Ventilatory Burden as a Measure of Obstructive Sleep Apnea Severity Is Predictive of Cardiovascular and All-Cause Mortality

Abstract

Rationale: The apnea-hypopnea index (AHI), used for the diagnosis of obstructive sleep apnea, captures only the frequency of respiratory events and has demonstrable limitations. Objectives: We propose a novel automated measure, termed "ventilatory burden" (VB), that represents the proportion of overnight breaths with less than 50% normalized amplitude, and we show its ability to overcome limitations of AHI. Methods: Data from two epidemiological cohorts (EPISONO [Sao Paolo Epidemiological Study] and SHHS [Sleep Heart Health Study]) and two retrospective clinical cohorts (DAYFUN; New York University Center for Brain Health) were used in this study to 1) derive the normative range of VB, 2) assess the relationship between degree of upper airway obstruction and VB, and 3) assess the relationship between VB and all-cause and cardiovascular disease (CVD) mortality with and without hypoxic burden that was derived using an in-house automated algorithm. Measurements and Main Results: The 95th percentiles of VB in asymptomatic healthy subjects across the EPISONO and the DAYFUN cohorts were 25.2% and 26.7%, respectively (median [interquartile range], VB_EPISONO, 5.5 [3.5-9.7]%; VB_DAYFUN, 9.8 [6.4-15.6]%). VB was associated with the degree of upper airway obstruction in a dose-response manner (VB_untreated, 31.6 [27.1]%; VB_treated, 7.2 [4.7]%; VB_{suboptimally treated}, 17.6 [18.7]%; VB_{off-treatment}, 41.6 [18.1]%) and exhibited low night-to-night variability (intraclass correlation coefficient [2,1], 0.89). VB was predictive of all-cause and CVD mortality in the SHHS cohort before and after adjusting for covariates including hypoxic burden. Although AHI was predictive of all-cause mortality, it was not associated with CVD mortality in the SHHS cohort. Conclusions: Automated VB can effectively assess obstructive sleep apnea severity, is predictive of all-cause and CVD mortality, and may be a viable alternative to the AHI.

Keywords: hypertension; mortality; sleep apnea; sleepiness; ventilation.

Figure 1.

Overview of the methodology for deriving ventilatory burden and the ventilatory distribution. (A) Using a detrended nasal cannula/pressure transducer signal, the average midbreath (middle one-third) flow is calculated and used as the measure of peak flow (i.e., normalized amplitude). (B) In case of apneas, breaths (zero flow) are imputed as described in the authors’ previous paper (27). (C) Example tracing of flow in a section of the study for a subject from the EPISONO cohort. The yellow shaded region represents the 50% cutoff. The oxygen saturation as measured by pulse oximetry signal is shown to illustrate that the hypopneas were not associated with any desaturations. (D) The overnight ventilatory distribution for the example subject in C. Ventilatory burden is the total area in the yellow shaded region.

Figure 2.

Boxplots indicating relationship of ventilatory burden (%) to degree of upper airway obstruction. (A) Ventilatory burden in asymptomatic healthy subjects across the EPISONO and DAYFUN cohorts. (B) Ventilatory burden across the DAYFUN_SYMPTOMATIC cohort at diagnosis (baseline), on continuous positive airway pressure (CPAP) treatment, on suboptimal CPAP treatment, and after two nights of CPAP withdrawal (off-CPAP). In A, the 95th percentile line derived from the EPISONO normal cohort is depicted for illustrative purposes (ventilatory burden, 25.2%). *P < 0.05.

Figure 3.

Average overnight ventilatory distribution across the EPISONO_Normal (A) and DAYFUN_Normal cohorts (B). Also shown are the overnight ventilatory distribution histograms across DAYFUN_Symptomatic patients at baseline (C), on continuous positive airway pressure (CPAP) (D), on suboptimal CPAP (E), and after two-night CPAP withdrawal (off-CPAP) (F). Error bars on each histogram bar represent the SEM.

Figure 4.

Adjusted survival curves for the relationship between ventilatory burden quintiles and all-cause mortality (A) and cardiovascular disease mortality (B) in the SHHS cohort. Curves were derived from Cox regression models reported in model 4 in Table 3 for both all-cause and cardiovascular disease mortality. SHHS = Sleep Heart Health Study.