Frequency of flow limitation using airflow shape

Abstract

Study objectives: The presence of flow limitation during sleep is associated with adverse health consequences independent of obstructive sleep apnea (OSA) severity (apnea-hypopnea index, AHI), but remains extremely challenging to quantify. Here we present a unique library and an accompanying automated method that we apply to investigate flow limitation during sleep.

Methods: A library of 117,871 breaths (N = 40 participants) were visually classified (certain flow limitation, possible flow limitation, normal) using airflow shape and physiological signals (ventilatory drive per intra-esophageal diaphragm EMG). An ordinal regression model was developed to quantify flow limitation certainty using flow-shape features (e.g. flattening, scooping); breath-by-breath agreement (Cohen's ƙ); and overnight flow limitation frequency (R2, %breaths in certain or possible categories during sleep) were compared against visual scoring. Subsequent application examined flow limitation frequency during arousals and stable breathing, and associations with ventilatory drive.

Results: The model (23 features) assessed flow limitation with good agreement (breath-by-breath ƙ = 0.572, p < 0.001) and minimal error (overnight flow limitation frequency R2 = 0.86, error = 7.2%). Flow limitation frequency was largely independent of AHI (R2 = 0.16) and varied widely within individuals with OSA (74[32-95]%breaths, mean[range], AHI > 15/h, N = 22). Flow limitation was unexpectedly frequent but variable during arousals (40[5-85]%breaths) and stable breathing (58[12-91]%breaths), and was associated with elevated ventilatory drive (R2 = 0.26-0.29; R2 < 0.01 AHI v. drive).

Conclusions: Our method enables quantification of flow limitation frequency, a key aspect of obstructive sleep-disordered breathing that is independent of the AHI and often unavailable. Flow limitation frequency varies widely between individuals, is prevalent during arousals and stable breathing, and reveals elevated ventilatory drive. Clinical trial registration: The current observational physiology study does not qualify as a clinical trial.

Keywords: airflow obstruction; automated; classification; diaphragm EMG; inspiratory flow limitation; phenotype; polysomnography; upper airway resistance syndrome.

Figure 1.

Histograms illustrating differences in breaths from each visually scored flow-limitation category (gray, certain flow limitation; “Certain FL,” N = 29,080; orange, possible flow limitation “Possible FL,” N = 39,053; yellow, “Normal,” N = 49,738). Differences between categories are shown for (A) physiologically measured airflow obstruction severity (flow:drive per diaphragm EMG) where lower values indicate more severe obstruction and (B) novel flow shape model developed in the current study to estimate the probability of flow limitation (23-feature ordinal logistic regression model). Note clear group separation between certain flow limitations and normal categories (gray v. yellow). Optimal thresholds to separate certain from possible, and possible from normal in (A) were 58% and 86%, respectively (2-class threshold separating certain from normal: 70%), and in (B) were 50% and 95.7% (2-class threshold: 75%). Greater x-axis distance in (B) represents a higher log-odds score based on the linear combination of features in the regression model; 12%, 50%, and 88% probability is equivalent to log-odds of flow limitation of −2, 0, and +2, respectively.

Figure 2.

Example pneumotach flow traces for breaths visually scored as normal, possible flow limitation “Possible FL,” and certain flow limitation “Certain FL” for an individual patient. Figures for other patients are provided in the Online Supplement and are intended to provide a resource upon which readers can use to calibrate visual scoring of flow limitation across centers. Numbers accompanying each breath illustrate the flow shape model-estimated percent probability of flow limitation (ordinal logistic regression model). The figure illustrates the continuum of certainty in flow limitation from Normal to Possible to Certain flow limitation categories; breaths at the top are rounded, while those lower on the page exhibited greater flattening and scooping illustrating that the intended airflow is lower than the achieved level. Note also the heterogeneity in shapes for breaths within this single subject.

Figure 3.

Frequency of flow limitation during sleep. Comparison between visual scoring and flow shape model measurements. The figure shows analysis based on pneumotach flow data prior to cross-validation. (A) The correlation between proportions of sleep breaths categorized as possible + certain flow limitation by the flow shape model versus visual scoring (after cross-validation R² = 0.76). (B) Analysis repeated for certain flow limitation (after cross-validation R² = 0.53). FL, flow limitation; R², coefficient of determination; Error, mean absolute error.

Figure 4.

(A) Relationship between flow limitation frequency during sleep (flow shape model, “objective”) and the apnea-hypopnea index (AHI; black dots show certain flow limitation, R² = 0.33, p < 0.001; orange dots show certain + possible flow limitation, R² = 0.16, p < 0.05). Data from all participants are shown (N = 40). Flow limitation frequency is shown for both flow shape model (“objective”) and visual scoring (“visual”) measurements during: (B) Sleep, (C) Wake, and (D) Obstructive Hypopneas. Stacked bars in panels B–D show group mean results from participants with obstructive sleep apnea (OSA, AHI > 15 events/h). As expected, we find that the majority of breaths during sleep in patients with OSA are flow limited. Also confirming methodological validity: most breaths during wake were scored as not flow limited (panel C, see Online Supplement Figure S6 for results in non-OSA) and most breaths within obstructive hypopneas were scored as flow limited (panel D).

Figure 5.

Flow limitation frequency during arousals. (A) Representative airflow recordings from two participants, showing (top) arousal breaths without flow limitation, and (bottom) arousal breaths with flow limitation. (B) Stacked bar chart showing the group mean flow limitation frequency during arousals. (C) Scatter plot showing the relationship between flow limitation frequency during arousals and the apnea-hypopnea index (AHI; orange dots show certain + possible flow limitation, R² = 0.14, p < 0.05; black dots show certain flow limitation, R² = 0.3, p < 0.001). (D) Stacked bar charts for example individual participants; note that some individuals with similar AHI exhibit remarkably different levels of flow limitation during arousals. Certain flow limitation = black, possible flow limitation = orange, and normal = yellow. Objective flow-shape model scoring (O) and visual scoring (V).

Figure 6.

Flow limitation frequency during “stable breathing” (continuous period of >3 min of uninterrupted breathing during sleep, without arousal or scored respiratory event). (A) Representative airflow recordings from two participants, showing (top) stable breathing period without flow limitation, and (bottom) stable breathing period with flow limitation. (B) Stacked bar chart showing the group mean flow limitation frequency during periods of stable breathing. (C) Scatter plot showing the relationship between flow limitation frequency during stable breathing and the apnea-hypopnea index (AHI; orange dots show certain + possible flow limitation, R² = 0.12, p < 0.05; black dots show certain flow limitation, R² = 0.26, p < 0.001). (D) Stacked bar charts for example individual participants; note that some individuals with similar AHI exhibit remarkably different levels of flow limitation during stable breathing. Certain flow limitation = black, possible flow limitation = orange, and normal = yellow. Objective flow-shape model scoring (O) and visual scoring (V).