Identifying obstructive sleep apnoea patients responsive to supplemental oxygen therapy

Abstract

A possible precision-medicine approach to treating obstructive sleep apnoea (OSA) involves targeting ventilatory instability (elevated loop gain) using supplemental inspired oxygen in selected patients. Here we test whether elevated loop gain and three key endophenotypic traits (collapsibility, compensation and arousability), quantified using clinical polysomnography, can predict the effect of supplemental oxygen on OSA severity.36 patients (apnoea-hypopnoea index (AHI) >20 events·h-1) completed two overnight polysomnographic studies (single-blinded randomised-controlled crossover) on supplemental oxygen (40% inspired) versus sham (air). OSA traits were quantified from the air-night polysomnography. Responders were defined by a ≥50% reduction in AHI (supine non-rapid eye movement). Secondary outcomes included blood pressure and self-reported sleep quality.Nine of 36 patients (25%) responded to supplemental oxygen (ΔAHI=72±5%). Elevated loop gain was not a significant univariate predictor of responder/non-responder status (primary analysis). In post hoc analysis, a logistic regression model based on elevated loop gain and other traits (better collapsibility and compensation; cross-validated) had 83% accuracy (89% before cross-validation); predicted responders exhibited an improvement in OSA severity (ΔAHI 59±6% versus 12±7% in predicted non-responders, p=0.0001) plus lowered morning blood pressure and "better" self-reported sleep.Patients whose OSA responds to supplemental oxygen can be identified by measuring their endophenotypic traits using diagnostic polysomnography.

Trial registration: ClinicalTrials.gov NCT01751971.

Conflict of interest statement

Conflict of interest: S.A. Sands reports grants from NIH and AHA, during the conduct of the study; and personal fees from Cambridge Sound Management, Nox Medical and Merck, outside the submitted work. Conflict of interest: B.A. Edwards received a salary from Heart Foundation of Australia, during the conduct of the study. Conflict of interest: P.I. Terrill reports grants from Australian National Health and Medical Research Council (1064163), during the conduct of the study; and grants from Hull Family Donation, 2014, outside the submitted work. Conflict of interest: J.P. Butler has nothing to disclose. Conflict of interest: R.L. Owens reports personal fees for consultancy from Novartis, and honoraria and travel reimbursement from ResMed and Itamar Medical, outside the submitted work. Conflict of interest: L. Taranto-Montemurro reports grants from American Heart Association, personal fees from Novion Pharmaceuticals and Cambridge Sound management, and other from Apnimed, outside the submitted work. Conflict of interest: A. Azarbarzin has nothing to disclose. Conflict of interest: M. Marques has nothing to disclose. Conflict of interest: L.B. Hess has nothing to disclose. Conflict of interest: E.T. Smales has nothing to disclose. Conflict of interest: C.M. de Melo has nothing to disclose. Conflict of interest: D.P. White reports personal fees from Philips Respironics (Chief Scientific Officer), personal fees for consultancy from Night Balance, and personal fees from Apnicure (previously Chief Medical Officer), outside the submitted work. Conflict of interest: A. Malhotra relinquished all outside personal income in 2012 as an Officer of the American Thoracic Society. ResMed provided a philanthropic donation to University College San Diego in support of a sleep centre. Conflict of interest: A. Wellman reports grants from National Institutes of Health and Philips Respironics, during the conduct of the study; grants from Varnum Sleep and Breathing Solutions and Cambridge Sound Management, and personal fees from Bayer and Nox Medical, outside the submitted work; in addition, A. Wellman has a patent Airway and Airflow Factors issued.

Copyright ©ERS 2018.

Figures

FIGURE 1

Effect of supplemental oxygen on primary and secondary outcomes in responders (n=9) and non-responders (n=27). a) In responders, improvements were observed in the apnoea–hypopnoea index (AHI) by definition. In addition, responders exhibited improvements in b) the frequency of arousals from sleep (arousal index) as well as in c, d) blood pressure (change from evening to morning) and e) subjective sleep quality. Subjective sleep quality was scored as follows: 1=slept better; 0=slept the same; −1=slept worse. There was no effect on the Stanford Sleepiness Scale (subjective morning alertness, not shown). Error bars indicate SEM. SBP: systolic blood pressure; DBP: diastolic blood pressure. #: oxygen versus sham; ¶: responders versus non-responders.

FIGURE 2

Example endophenotype data off treatment are shown for a, b) a responder (sham apnoea–hypopnoea index (AHI)=44.9, treatment AHI=9.7 events·h−1) and c, d) a non-responder (sham AHI=42.8, treatment AHI=45.2 events·h−1). a, c) Illustrative traces of sleep apnoea and model estimation of ventilatory drive. Note that events are self-similar within a subject. In the responder, changes in ventilation track estimated ventilatory drive during obstructive events. By contrast, in the non-responder ventilation falls as ventilatory drive rises. In the model estimations of ventilatory drive, note that the thoracic (Th.) and abdominal (Ab.) excursion (piezoelectric respiratory belts) signals are out of phase (paradox) during events in both subjects, consistent with airflow obstruction. Ventilation and ventilatory drive are expressed as a proportion of the mean ventilation during the window (“eupnoea”). Estimated ventilatory drive (green line) is shown partitioned into chemical drive (chemoreflex, i.e. loop gain (LGn) contribution) and the ventilatory response to arousal (arousal contribution, green minus black line). b, d) Summary plots of ventilation (i.e. actual airflow) versus ventilatory drive (i.e. intended airflow) during sleep (black line: median; shading: interquartile range). The responder has a higher loop gain, a lower ventilatory drive preceding arousal (arousal threshold) and less-severe collapsibility as inferred from the higher level of ventilation at normal ventilatory drive (Vpassive). EEG: electroencephalogram; Flow: square-root transformed nasal pressure; Comp: compensation.

FIGURE 3

Predictive value of the endophenotypic traits causing obstructive sleep apnoea. a) Loop gain (LGn) indicates ventilatory instability, i.e. the predisposition to spontaneous periodic breathing. b) Collapsibility (Vpassive), c) compensation and d) arousal threshold data are presented as a proportion of eupnoeic levels. See text for details. Shading illustrates the region of predicted responders and definition of high versus low for each trait subgroup. Bars illustrate the reduction in apnoea–hypopnoea index (AHI) with treatment in the high versus low subgroups (mean±SEM, patients were assigned to subgroups using cross-validation). Note the y-axis scale is compressed below zero to facilitate visual interpretation of values above zero. Each trait had significant negative predictive value: LGn (reduction in AHI: 37.6±6.9% versus 14.0±11.5%; positive predictive value (PPV)=35±10%, p=0.3; negative predictive value (NPV)=92±7%, p=0.02); Vpassive (49.3±6.9% versus 10.9±8.1%; PPV=47±12%, p=0.07; NPV=95 ±5%, p<0.001); compensation (53.0±8.9% versus 18.5±7.2%; PPV=55±15%, p=0.049; NPV=88±6%, p=0.046); arousal threshold (39.7±12.8% versus 22.2±6.0%; PPV=50±13%, p=0.06; NPV=91±6%, p=0.009).

FIGURE 4

Multivariable analysis of the obstructive sleep apnoea traits. a–c) Two-trait “slices” of the four-trait regression model illustrate how the traits causing sleep apnoea combine to predict responses to supplemental oxygen. Dots are individual patients (circles are patients from current study, squares are patients from EDWARDS et al. [4]). Shading illustrates the regions of “predicted responders” (green) and “predicted non-responders” (red). Each two-trait slice represents model predictions at constant values of the other two traits; data points that are far enough away from the slice such that the slice prediction does not match the overall model prediction (irrespective of correct/incorrect) are shown in light grey. d) The continuous relationship between the reduction in apnoea–hypopnoea index (AHI) with oxygen and the regression model prediction is shown (probability=1/(1+e−Y); see table 2). Note the y-axis scale is compressed below zero to facilitate visual interpretation of values above zero.

FIGURE 5

Effect of supplemental oxygen on primary and secondary outcomes in patients with suitable pathophysiology, i.e. “predicted responders” (n=13), and patients with unsuitable pathophysiology, i.e. “predicted non-responders” (n=23), based on endophenotypic traits (logistic regression, cross-validated; table 2, figure 4). a) In predicted responders, treatment led to an improvement in obstructive sleep apnoea severity (reduction in apnoea–hypopnoea index (AHI)); in contrast to figure 1, differences between predicted responders and non-responders are not “by definition” because subgroups were assigned using only data from the other subjects (i.e. cross-validation). Predicted responders also exhibited improvements in b) the frequency of arousals from sleep, c, d) blood pressure (evening minus morning levels) and e) subjective sleep quality. Subjective sleep quality was scored as follows: 1=slept better; 0=slept the same; −1=slept worse. There was no effect on the Stanford Sleepiness Scale (subjective morning alertness, not shown) in either subgroup. SBP: systolic blood pressure; DBP: diastolic blood pressure. #: oxygen versus sham; ¶: responders versus non-responders. Compare results with figure 1.