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) completed two overnight polysomnographic studies (single-blinded randomised-controlled crossover) on supplemental oxygen (40% inspired) 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 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% 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.
Rationale: Oral appliance therapy is efficacious in many patients with obstructive sleep apnea (OSA) but prediction of treatment outcome is challenging. Small, detailed physiological studies have identified key OSA endotypic traits (pharyngeal collapsibility and loop gain) as determinants of greater oral appliance efficacy. Objectives: We used a clinically-applicable method to estimate OSA traits from routine polysomnography and identify an endotype-based subgroup of patients expected to show superior efficacy. Methods: In 93 patients (baseline apnea-hypopnea index [AHI] ≥20 events/hr), we examined whether polysomnography-estimated OSA traits (pharyngeal: collapsibility and muscle compensation; non-pharyngeal: loop gain, arousal threshold and ventilatory response to arousal) were associated with oral appliance efficacy (percent reduction in AHI from baseline) and could predict responses to treatment. Multivariable regression (with interactions) defined endotype-based subgroups of "predicted" responders and non-responders (based on 50% reduction in AHI). Treatment efficacy was compared between the predicted subgroups (with cross-validation). Results: Greater oral appliance efficacy was associated with favorable non-pharyngeal traits (lower loop gain, higher arousal threshold and lower response to arousal), moderate (non-mild, non-severe) pharyngeal collapsibility and weaker muscle compensation (overall R 2 =0.30, adjusted R 2 =0.19, p=0.003). Predicted responders (N=54), compared with predicted nonresponders (N=39), exhibited a greater reduction in AHI from baseline (73[66-79] vs. 51[38-61]%, mean[95%CI], p<0.0001) and a lower treatment AHI (8[6-11] vs. 16[12-20]events/hr, p=0.002). Differences persisted after adjusting for clinical covariates (including baseline AHI, body mass index, and neck circumference). Conclusions: Quantifying OSA traits using clinical polysomnography can identify an endotypebased subgroup of patients that is highly responsive to oral appliance therapy. Prospective validation is warranted.
Sleep apnea is caused by several endophenotypic traits, namely pharyngeal collapsibility, poor muscle compensation, ventilatory instability (high loop gain), and arousability from sleep (low arousal threshold). Measures of these traits have shown promise for predicting outcomes of therapies (e.g. oral appliances, surgery, hypoglossal nerve stimulation, CPAP, and pharmaceuticals), which may become an integral part of precision sleep medicine. Currently the methods Sands et al. [1] developed for endotyping sleep apnea from polysomnography (PSG) are embedded in the original authors’ code, which is computationally expensive and requires technological expertise to run. We present a re-implementation and validation of the integrity of the original authors’ code by reproducing the endo-Phenotype Using Polysomnography (PUP) method of Sands et al. [1, 2] The original MATLAB methods were reprogrammed in Python; efficient methods were developed to detect breaths, calculate normalized ventilation (moving time-average), and model ventilatory drive (intended ventilation). The new implementation (PUPpy) was validated by comparing the endotypes from PUPpy with the original PUP results. Both endotyping methods were applied to 38 manually scored polysomnographic studies. Results of the new implementation were strongly correlated with the original (p<10 -6 for all): ventilation at eupnea V̇passive (ICC=0.97), ventilation at arousal onset V̇active (ICC=0.97), loop-gain (ICC=0.96), and arousal threshold (ICC=0.90). We successfully implemented the original method by Sands et.al. [1, 2] providing further evidence of its integrity. Additionally, we created a cloud-based version for scaling up sleep apnea endotyping that can be used more easily by a wider audience of researchers and clinicians.
SO, SS, AW and OMV contributed to the study concept and design. SO, MD, MW, JV, MB, OMV collected the data. SO and SS performed the trait analysis and statistical analysis. All authors interpreted the results, performed a critical revision of the article and approved the version to be published.
Reduced ventilatory control stability (elevated loop gain) is a key non-anatomical pathological trait contributing to obstructive sleep apnea (OSA), yet the mechanisms responsible remain unclear. We sought to identify the key factors contributing to elevated loop gain in OSA (controller versus plant contributions) and to examine if abnormalities in these factors persist after OSA treatment. In 15 males (8 OSA, 7 height, weight- and age-matched controls) we measured loop gain, controller gain and plant gain using a pseudorandom binary CO stimulation method during wakefulness. Factors potentially influencing plant gain were also assessed (supine lung volume via helium dilution and spirometry). Measures were repeated 2 and 6 weeks after initiating CPAP. Loop gain was higher in OSA versus controls (LG at 1 cycle/min 0.28 ± 0.04 versus 0.16 ± 0.04, p = 0.046) and the controller exhibited a greater peak response to CO2 and faster roll-off in OSA. OSA patients also exhibited reduced FEV1 and FVC compared to controls (92.2 ± 1.7 versus 102.9 ± 3.5% predicted, p = 0.021; 93.4 ± 3.1 versus 106.6 ± 3.6% predicted, p = 0.015, respectively). There was no effect of treatment on any variable. These findings confirm loop gain is higher in untreated OSA patients than in matched controls, however this was not affected by treatment.
Study Objectives Obstructive sleep apnea (OSA) is characterized by multiple “endotypic traits,” including pharyngeal collapsibility, muscle compensation, loop gain, and arousal threshold. Here, we examined (1) within-night repeatability, (2) long-term consistency, and (3) influences of body position and sleep state, of endotypic traits estimated from in-home polysomnography in mild-to-severe OSA (apnea-hypopnea index, AHI > 5 events/h). Methods Within-night repeatability was assessed using Multi-Ethnic Study of Atherosclerosis (MESA): Traits derived separately from “odd” and “even” 30-min periods were correlated and regression (error vs. N windows available) provided a recommended amount of data for acceptable repeatability (Rthreshold = 0.7). Long-term consistency was assessed using the Osteoporotic Fractures in Men Study (MrOS) at two time points 6.5 ± 0.7 years apart, before and after accounting for across-year body position and sleep state differences. Within-night dependence of traits on position and state (MESA plus MrOS data) was estimated using bootstrapping. Results Within-night repeatability for traits ranged from R = 0.62–0.79 and improved to R = 0.69–0.83 when recommended amounts of data were available (20–35 7-min windows, available in 94%–98% of participants); repeatability was similar for collapsibility, loop gain, and arousal threshold (R = 0.79–0.83), but lower for compensation (R = 0.69). Long-term consistency was modest (R = 0.30–0.61) and improved (R = 0.36–0.63) after accounting for position and state differences. Position/state analysis revealed reduced loop gain in REM and reduced collapsibility in N3. Conclusions Endotypic traits can be obtained with acceptable repeatability. Long-term consistency was modest but improved after accounting for position and state changes. These data support the use of endotypic assessments in large-scale epidemiological studies. Clinical Trial Information The data used in the manuscript are from observational cohort studies and are not a part of the clinical trial.
We aimed to determine whether patients diagnosed with obstructive sleep apnea (OSA) who fail to respond to upper airway surgery may be successfully treated with supplemental oxygen and whether we could identify baseline physiologic endotypes (ie, collapsibility, loop gain, arousal threshold, and muscle compensation) that predict response to oxygen therapy. Methods: We conducted a single night, randomized double-blinded cross over trial in which patients with OSA who failed to respond to upper airway surgery were treated on separate nights with oxygen therapy (4 L/min) or placebo (medical air). Effect of oxygen/air on OSA on key polysomnography outcomes were assessed: apnea-hypopnea index (AHI), AHI without desaturation (ie, flow-based AHI), arousal index, and morning blood pressure. OSA endotypes were estimated from the polysomnography signals to determine whether baseline OSA physiology could be used to predict response to oxygen therapy. Results: There was a statistically significant reduction in AHI and flow-based AHI on oxygen vs placebo (flow-based AHI: 42.4 ± 21.5 vs 30.5 ± 17.1 events/h, P = .008). Arousal index was also reduced on oxygen vs placebo (41.1 ± 19.5 vs 33.0 ± 15.3 events/h, P =.006). There was no significant difference in morning blood pressure between oxygen and placebo. Although 7 of 20 individuals experienced a 50% reduction or greater in flow-based AHI on oxygen (responders), there was no difference in the baseline OSA endotypes (or clinical characteristics) between responders and nonresponders. Conclusions: Our findings demonstrate that a proportion of patients who fail to respond to upper airway surgery for OSA respond acutely to treatment with supplemental oxygen.
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