indexes for its diagnosis.…”