American Journal of Respiratory and Critical Care Medicine

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], VBEPISONO, 5.5 [3.5–9.7]%; VBDAYFUN, 9.8 [6.4–15.6]%). VB was associated with the degree of upper airway obstruction in a dose–response manner (VBuntreated, 31.6 [27.1]%; VBtreated, 7.2 [4.7]%; VBsuboptimally treated, 17.6 [18.7]%; VBoff-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.

Correspondence and requests for reprints should be addressed to Ankit Parekh, Ph.D., Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029. E-mail: .

Supported by NIH grants K25HL151912 and R21HL165320 and American Academy of Sleep Medicine Foundation grant BS-233-20, Foundation for Research in Sleep Disorders.

Author Contributions: Conception and design: A.P., I.A., and D.M.R. Data collection/management: A.P., S.W., R.O., M.A., L.B.M.d.G., L.O.P., S.T., I.A., and D.M.R. Algorithm development and statistical analysis: A.P., I.A., S.W., K.K., and D.M.R. Data interpretation: A.P., K.K., S.W., T.M.T., A.V., I.A., and D.M.R. Critical revision of the article: A.P., K.K., S.W., T.M.T., A.V., R.O., M.A., L.B.M.d.G., L.O.P., S.T., I.A., and D.M.R. Final approval of the version to be published: A.P., K.K., S.W., T.M.T., A.V., R.O., M.A., L.B.M.d.G., L.O.P., S.T., I.A., and D.M.R.

This article has a related editorial.

This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.

Originally Published in Press as DOI: 10.1164/rccm.202301-0109OC on September 12, 2023

Author disclosures are available with the text of this article at www.atsjournals.org.

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