Abstract
Rationale
Randomized controlled trials of continuous positive airway pressure (CPAP) in patients with obstructive sleep apnea (OSA) have not demonstrated protection against adverse cardiovascular outcomes. Recently, observational studies revealed that OSA-related cardiovascular risk is concentrated in patients with an elevated pulse rate response to respiratory events (ΔHR).
Objectives
Here, in this post hoc analysis of a prospective clinical trial, we test the hypothesis that a greater pretreatment ΔHR is associated with greater CPAP-related protection against adverse cardiovascular outcomes.
Methods
ΔHR was measured from baseline polysomnography of the RICCADSA (Randomized Intervention with CPAP in CAD and OSA) randomized controlled trial (patients with coronary artery disease [CAD] and OSA [apnea–hypopnea index ⩾ 15 events/h] with Epworth Sleepiness Scale score < 10; nCPAP:ncontrol = 113:113; male, 85%; age, 66 ± 8 [mean ± SD] yr). The primary outcome was a composite of repeat revascularization, myocardial infarction, stroke, and cardiovascular mortality. Multivariable Cox regression assessed whether the effect of CPAP was moderated by ΔHR (treatment-by-ΔHR interaction).
Measurements and Main Results
The CPAP-related reduction in risk increased progressively with increasing pretreatment ΔHR (interaction hazard ratio [95% confidence interval], 0.49 [0.27 to 0.90] per SD increase in ΔHR; P < 0.05). This means that in patients with a ΔHR of 1 SD above the mean (i.e., 10 beats/min), CPAP was estimated to reduce cardiovascular risk by 59% (6% to 82%) (P < 0.05), but no significant risk reduction was estimated in patients with a mean ΔHR (6 beats/min; CPAP risk reduction, 16% [−53% to 54%]; P = 0.6).
Conclusions
The protective effect of CPAP in patients with CAD and OSA without excessive sleepiness was modified by the ΔHR. Specifically, patients with higher ΔHR exhibit greater cardiovascular benefit from CPAP therapy.
Keywords: heart rate response, sleepiness, sleep apnea, cardiovascular, clinical trial
At a Glance Commentary
Scientific Knowledge on the Subject
Obstructive sleep apnea (OSA) is associated with adverse cardiometabolic/neurocognitive outcomes and daytime symptoms, most importantly excessive sleepiness. Continuous positive airway pressure (CPAP; first line therapy for OSA) is prescribed for control of symptoms, however, half of those with OSA do not report excessive sleepiness. In these asymptomatic patients, CPAP is recommended to reduce cardiovascular disease risk. However, randomized controlled trials in predominantly non-sleepy OSA patients have not demonstrated a protective effect of CPAP on adverse cardiovascular outcomes. In the light of these findings, there is uncertainty in the field regarding the clinical benefits of treating OSA without excessive sleepiness. An explanation for these findings may be related to patient selection for the CPAP trials.
What This Study Adds to the Field
In this study, we show that individuals with OSA (without excessive sleepiness) who have an elevated heart rate response to apneas/hypopneas are at increased risk for incident cardiovascular events. These individuals exhibit greater cardiovascular benefit from CPAP therapy than those with lower heart rate response. Specifically, CPAP was estimated to reduce cardiovascular risk by 59% (p<0.05) in those with a high heart rate response, but no significant risk reduction was estimated in all comers.
Obstructive sleep apnea (OSA) is a prevalent condition (1) associated with adverse cardiovascular events and poor health outcomes (2–4). Observational studies report a strong association between untreated OSA and incident cardiovascular disease and mortality (3–6). Although treatment of OSA is clearly indicated for control of symptoms, most importantly excessive sleepiness, approximately half of those with OSA in the general population do not report excessive sleepiness (7). In these asymptomatic patients, treatment is often recommended to reduce cardiovascular disease risk. However, randomized controlled trials in predominantly nonsleepy patients with OSA have not demonstrated a protective effect of continuous positive airway pressure (CPAP; first line therapy for OSA) on adverse cardiovascular outcomes (8–10).
The explanation for this observation may be related to patient selection for these major trials (8). OSA is characterized by repeated partial (“hypopnea”) or complete (“apnea”) pharyngeal obstruction during sleep, and OSA is typically quantified by the total number of these events. However, apneas and hypopneas result in intermediate sequelae that vary remarkably from patient to patient, including oxygen desaturation, arousal from sleep, and sympathoexcitation evidenced by surges in heart rate. These downstream characteristics have not been used to inform patient selection for inclusion in trials or clinical decision-making.
Recently, analysis of large cohort studies revealed that a subgroup of patients with OSA and without excessive sleepiness who demonstrated a greater respiratory event–related pulse rate response (ΔHR) were at increased OSA-related risk of cardiovascular morbidity and mortality (4). However, no study has examined whether this phenotypic trait, measured before treatment, could predict the therapeutic benefit of CPAP. Accordingly, in a secondary analysis of the RICCADSA (Randomized Intervention with CPAP in CAD and Sleep Apnea) trial (9), which randomized patients with OSA (apnea–hypopnea index [AHI] ⩾ 15 events/h) who had coronary artery disease (CAD) and were without excessive sleepiness (Epworth Sleepiness Scale score < 10) to CPAP or usual care, we tested the hypothesis that a larger pretreatment ΔHR is associated with a greater CPAP-related reduction in adverse cardiovascular outcomes. In addition, sensitivity analyses examined ΔHR normalized by or adjusted for other event severity measures (desaturation area [3, 10], arousal intensity [11, 12], event duration [13]). Finally, sensitivity analyses assessed the effect of β-blockers and other CAD medications and adherence to CPAP treatment. Some of the results of these studies have been previously reported in the form of an abstract (14).
Methods
Study Participants
The details of the methodology of the parent RICCADSA randomized controlled trial (RCT) have been published previously (9). In brief, the entire study cohort included adult patients with angiography-verified CAD who had undergone percutaneous coronary intervention (PCI) or coronary artery bypass surgery within 6 months before the baseline sleep study. Inclusion criteria for the RCT comprised an AHI ⩾ 15 events/h on home sleep apnea testing (HSAT), and the absence of excessive daytime sleepiness (defined by Epworth Sleepiness Scale score < 10) at baseline. All patients underwent an overnight polysomnography (PSG) recording before the randomization to CPAP or usual care. For the purpose of the current analysis of the RICCADSA trial, 226 patients with available PSG data with adequate quality pulse signals constituted the final analytic sample (Figure 1). Institutional review board approval was obtained from all participants who provided written informed consent (9).
Figure 1.
Flow diagram presenting ascertainment of the study sample. CPAP = continuous positive airway pressure; PSG = polysomnography.
Sleep Studies
HSAT
To determine parent trial eligibility, participants underwent a sleep questionnaire and an HSAT (Embletta), including recordings of nasal pressure using a nasal cannula and/or pressure transducer, thoraco-abdominal movements (respiratory inductance plethysmography), a finger pulse oximeter, and body position and movements. The total sleep time was estimated based on self-reporting as well as patterns of body movements. Apnea was defined by a reduction in flow of ⩾90%, and hypopneas as a ⩾50% reduction in thoracoabdominal movement and/or a ⩾50% decrease in the nasal pressure amplitude for ⩾10 seconds regardless of oxygen desaturation (15), or a ⩾30% reduction in thoracoabdominal movement and/or in the nasal pressure amplitude for ⩾10 seconds with an oxygen desaturation of ⩾4%.
Overnight PSG
All patients with CAD with an AHI ⩾ 15 events/h based on the HSAT underwent unattended overnight PSG in the hospital using a computerized recording system (Embla A10). The PSG system included sleep monitoring through three-channel electroencephalography (C4-A1, C3-A2, CZ-A1), two-channel electrooculography, one-channel submental EMG, bilateral tibial EMG, and two-lead ECG in addition to the cardiorespiratory channels as described for the HSAT system above. PSG recordings were scored by an observer blinded to clinical data and baseline screening results from the previous HSAT recordings. Obstructive events on the PSGs were scored according to the same apnea-hypopnea criteria described for the HSAT recordings (15).
Clinical Endpoints and Outcomes
The primary outcome was a composite of repeat revascularization, acute myocardial infarction, stroke, and cardiovascular mortality during a median follow-up of 57 months. Clinical endpoint data were obtained from hospital records and death certificates by a blinded team, and outcomes were adjudicated by a RICCADSA events committee blinded to intervention arm (9).
ΔHR
Using baseline PSGs, pulse rate was derived from the pulse oximetry sensor and was used to estimate the heart rate. Consistent with our previous studies (4, 11, 16) and as shown in Figure 2, the ΔHR was defined as the difference between a maximum pulse rate during a subject-specific search window (search window extended from the preevent minimum to the postevent minimum of the event-related, ensemble-averaged pulse rate, as previously used [3, 4, 10]; see online supplement and Figure E1 for additional details on the calculation of search window) and an event-related minimum pulse rate (the minimum pulse rate during apneas and hypopneas). Finally, individual-level ΔHR was defined as the mean of all event-specific responses.
Figure 2.
The pulse rate response to each individual respiratory event (ΔHR) was defined as the maximum pulse rate during a subject-specific search window (see Figure E1 for more detail) and the minimum pulse rate during that event. The individual-level ΔHR was defined as the mean of all event-specific responses. BPM = beats per minute.
Statistical Analysis
To test the primary hypothesis that the effect of CPAP treatment on the primary outcome is moderated by ΔHR (i.e., that the effect of CPAP differs between those with higher ΔHR than those with lower ΔHR). Inclusion of control subjects was necessary per study design to estimate the CPAP-related risk reduction of treated versus untreated patients at different levels of ΔHR. Multivariable Cox regression was used to quantify the interaction between treatment arm and ΔHR (log-transformed), adjusting for age, sex, body mass index, and cardiac intervention (PCI or coronary artery bypass surgery). The Cox regression model included the covariates listed above, the main effect of ΔHR and CPAP, as well as the interaction effect (ΔHR × CPAP) under investigation. This model was used to calculate the CPAP-related risk reduction at point estimates of 4 beats/min (BPM) (mean − 1 SD), 6 BPM (mean), and 10 BPM (mean + 1 SD). Kaplan-Meier survival curves were plotted to illustrate incident cardiovascular disease (CVD) per treatment arm in the overall sample and in a subgroup with high ΔHR (defined as ΔHR ⩾ 6 BPM; n.b., 6 BPM is the mean ΔHR, calculated on a log scale and back-transformed, and is also equal to the median); log-rank tests were used to assess differences between survival curves (secondary analysis only).
Opening of an airway at the end of obstructive events is associated with cardiovascular and respiratory changes that can alter the heart rate (17–20). Given our understanding that more severe respiratory events are expected to yield greater surges in heart rate (16), we performed sensitivity analyses to assess whether ΔHR normalized by event severity measures (desaturation area, arousal intensity, event duration; see online supplement for more details) also moderated the benefit of CPAP (normalized ΔHR indices were log-transformed and modeled continuously). Additional sensitivity analyses separately adjusted the primary model for these event severity measures, use of β-blockers, angiotensin-converting enzyme inhibitors, lipid-lowering medications, and warfarin and tested the effect of adherence to therapy on the primary outcome, both in the overall sample and in ΔHR-informed subgroups (ΔHR < 6 BPM versus ΔHR ⩾ 6 BPM). Treatment-adjusted hazard ratios were quantified for 1-year average nightly adherence of 0–4 versus ⩾4 hours (9). Finally, additional sensitivity analyses excluded individuals with chronic atrial fibrillation in whom the measurement of the ΔHR was unreliable. All statistical analyses were conducted using the R statistical package (http://www.r-project.org), and statistical significance was accepted at P < 0.05 for the primary analysis. All other analyses were considered exploratory.
Results
Participants’ characteristics per treatment arm and per treatment arm and ΔHR categories are shown in Tables 1 and 2, respectively. There were no statistically significant differences in baseline characteristics among different groups (Table 1). Similarly, cardiovascular (CV)-related baseline characteristics were similar between patients with higher versus lower ΔHR (Table 2). In the entire sample, 89% were on β-blockers, and a vast majority had PCI before the baseline visit. All sleep study parameters were similar between treatment arms.
Table 1.
Baseline Characteristics of the RICCADSA Randomized Controlled Trial
| No CPAP (n = 113) | CPAP (n = 113) | |
|---|---|---|
| Age, yr | 67.3 (60.7–73.2) | 66.3 (60.5–71.0) |
| Female sex, n (%) | 15 (13.3) | 18 (15.9) |
| BMI, kg/m2 | 28.7 (26.7–30.6) | 28.1 (25.9–30.2) |
| Current smoker, n (%) | 14 (12.4) | 20 (17.7) |
| Hypertension, n (%) | 66 (58.4) | 75 (66.4) |
| Diabetes, n (%) | 24 (21.2) | 29 (25.7) |
| Intervention | ||
| PCI, n (%) | 84 (74.3) | 83 (73.5) |
| CABG, n (%) | 29 (25.7) | 30 (26.5) |
| Acute MI, n (%) | 51 (45.1) | 61 (54.0) |
| AHIEmbletta, events/h | 25.0 (19.2–35.3) | 23.8 (18.0–36.2) |
| AHIPSG, events/h | 36.6 (24.2–61.3) | 31.6 (21.5–53.6) |
| Hypoxic burden, % min/h | 56.7 (34.4–105) | 58.1 (34.6–110) |
| ΔHR, beats/min | 6.2 (4.9–8.2) | 6.6 (4.8–8.9) |
| Arousal intensity | 4.17 (0.84) | 4.13 (0.90) |
| Event duration, s | 20.6 (18.4–23.0) | 20.4 (17.9–24.2) |
| ΔHR/desaturation severity | 4.1 (2.8–5.3) | 3.6 (2.4–5.1) |
| ΔHR/arousal intensity | 1.45 (1.12–2.10) | 1.57 (1.18–2.27) |
| ΔHR/event duration | 0.31 (0.23–0.40) | 0.31 (0.23–0.40) |
Definition of abbreviations: AHI = apnea–hypopnea index; BMI = body mass index; CABG = coronary artery bypass graft surgery; CPAP = continuous positive airway pressure; ΔHR = pulse rate response to respiratory events; MI = myocardial infarction; PCI = percutaneous coronary intervention; PSG = polysomnography; RICCADSA = Randomized Intervention with CPAP in CAD and OSA.
Average values are mean (SD or proportion) or median (interquartile range).
Table 2.
Baseline Characteristics of the RICCADSA Randomized Controlled Trial per Treatment Arms and Pulse Rate Response to Respiratory Events
| No CPAP (n = 113) |
CPAP (n = 113) |
P Overall | P (ΔHR ⩾ 6) No CPAP vs. CPAP | P (ΔHR < 6) No CPAP vs. CPAP | |||
|---|---|---|---|---|---|---|---|
| ΔHR < 6 BPM (n = 52) | ΔHR ⩾ 6 BPM (n = 61) | ΔHR < 6 BPM (n = 49) | ΔHR ⩾ 6 BPM (n = 64) | ||||
| Age, yr | 66.2 (60.9–72.2) | 67.6 (60.7–73.9) | 67.7 (61.7–72.7) | 65.1 (59.2–69.6) | 0.239 | 0.239 | 0.685 |
| Female sex, n (%) | 9 (17.3) | 6 (9.84) | 8 (16.3) | 10 (15.6) | 0.662 | 0.967 | 1.00 |
| BMI, kg/m2 | 29.4 (26.7–30.8) | 28.0 (26.6–30.1) | 28.8 (26.1–30.2) | 27.7 (25.7–30.3) | 0.285 | 0.721 | 0.721 |
| Current smoker, n (%) | 5 (9.62) | 9 (14.8) | 5 (10.2) | 15 (23.4) | 0.131 | 0.630 | 1.000 |
| Hypertension, n (%) | 30 (57.7) | 36 (59.0) | 34 (69.4) | 41 (64.1) | 0.597 | 0.833 | 0.833 |
| Diabetes, n (%) | 11 (21.2) | 13 (21.3) | 14 (28.6) | 15 (23.4) | 0.795 | 1.000 | 1.000 |
| Intervention | 0.839 | 1.000 | 1.000 | ||||
| PCI, n (%) | 38 (73.1) | 46 (75.4) | 34 (69.4) | 49 (76.6) | |||
| CABG, n (%) | 14 (26.9) | 15 (24.6) | 15 (30.6) | 15 (23.4) | |||
| Acute MI, n (%) | 22 (42.3) | 29 (47.5) | 23 (46.9) | 38 (59.4) | 0.285 | 0.522 | 0.947 |
| AHIEmbletta, events/h | 24.8 (19.2–35.0) | 25.4 (18.6–36.0) | 24.6 (19.4–37.6) | 23.4 (17.8–31.4) | 0.642 | 0.831 | 0.921 |
| AHIPSG, events/h | 35.6 (25.0–52.3) | 37.1 (23.8–66.5) | 42.6 (28.3–61.5) | 27.1 (18.1–48.1) | 0.072 | 0.148 | 0.682 |
| Hypoxic burden, %, min/h | 53.4 (34.8–101) | 58.5 (34.3–105) | 62.7 (34.0–112) | 53.8 (36.5–102) | 0.932 | 0.935 | 0.935 |
| ΔHR, beats/min | 4.60 (3.89–5.35) | 8.12 (6.96–10.3) | 4.70 (3.86–5.33) | 8.32 (7.02–11.0) | <0.001 | 0.941 | 1.000 |
| Arousal intensity, mean (SD) | 4.21 (0.79) | 4.14 (0.89) | 4.01 (0.92) | 4.22 (0.88) | 0.586 | 0.954 | 0.648 |
| Event duration, s | 20.4 (17.2–22.7) | 21.0 (18.5–23.6) | 18.7 (17.0–21.9) | 22.1 (18.3–25.8) | 0.022 | 0.318 | 0.318 |
| ΔHR/hypoxia | 2.78 (2.02–4.20) | 4.76 (3.94–6.46) | 2.58 (1.96–3.99) | 4.53 (3.27–6.50) | <0.001 | 0.343 | 0.755 |
| ΔHR/arousal intensity | 1.07 (0.91–1.32) | 1.95 (1.62–2.60) | 1.13 (0.91–1.31) | 2.04 (1.66–2.63) | <0.001 | 0.867 | 0.697 |
| ΔHR/event duration | 0.23 (0.17–0.26) | 0.40 (0.33–0.47) | 0.23 (0.18–0.29) | 0.39 (0.31–0.52) | <0.001 | 0.929 | 0.715 |
For definition of abbreviations, see Table 1.
For all continuous variables, a Shapiro-Wilk test was performed to decide between normal or nonnormal distribution. The average values for normal distributions are mean (SD), and the statistical tests were ANOVA. The average values for nonnormal distributions are median (interquartile range), and the statistical tests were Kruskall-Wallis tests. Finally, for categorical variables, the numbers (proportions) are shown and the chi-square or exact Fisher test (when the expected frequencies was <5) were used.
Primary Outcome Analysis
As shown in Table 3, significant interactions were observed between treatment and ΔHR (P-interaction = 0.022). Patients with a higher ΔHR, if untreated, were at higher risk for CVD (hazard ratio [95% confidence interval (CI)] per 1-SD increase in ΔHR: 1.55 [1.02 to 2.34]; Table 3) and exhibited greater cardiovascular benefit from CPAP therapy than those with lower ΔHR responses. At an elevated ΔHR (i.e., 10 BPM, point estimate), the calculated CPAP-related risk reduction was 59% (6% to 82%; P < 0.05) in contrast to a nonsignificant small effect at average ΔHR (i.e., 6 BPM; point estimate 16% [−53% to 54%] reduction in risk; P = 0.56; Table 3).
Table 3.
The Primary Cox Regression Model Testing Treatment Benefit at Levels of Heart Rate Response to Events
| Predictors | Coefficient (95% CI) | Hazard Ratio (95% CI) | P Value |
|---|---|---|---|
| ΔHR (per 1 SD) | 0.44 (0.02 to 0.85) | 1.55 (1.02 to 2.34) | 0.039 |
| CPAP | −0.18 (−0.78 to 0.42) | 0.84 (0.46 to 1.53) | 0.562 |
| CPAP × ΔHR | −0.71 (−1.32 to −0.10) | 0.49 (0.27 to 0.90) | 0.022 |
| Calculated CPAP Risk Reduction at Different Levels of ΔHR | % Risk Reduction (95% CI) | P Value | |
|---|---|---|---|
| At mean value | 16 (−53 to 54) | 0.562 | |
| At mean + 1 SD value | 59 (6 to 82) | 0.036 | |
Definition of abbreviations: BPM = beats per minute; CI = confidence interval; CPAP = continuous positive airway pressure; ΔHR = pulse rate response to respiratory events.
The primary endpoint was defined as a composite of repeat revascularization, myocardial infarction, stroke, and cardiovascular mortality. ΔHR was log-transformed and modeled continuously. Risk reduction was calculated at mean ΔHR (log-transformed), equivalent to 6 BPM, and mean + 1 SD ΔHR, equivalent to 10 BPM. All models were adjusted for age, sex, body mass index, and cardiac intervention (percutaneous coronary intervention or coronary artery bypass graft surgery). The hazard ratio of CPAP at mean − 1 SD ΔHR was 1.71 (0.71 to 4.12) (P = 0.232), suggesting possible harm in those with low ΔHR. Bold denotes significant associations (P < 0.05).
Exploratory Subgroup Analyses
To explore the potential for ΔHR to identify patients who are most likely to benefit from CPAP, we defined two subgroups based on baseline ΔHR above or below the average value of 6 BPM. The mean (−1 SD, +1 SD) of ΔHR in patients exceeding this threshold was 9 (7, 12) (calculated on log scale and back-transformed), consistent with the point estimate of 10 BPM used in the primary analysis above (Table 3). Unadjusted Kaplan-Meier survival curves illustrate an exclusive risk reduction effect of CPAP in the subgroup of patients with ΔHR ⩾ 6 BPM (Figure 3). Similar to Cox regression findings, there was no significant effect of CPAP in all comers, whereas in those with ΔHR ⩾ 6 BPM, a significant and large effect (i.e., >50% risk reduction) of CPAP was observed. It is worth noting that in those with a ΔHR < 6 BPM (subgroup mean, 4 [3, 6] BPM), there was a nonsignificant suggestion of possible harm from CPAP (hazard ratio, 1.78 [0.69–4.55]; P = 0.23), which was similarly seen in primary analysis (hazard ratio, 1.71 [0.71–4.12] at point estimate of 4 BPM; i.e., mean − 1 SD; Table 3).
Figure 3.
Unadjusted Kaplan-Meier survival curve demonstrates a preferential benefit of continuous positive airway pressure (CPAP) in those with high pulse rate response to respiratory events (ΔHR; ΔHR ⩾ 6 BPM). No significant effect was observed in all comers. There was a suggestion of possible harm from CPAP in those with a low ΔHR (ΔHR < 6 BPM). BPM = beats per minute; CVD = cardiovascular disease; ΔHR = pulse rate response to respiratory events.
Stronger effects were suggested for either normalized ΔHRs or ΔHR adjusted by event severity measures (Table 4). For example, the interaction hazard ratio (95% CI) for ΔHR normalized by arousal intensity was 0.41 (0.22–0.75) (P < 0.01), translating to a CPAP-related risk reduction of 67% (22–86%) at elevated (mean + 1 SD) normalized ΔHR by arousal intensity (Table 4). Adjustment for event severity metrics in alternative models yielded similar findings (Table 4). Similarly, adjustment for use of β-blockers (Table E1) or other medications, including angiotensin-converting enzyme inhibitors, lipid-lowering medications, and warfarin (Table E2), yielded similar findings. The sensitivity analysis, shown in Table E3, provides further variation in outcomes by adherence to CPAP. Although nonsignificant, in the entire sample, as well as in the subgroup with high ΔHR, there was a suggestion of decreased mortality in those with higher adherence (i.e., the CPAP hazard ratio for CVD was lower in high ΔHR than in the overall sample). Finally, excluding individuals with chronic atrial fibrillation and an unreliable measurement of ΔHR (n = 4) did not affect our findings in a meaningful way (Table E4).
Table 4.
Interaction P Value for the Primary Endpoint and Treatment Benefit at Different Levels of Normalized Heart Rate Response to Events
| Predictors | CPAP Interaction P Value | CPAP Risk Reduction at Mean Value (% Reduction [95% CI]) | CPAP Risk Reduction at Mean + 1 SD (% Reduction [95% CI]) |
|---|---|---|---|
| Normalized versions of ΔHR | |||
| ΔHR/desaturation area | 0.011 | 23 (−43 to 59) | 67 (17 to 87) |
| ΔHR/arousal intensity | 0.004 | 18 (−51 to 56) | 67 (22 to 86) |
| ΔHR/event duration | 0.014 | 14 (−57 to 53) | 60 (9 to 82) |
| ΔHR adjusted by | |||
| Desaturation area | 0.017 | 21 (−47 to 58) | 64 (13 to 85) |
| Arousal intensity | 0.010 | 20 (−49 to 57) | 65 (16 to 85) |
| Event duration | 0.015 | 14 (−57 to 53) | 61 (10 to 83) |
Definition of abbreviations: CI = confidence interval; CPAP = continuous positive airway pressure; ΔHR = pulse rate response to respiratory events.
Desaturation area indicates the respiratory event–related desaturation area. The primary endpoint was defined as a composite of repeat revascularization, myocardial infarction, stroke, and cardiovascular mortality. All models were further adjusted for age, sex, body mass index, cardiac intervention (percutaneous coronary intervention or coronary artery bypass graft surgery). All predictors were log-transformed and modeled continuously. Risk reduction was calculated from the Cox regression model including interaction terms. Bold denotes significant associations (P < 0.05).
Discussion
In the current secondary analysis of a randomized controlled trial of CPAP in patients with CAD with OSA and no excessive daytime sleepiness, we demonstrate a distinct protective effect of CPAP on adverse cardiovascular outcomes in those who exhibit greater pretreatment heart rate responses to OSA-related respiratory events. Our study provides novel evidence that a greater heart rate responsiveness to obstructive events is a readily identifiable, deleterious, and potentially reversible risk factor for adverse outcomes. This new metric may help select patients most likely to exhibit long-term cardiovascular benefit from CPAP therapy.
Despite the strong evidence of elevated risk of harmful CVD outcomes in individuals with OSA, this risk is largely heterogeneous (21, 22), a factor that may contribute to the overall null findings of clinical trials of OSA (9, 23, 24). Although adherence to treatment is an issue, a recent meta-analysis demonstrated no associations between CPAP and CVD outcomes for different levels of CPAP adherence (25). Hence, it has been argued that CPAP treatment for all comers with OSA for the sole purpose of secondary CVD prevention should not be supported. Coupled with suboptimal adherence to CPAP, determining whom to treat is one of the most challenging tasks in clinical sleep medicine.
Data to date do not support using AHI measurements to identify high-risk individuals within OSA, spurring the search for additional predictive phenotypes that would inform the identification of high-risk individuals likely to benefit from early and aggressive interventions (9, 23, 24). To better risk stratify individuals with OSA, we have recently shown that there is a U-shaped relationship between ΔHR and risk of CVD morbidity and mortality in the Sleep Heart Health Study and the Multi-Ethnic Study of Atherosclerosis (4). These data suggested that those with a low and high ΔHR were at elevated risk but that only the risk associated with high ΔHR was OSA specific and could potentially be reversed by CPAP treatment (4). Indeed, our previous findings show that those with a high ΔHR may have a more severe form of OSA, as evidenced by greater hypoxemia, more frequent and/or intense arousals, and longer respiratory events, aspects of OSA that are inherently treatable (4). The findings from this study confirm the hypothesis that individuals with a high ΔHR are not only at increased risk for OSA-related adverse outcomes but also preferentially benefit from CPAP therapy compared with those with a low ΔHR (Table 3). Indeed, our findings may suggest possible harm from CPAP in the subgroup with a low ΔHR. Although these findings merit further research, the inclusion of people with a low ΔHR in other RCTs, including the Sleep Apnea Cardiovascular Endpoints (SAVE) (23) and Impact of Sleep Apnea syndrome in the evolution of Acute Coronary (ISAACC) syndrome. Effect of intervention with CPAP (24), may be a reason for null findings in these trials. Although we caution overinterpretation of CPAP harm in the low ΔHR subgroup, a potential explanation may be that there are potential benefits as well as harms to intermittent hypoxia (e.g., benefit of hypoxic preconditioning) and potential benefits and harms to CPAP administration (e.g., possible distraction from other disease management); in the absence of the CV benefit of OSA treatment in the presence of higher ΔHR, our data lead us to speculate that harm might outweigh benefit in those without higher ΔHR.
In addition to being a marker of more severe respiratory events (4, 16), a high ΔHR could potentially reflect an overreactive autonomic nervous system (26). We attempted to distinguish these underlying mechanisms by normalizing ΔHR by metrics that capture severity of respiratory events (i.e., desaturation severity, arousal intensity, and event duration). In all cases, significant and somewhat stronger interactions with treatment outcomes were observed (Table 3). This suggests that the predictive value of ΔHR (responsiveness) persists even after normalizing and/or adjusting for multiple markers of event severity. Indeed, although CPAP or other similar treatments may not modify the specific heart rate response to respiratory events, these treatments eliminate or reduce the stimuli (i.e., respiratory events) that trigger autonomic responses, thus potentially reducing long-term stress on the cardiovascular system.
Finally, although some secondary analyses of previous RCTs have demonstrated a larger benefit of therapy in CPAP-adherent individuals (usually ⩾4 h/night), our analysis did not rely on a post hoc subgroup definition based on CPAP adherence to detect a CVD benefit. Such selection of participants based on CPAP adherence introduces a healthy user bias (27), which is notably not at play in the primary analysis here. Nonetheless, in our sensitivity analysis examining adherence, we observed a trend for stronger therapeutic benefit in patients who were adherent to therapy.
Our sensitivity analysis shows that although β-blockers could potentially attenuate ΔHR, it appears that adjusting for β-blockers does not affect the associations between ΔHR and CVD outcomes (4) or CPAP-related reduction in CVD risk (Table E1). Indeed, the mean (SD) ΔHR in individuals with moderate to severe sleep apnea (AHI ⩾ 15 events/h) who were not on β-blockers was 7.8 (3.5) BPM (data from the Sleep Heart Health Study). A slightly lower mean (SD) of 7.0 (3.3) BPM was noted in this study. Finally, the proportion of individuals on β-blockers was not significantly different in the subgroups ΔHR < 10 BPM versus ⩾10 BPM. For example, 89.5% of individuals with ΔHR ⩾ 10 BPM in the CPAP group were on β-blockers, which was similar to the entire sample.
Strengths and Limitations
This study has several strengths, including 1) the simplicity of ΔHR measurement that enables use of HSAT devices that measure airflow and pulse rate as opposed to other metrics that may require in-laboratory polysomnography; and 2) use of an automated, subject-specific search window to link the heart rate responses with apneas or hypopneas. However, the study also had several limitations, including the relatively small sample size that may limit the generalizability of our findings. We also analyzed data with underrepresentation of women and members from underrepresented minority groups. Indeed, women and African Americans have the shortest respiratory event duration (13), and their low arousal threshold may represent cardiovascular risk (and CPAP benefit) that requires additional study. Finally, given the potential utility of ΔHR as a means to assess OSA-related CV risk, we encourage the development of methods to assess ΔHR reliability.
Conclusions
A protective effect of CPAP on adverse cardiovascular outcomes was shown in the patients with CAD with nonsleepy OSA who exhibit the most exaggerated surges in heart rate in response to apneas and hypopneas. Our study provides novel high-quality evidence that an elevated heart rate response to obstructive events is a recognizable and deleterious trait that could be used to select high-risk patients most likely to exhibit long-term cardiovascular benefit from CPAP therapy.
Acknowledgments
Acknowledgment
The authors thank all those involved in the data collection of the parent study in Sweden, especially Erik Thunström, M.D., Ph.D., and Helena Glantz, M.D., Ph.D., for patient recruitment; sleep technologist Lena Andersson for precise conductance and coordination of the polysomnography recordings; and sleep technologist Paul Murphy for scoring of the polysomnography recordings. They also thank John Denos, Lauren Hess, and Erik Smales for meticulous scoring of polysomnograms and James Kim for assistance with data exports.
Footnotes
Supported by American Heart Association grant 19CDA34660137, NIH grant R01HL153874, and American Academy of Sleep Medicine Foundation grant 188-SR-17 (A.A.); NIH NHLBI grant R01HL146697 and American Academy of Sleep Medicine Foundation grant 228-SR-20 (S.A.S.); NHLBI grant R35HL135818 (S.R., S.A.S., A.W., and A.A.); the Parker B. Francis foundation fellowship award (A.Z.); NIH grants R01HL102321, R01HL128658, P01HL095491, and UL1RR025758; Swedish Research Council grants 521-2011-537 and 521-2013-3439, ResMed Foundation for the parent RICCADSA study in Sweden, and Swedish Heart-Lung Foundation grants 20080592, 20090708, and 20100664 (Y.P.).
Author Contributions: A.A. was responsible for the study design, analysis, interpretation of data, and drafting the manuscript. Y.P. designed the parent RICCADSA trial in 2005 and performed the patient recruitment and clinical follow-ups in Sweden. A.Z. and S.A.S. contributed to the study design, analysis, interpretation of the data, and critical revision of the manuscript. All authors contributed to the interpretation of the data and critical revision of the manuscript. A.A. and S.A.S. are responsible for the current work as a whole, including the study design, access to data, and the decision to submit and publish the manuscript. All authors approved this manuscript in its final form.
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.202111-2608OC on May 17, 2022
Author disclosures are available with the text of this article at www.atsjournals.org.
References
- 1. Benjafield AV, Ayas NT, Eastwood PR, Heinzer R, Ip MSM, Morrell MJ, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med . 2019;7:687–698. doi: 10.1016/S2213-2600(19)30198-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Veasey SC, Rosen IM. Obstructive sleep apnea in adults. N Engl J Med . 2019;380:1442–1449. doi: 10.1056/NEJMcp1816152. [DOI] [PubMed] [Google Scholar]
- 3. Azarbarzin A, Sands SA, Stone KL, Taranto-Montemurro L, Messineo L, Terrill PI, et al. The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the Osteoporotic Fractures in Men Study and the Sleep Heart Health Study. Eur Heart J . 2019;40:1149–1157. doi: 10.1093/eurheartj/ehy624. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Azarbarzin A, Sands SA, Younes M, Taranto-Montemurro L, Sofer T, Vena D, et al. The sleep apnea-specific pulse-rate response predicts cardiovascular morbidity and mortality. Am J Respir Crit Care Med . 2021;203:1546–1555. doi: 10.1164/rccm.202010-3900OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Campos-Rodriguez F, Martinez-Garcia MA, de la Cruz-Moron I, Almeida-Gonzalez C, Catalan-Serra P, Montserrat JM. Cardiovascular mortality in women with obstructive sleep apnea with or without continuous positive airway pressure treatment: a cohort study. Ann Intern Med . 2012;156:115–122. doi: 10.7326/0003-4819-156-2-201201170-00006. [DOI] [PubMed] [Google Scholar]
- 6. Marin JM, Carrizo SJ, Vicente E, Agusti AG. Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet . 2005;365:1046–1053. doi: 10.1016/S0140-6736(05)71141-7. [DOI] [PubMed] [Google Scholar]
- 7. Kapur VK, Baldwin CM, Resnick HE, Gottlieb DJ, Nieto FJ. Sleepiness in patients with moderate to severe sleep-disordered breathing. Sleep . 2005;28:472–477. doi: 10.1093/sleep/28.4.472. [DOI] [PubMed] [Google Scholar]
- 8. Punjabi NM. COUNTERPOINT: is the apnea-hypopnea index the best way to quantify the severity of sleep-disordered breathing? No. Chest . 2016;149:16–19. doi: 10.1378/chest.14-2261. [DOI] [PubMed] [Google Scholar]
- 9. Peker Y, Glantz H, Eulenburg C, Wegscheider K, Herlitz J, Thunström E. Effect of positive airway pressure on cardiovascular outcomes in coronary artery disease patients with nonsleepy obstructive sleep apnea: the RICCADSA randomized controlled trial. Am J Respir Crit Care Med . 2016;194:613–620. doi: 10.1164/rccm.201601-0088OC. [DOI] [PubMed] [Google Scholar]
- 10. Azarbarzin A, Sands SA, Taranto-Montemurro L, Vena D, Sofer T, Kim SW, et al. The sleep apnea-specific hypoxic burden predicts incident heart failure. Chest . 2020;158:739–750. doi: 10.1016/j.chest.2020.03.053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Azarbarzin A, Ostrowski M, Hanly P, Younes M. Relationship between arousal intensity and heart rate response to arousal. Sleep (Basel) . 2014;37:645–653. doi: 10.5665/sleep.3560. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Azarbarzin A, Ostrowski M, Younes M, Keenan BT, Pack AI, Staley B, et al. Arousal responses during overnight polysomnography and their reproducibility in healthy young adults. Sleep (Basel) . 2015;38:1313–1321. doi: 10.5665/sleep.4916. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Borker PV, Reid M, Sofer T, Butler MP, Azarbarzin A, Wang H, et al. Non-REM apnea and hypopnea duration varies across population groups and physiologic traits. Am J Respir Crit Care Med . 2021;203:1173–1182. doi: 10.1164/rccm.202005-1808OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Azarbarzin A, Zinchuk A, Wellman DA, Taranto Montemurro L, Vena D, Gell L, et al. Cardiovascular benefit of CPAP is modified by the sleep apnea related pulse rate response in coronary artery disease patients with nonsleepy OSA: findings from the RICCADSA randomized controlled trial [abstract] Am J Respir Crit Care Med . 2021;203:A1103. [Google Scholar]
- 15. Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research: the report of an American Academy of Sleep Medicine task force. Sleep . 1999;22:667–689. [PubMed] [Google Scholar]
- 16. Azarbarzin A, Ostrowski M, Moussavi Z, Hanly P, Younes M. Contribution of arousal from sleep to postevent tachycardia in patients with obstructive sleep apnea. Sleep (Basel) . 2013;36:881–889. doi: 10.5665/sleep.2716. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Somers VK, Dyken ME, Clary MP, Abboud FM. Sympathetic neural mechanisms in obstructive sleep apnea. J Clin Invest . 1995;96:1897–1904. doi: 10.1172/JCI118235. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Stoohs R, Guilleminault C. Cardiovascular changes associated with obstructive sleep apnea syndrome. J Appl Physiol (1985) . 1992;72:583–589. doi: 10.1152/jappl.1992.72.2.583. [DOI] [PubMed] [Google Scholar]
- 19. Zwillich C, Devlin T, White D, Douglas N, Weil J, Martin R. Bradycardia during sleep apnea. Characteristics and mechanism. J Clin Invest . 1982;69:1286–1292. doi: 10.1172/JCI110568. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Venkataraman S, Vungarala S, Covassin N, Somers VK. Sleep apnea, hypertension and the sympathetic nervous system in the adult population. J Clin Med . 2020;9:E591. doi: 10.3390/jcm9020591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Phillipson EA. Sleep apnea: a major public health problem. N Engl J Med . 1993;328:1271–1273. doi: 10.1056/NEJM199304293281712. [DOI] [PubMed] [Google Scholar]
- 22. Zapater A, Sánchez-de-la-Torre M, Benítez ID, Targa A, Bertran S, Torres G, et al. Spanish Sleep Network The effect of sleep apnea on cardiovascular events in different acute coronary syndrome phenotypes. Am J Respir Crit Care Med . 2020;202:1698–1706. doi: 10.1164/rccm.202004-1127OC. [DOI] [PubMed] [Google Scholar]
- 23. McEvoy RD, Antic NA, Heeley E, Luo Y, Ou Q, Zhang X, et al. SAVE Investigators and Coordinators CPAP for prevention of cardiovascular events in obstructive sleep apnea. N Engl J Med . 2016;375:919–931. doi: 10.1056/NEJMoa1606599. [DOI] [PubMed] [Google Scholar]
- 24. Sánchez-de-la-Torre M, Sánchez-de-la-Torre A, Bertran S, Abad J, Duran-Cantolla J, Cabriada V, et al. Spanish Sleep Network Effect of obstructive sleep apnoea and its treatment with continuous positive airway pressure on the prevalence of cardiovascular events in patients with acute coronary syndrome (ISAACC study): a randomised controlled trial. Lancet Respir Med . 2020;8:359–367. doi: 10.1016/S2213-2600(19)30271-1. [DOI] [PubMed] [Google Scholar]
- 25. Yu J, Zhou Z, McEvoy RD, Anderson CS, Rodgers A, Perkovic V, et al. Association of positive airway pressure with cardiovascular events and death in adults with sleep apnea: a systematic review and meta-analysis. JAMA . 2017;318:156–166. doi: 10.1001/jama.2017.7967. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Narkiewicz K, Somers VK. Sympathetic nerve activity in obstructive sleep apnoea. Acta Physiol Scand . 2003;177:385–390. doi: 10.1046/j.1365-201X.2003.01091.x. [DOI] [PubMed] [Google Scholar]
- 27. Shrank WH, Patrick AR, Brookhart MA. Healthy user and related biases in observational studies of preventive interventions: a primer for physicians. J Gen Intern Med . 2011;26:546–550. doi: 10.1007/s11606-010-1609-1. [DOI] [PMC free article] [PubMed] [Google Scholar]