Graphical abstract
Overview of the study. CPRD: Clinical Practice Research Datalink; OSA: obstructive sleep apnoea; CVD: cardiovascular disease; ACS: acute coronary syndrome.
Abstract
Rationale
COPD and obstructive sleep apnoea (OSA) are both independently associated with an increased risk of cardiovascular disease. It is not clear whether this risk is further increased in COPD–OSA overlap. We aimed to determine whether COPD–OSA overlap has a higher cardiovascular burden compared with COPD or OSA alone.
Methods
This was a matched cohort study using electronic health records from the Clinical Practice Research Datalink Aurum database (England). Exposures of interest were a diagnosis of COPD, OSA or both based on validated code lists. Our primary outcome of interest was cardiovascular mortality defined using ICD-10 codes. Our secondary outcomes of interest included: all-cause mortality, incident acute coronary syndrome (ACS), incident stroke and incident heart failure.
Results
The initial study cohort consisted of 365 667 people with COPD (mean±sd) age 68±11 years, 59 526 with OSA age 57±11 years and 11 040 with COPD–OSA age 65±10 years. Cardiovascular mortality was significantly higher in patients with COPD–OSA overlap compared with OSA alone (adjusted hazard ratio 1.72 (1.58–1.88); p<0.001) but not compared with COPD alone. People with COPD–OSA overlap had 27% and 58% increased risk of developing ACS compared with COPD and OSA alone respectively, and 69% and 101% increased risk of developing heart failure compared with COPD and OSA respectively.
Conclusions
We have demonstrated that people with COPD–OSA overlap have a significantly higher cardiovascular burden compared with COPD or OSA alone with real-world implications. Earlier and targeted treatment with cardiovascular optimisation can potentially reduce downstream sequelae.
Shareable abstract
Patients with COPD–obstructive sleep apnoea (OSA) overlap syndrome have a significantly higher cardiovascular mortality compared with OSA alone but not compared with COPD alone, necessitating earlier prevention strategies https://bit.ly/3QayPpz
Introduction
COPD and obstructive sleep apnoea (OSA) are common respiratory conditions, with a global prevalence of approximately 392 million and one billion respectively [1, 2]. COPD and OSA are both independently associated with an increased cardiovascular burden secondary to a combination of intermittent hypoxia, systemic inflammation, increased oxidative stress and hyperinflation [3–5]. The combined presence of COPD and OSA in the same individual is known as COPD–OSA overlap, a term first coined in 1985 [6]. COPD–OSA overlap is highly prevalent when individual populations of either COPD (2.9–65.9%) or OSA (7.6–55.7%) are specifically assessed [7]. While it is probable that COPD–OSA overlap leads to an increased cardiovascular burden compared with COPD or OSA alone, the evidence is unclear. A systematic review [8] (n=18 studies) concluded that COPD–OSA overlap was associated with an increased risk of hypertension compared with COPD alone (OR 1.68, 95% CI 1.21–2.35); peripheral vascular disease compared with OSA alone (OR 3.30, 95% CI 2.66–4.10); and some studies pointed to an increased risk of ischaemic heart disease, heart failure and stroke. However, studies were heterogeneous, small, did not adjust for confounders, furthermore only one investigated mortality.
If there is an increased cardiovascular risk in COPD–OSA overlap, then early diagnosis, screening of relevant population groups and prompt management may improve outcomes. Treatment of OSA syndrome with continuous positive airway pressure therapy significantly reduces major adverse cardiovascular events (MACEs, relative risk 0.43, 0.23–0.80) [9]; there is clear evidence that smoking cessation reduces MACE risk in patients with COPD, [5] and secondary analysis of clinical trials with triple inhaled therapy in COPD also concluded this [10]. Thus, early identification of COPD–OSA overlap could lead to targeted therapy, to reduce MACE risk. However, given the paucity of data surrounding cardiovascular mortality rates in COPD–OSA overlap, and the poor-quality data currently available for cardiovascular morbidity, there is a need for high-quality population data. This has been highlighted as a key research priority by the American Thoracic Society [11]. Analysis of large-scale electronic health records provides the best opportunity for this, as they allow analysis of a large cohort of patients with longitudinal data including cardiovascular mortality and morbidity.
Therefore, using electronic health records we examined whether patients with COPD–OSA overlap have higher cardiovascular mortality and morbidity compared with COPD or OSA alone.
Methods
Study design
This was a matched cohort study to examine the rate of cardiovascular mortality, all-cause mortality, incident ACS, stroke and heart failure, comparing people with COPD–OSA overlap with COPD alone, OSA alone and those with no history of COPD or OSA.
Ethical approval
The study was approved by the UK Medicines and Healthcare Products Regulatory Agency (MHRA) Independent Scientific Advisory Committee (RDG 23_002767), under Section 251 (National Health Service (NHS) Social Care Act 2006). This study was carried out as part of the CALIBER© resource. CALIBER, led by the University College London Institute of Health Informatics, is a research resource providing validated electronic health record phenotyping algorithms and tools for national structured data sources [12, 13].
This study is based in part on data from the Clinical Practice Research Datalink obtained under licence from the UK MHRA.
Data sources
We used data from the Clinical Practice Research Datalink (CPRD) Aurum (version September 2023) [14]. This database contains pseudo-anonymised data from primary care practices in England covering roughly 13% of the population and has data on over 15 million patients. Participants were limited to those with linkage to the Hospital Episode Statistics and Office for National Statistics mortality database. Data on distribution, in terms of demographics (age, sex, diversity and location), prevalence and management, mirror the overall population of the UK, and validation on the reliability of the data has been previously published [15, 16].
Study population
The entry date into the study was the latest of 1 January 1999, 12 months after entry into the CPRD, or the first record of the exposure of interest. The 12-month washout period was designed to avoid the time where individuals join the primary care research database, as it has been previously associated with poor health recordings [17]. Patients were only included if they were ≥35 years of age. This cut-off was chosen as the current UK National Institute for Health and Care Excellence (NICE) recommends suspecting a diagnosis of COPD in people aged above 35 years of age [18]. Patients were followed up until the earliest of the date of death, the date a patient's care was transferred out of a CPRD practice, the practice last collection date or July 2023.
Definition of exposures/controls
Patients’ entry date into the cohort was based on a new primary care diagnosis of COPD, OSA or COPD–OSA overlap within the study period. A diagnosis of COPD was defined using previously published CPRD Aurum phenotyping algorithms, which mirror validated CPRD Gold Phenotype algorithms, with an estimated positive predictive value of 87% [19, 20]. A diagnosis of OSA was defined as the earliest date on which the patient had a sleep apnoea diagnosis. Sleep apnoea was defined by mirroring validated previously published CPRD Gold Phenotype algorithm by Kuan et al. [21], and more recently published validation of these codes, which showed a positive predictive value of 85% (95% CI 77–91) [22]. Treatment codes were also added to this to further improve accuracy. Central sleep apnoea and mixed sleep apnoea codes were excluded in our definition. COPD–OSA overlap diagnosis was defined as a combination of both COPD and OSA using the generated phenotypes. The point of entry into the study was the date the second diagnosis resulting in overlap was made. The entry point was fixed and individuals in the COPD or OSA groups could not transition into the COPD–OSA overlap group. A schematic timeline is shown in the supplementary figure S1. Phenotype algorithms used for this study can be found in the supplementary material.
Exposure density matching was used. For each participant in each of the three groups, we sampled one patient (1:1) of the same sex and age (±3 years) from the same general practice with no previous record of COPD, OSA or COPD–OSA. The matched participant had the same cohort entry date as the corresponding participant with the relevant exposure, assuming they met the other inclusion criteria [23].
Outcomes of interest
Our primary outcome of interest was cardiovascular mortality. Cardiovascular mortality was defined using ICD-10 codes (supplementary material) in the ONS mortality database. Our secondary outcomes of interest included: all-cause mortality, incident ACS, incident stroke and incident heart failure.
All-cause mortality was defined as all death from any cause within the study period.
Incident ACS was defined as either unstable angina or acute myocardial infarction as defined by NICE UK. CPRD Aurum read codes for incident ACS, stroke and heart failure were generated using the HDR UK Phenotype library. For the incident outcomes, the prevalent cases were excluded from each analysis. Detailed explanation of these definitions and the number excluded can be seen in the supplementary material.
Covariates
We included covariates that are known risk factors for cardiovascular disease including age, sex, smoking history and known cardiovascular disease (heart failure, myocardial infarction, unstable angina and stroke), or established risk factors comprising diabetes, hypertension, obesity and hypercholesterolaemia. These covariates were defined by CPRD Aurum read codes (supplementary material). These factors were also included in the multivariable outcome models as they were clear confounding variables for cardiovascular disease (CVD) mortality and the secondary outcomes of interest.
Statistical analysis
We described the characteristics of the participants in the six groups at baseline. Kaplan–Meir survival analysis was performed for cardiovascular mortality and all-cause mortality. Mortality outcomes were analysed using Cox proportional hazard models to estimate hazard ratios (HRs) and 95% CIs. Unadjusted models were followed by multivariable models adjusted for the covariates outlined above.
For the incidence of acute coronary syndrome (ACS), stroke and heart failure, a univariate and multivariate Cox proportional hazard model was used and the HRs presented for each outcome measure. Given the high mortality rate, sensitivity analyses using a Fine-Gray model was also performed to account for competing risk of death. In less than 1% of cases, the duration of ACS, stroke and heart failure was recorded as 0 years. This suggested that either the event occurred at the same time as study entry, or this data was not reliable. These participants were excluded from analysis.
A record of absence of the covariates, was recorded as absence of the specific disease.
All the analysis was performed in MATLAB (R2020a) processing software and Stata (MP18).
Results
From 2 448 892 people who were eligible for the study, the study cohort consisted of 365 667 people with COPD (mean±sd) age 68±11 years, 59 526 with OSA age 57±11 years and 11 040 with COPD–OSA age 65±10 years. Participants were followed up for a median (IQR) duration of 5.8 (3.0–9.8) years, 6.0 (3.8–9.7) years and 5.0 (2.9–7.9) years, respectively. People with COPD–OSA overlap had a greater cardiovascular burden at baseline compared with people with COPD and/or OSA alone. The flowchart of the study population and participant baseline characteristics are illustrated in figure 1 and table 1, respectively.
FIGURE 1.
Flowchart of the study population. OSA: obstructive sleep apnoea; CPRD: Clinical Practice Research Datalink.
TABLE 1.
Participant baseline demographics
| Variable | COPD | OSA | COPD–OSA | Control non-COPD | Control non-OSA | Control non-COPD–OSA |
|---|---|---|---|---|---|---|
| Total number | 365 667 | 59 526 | 11 040 | 364 766 | 59 467 | 11 028 |
| Age at diagnosis, years | 68±11 | 57±11 | 65±10 | 69±11 | 57±11 | 65±10 |
| Male | 194 255 (53) | 43 192 (73) | 7853 (71) | 193 740 (53) | 43 160 (73) | 7842 (71) |
| Female | 171 412 (47) | 16 334 (27) | 3187 (29) | 171 026 (47) | 16 307 (27) | 3186 (29) |
| Duration of follow-up, years | 5.8 (3.0–9.8) | 6.0 (3.8–9.7) | 5.0 (2.9–7.9) | 7.0 (3.8–11.7) | 6.1 (3.9–9.9) | 5.9 (3.8–9.2) |
| Practice level region# | ||||||
| 1 | 15 897 (4.3) | 2006 (3.4) | 412 (3.7) | 15 881 (4.4) | 2004 (3.4) | 412 (3.7) |
| 2 | 85 673 (23.4) | 9084 (15.3) | 2035 (18.4) | 85 399 (23.4) | 9073 (15.3) | 2032 (18.4) |
| 3 | 14 182 (3.9) | 2463 (4.1) | 454 (4.1) | 14 165 (3.9) | 2461 (4.1) | 454 (4.1) |
| 4 | 8136 (2.2) | 1502 (2.5) | 267 (2.4) | 8117 (2.2) | 1500 (2.5) | 267 (2.4) |
| 5 | 57 898 (15.8) | 9762 (16.4) | 1883 (17.1) | 57 711 (15.8) | 9753 (16.4) | 1881 (17.1) |
| 6 | 14 612 (4.0) | 3132 (5.3) | 507 (4.6) | 14 593 (4.0) | 3128 (5.3) | 507 (4.6) |
| 7 | 57 766 (15.8) | 11 448 (19.2) | 1786 (16.2) | 57 598 (15.8) | 11 435 (19.2) | 1785 (16.2) |
| 8 | 67 361 (18.4) | 12 421 (20.9) | 2111 (19.1) | 67 287 (18.4) | 12 414 (20.9) | 2110 (19.1) |
| 9 | 44 142 (12.1) | 7708 (12.9) | 1585 (14.4) | 44 015 (12.1) | 7699 (12.9) | 1580 (14.3) |
| Smoking history | ||||||
| Never-smoker | 23 292 (6.4) | 15 179 (25.5) | 520 (4.7) | 103 324 (28.3) | 18 223 (30.6) | 2956 (26.8) |
| Ever-smoker | 338 604 (92.6) | 44 141 (74.1) | 10 509 (95.2) | 250 598 (68.7) | 40 009 (67.3) | 7884 (71.5) |
| Not recorded | 3771 (1.0) | 206 (0.3) | 11 (0.1) | 10 844 (3.0) | 1235 (2.1) | 188 (1.7) |
| Comorbidity¶ | ||||||
| Hypertension | 145 051 (39.7) | 25 799 (43.3) | 6364 (57.8) | 143 301 (39.3) | 14 377 (24.2) | 4032 (36.6) |
| Heart failure | 22 825 (6.2) | 2587 (4.3) | 1651 (15.0) | 10 869 (3.0) | 697 (1.2) | 249 (2.3) |
| Unstable angina | 7026 (1.9) | 992 (1.7) | 510 (4.6) | 4414 (1.2) | 414 (0.7) | 141 (1.3) |
| Myocardial infarction | 7179 (2.0) | 634 (1.1) | 454 (4.1) | 3368 (0.9) | 240 (0.4) | 88 (0.8) |
| Stroke | 26 097 (7.1) | 2615 (4.4) | 978 (8.9) | 20 421 (5.6) | 1366 (2.3) | 466 (4.2) |
| Lipid disorder (lipid-lowering therapy) | 138 891 (38) | 23 952 (40.2) | 7139 (64.7) | 120 998 (33.2) | 14 424 (24.3) | 4180 (37.9) |
| Diabetes | 52 715 (14.4) | 15 078 (25.3) | 4484 (40.6) | 52 508 (14.4) | 7391 (12.4) | 1872 (17.0) |
| Obesity+ | 17 397 (4.8) | 8968 (15.1) | 2491 (22.6) | 16 631 (4.6) | 2882 (4.8) | 657 (6.0) |
| CVD death | 30 774 (8.4) | 1646 (2.8) | 766 (6.9) | 25 915 (7.1) | 998 (1.7) | 332 (3.0) |
| All-cause mortality | 183 440 (50.2) | 7228 (12.1) | 3810 (34.5) | 121 932 (33.4) | 5310 (8.9) | 1652 (15.0) |
Data are presented as n (%), mean±sd or median (interquartile range). OSA: obstructive sleep apnoea; CVD: cardiovascular disease. #: Practice level is a geographical region recorded by Clinical Practice Research Datalink to indicate where in the UK the practice is based. 1: North East; 2: North West; 3: Yorkshire and The Humber; 4: East Midlands; 5: West Midlands; 6: East of England; 7: London; 8: South East; 9: South West. ¶: Comorbidities were recorded prior to COPD diagnosis. +: Patients coded as being obese in the Aurum database using GP read codes.
Cardiovascular mortality
Cardiovascular mortality was significantly higher in patients with COPD–OSA overlap compared with OSA alone (adjusted HR 1.72 (1.58–1.88); p<0.001). There were no significant differences found in the HRs when comparing COPD–OSA overlap with COPD alone (table 2). Kaplan–Meir survival analysis for cardiovascular mortality by group is illustrated in figure 2. Patients with COPD, OSA and COPD–OSA overlap had a higher cardiovascular mortality when compared with their matched controls respectively. Adjusted and unadjusted HRs are in supplementary table S1. The assumption of proportional hazards was consistent and are in supplementary figure S2. Sensitivity analysis looking at whether sex, smoking status or age made a difference to the HR are in supplementary table S2.
TABLE 2.
Hazard ratios for cardiovascular disease and all-cause mortality
| Group | Reference | Unadjusted hazard ratio (95% CI) | p-value | Adjusted hazard ratio# (95% CI) | p-value |
|---|---|---|---|---|---|
| Cardiovascular mortality | |||||
| COPD–OSA | COPD | 0.97 (0.90–1.04) | 0.43 | 0.99 (0.92–1.06) | 0.70 |
| COPD–OSA | OSA | 3.07 (2.82–3.35) | <0.001 | 1.72 (1.58–1.88) | <0.001 |
| OSA | COPD | 0.32 (0.30–0.33) | <0.001 | 0.57 (0.54–0.60) | <0.001 |
| All-cause mortality | |||||
| COPD–OSA | COPD | 0.83 (0.80–0.85) | <0.001 | 1.03 (1.00–1.07) | 0.06 |
| COPD–OSA | OSA | 3.55 (3.41–3.69) | <0.001 | 2.29 (2.20–2.38) | <0.001 |
| OSA | COPD | 0.23 (0.23–0.24) | <0.001 | 0.45 (0.44–0.46) | <0.001 |
OSA: obstructive sleep apnoea. #: Adjusted for age, sex, smoking history, heart failure, myocardial infarction, unstable angina, stroke, diabetes, hypertension, obesity and hypercholesterolaemia.
FIGURE 2.
Kaplan–Meier survival analysis for cardiovascular mortality among people with COPD, obstructive sleep apnoea (OSA) and COPD–OSA overlap.
All-cause mortality
50% of people with COPD died during the study period. People with COPD–OSA overlap had a significantly increased all-cause mortality compared with OSA alone (adjusted HR 2.29 (2.20–2.38); p<0.001). No significant differences were found comparing people with COPD–OSA overlap and people with COPD alone (table 2). Kaplan–Meir survival analysis for all-cause mortality can be seen in figure 3. Patients with COPD, OSA and COPD–OSA overlap had a higher all-cause mortality when compared with their matched controls, respectively. Kaplan–Meir survival analysis for all-cause mortality can be seen in figure 3. Adjusted and unadjusted HRs are in supplementary table S3. The assumption of proportional hazards was consistent and are in supplementary figure S3.
FIGURE 3.
Kaplan–Meier survival analysis for all-cause mortality among people with COPD, obstructive sleep apnoea (OSA) and COPD–OSA overlap.
Cardiovascular burden
People with COPD–OSA overlap had a 27% increased hazard of developing ACS compared with people with COPD alone (adjusted HR 1.27 (1.16–1.40); p<0.001), and a 58% increased hazard of developing ACS compared with people with OSA alone (adjusted HR 1.58 (1.42–1.76); p<0.001). People with COPD–OSA overlap also had a 69% increased hazard of developing heart failure compared with COPD alone (adjusted HR 1.69 (1.60–1.79); p<0.001), and 101% increased hazard compared with OSA alone (adjusted HR 2.01 (1.89–2.15); p<0.001). People with COPD–OSA overlap had a 17% increased hazard of developing a stroke compared with people with OSA alone (adjusted HR 1.17 (1.07–1.30); p<0.005). No significant differences were found in incident stroke when comparing COPD–OSA overlap with COPD alone. These findings are illustrated in table 3. HRs for ACS, stroke and heart failure were all significantly higher in COPD, OSA and COPD–OSA overlap compared with their respective controls. These findings are illustrated in supplementary tables S4–S6.
TABLE 3.
Hazard ratios for cardiovascular burden in COPD, OSA and COPD–OSA overlap
| Group | Reference | Unadjusted hazard ratio (95% CI) | p-value | Adjusted hazard ratio (95% CI) | p-value |
|---|---|---|---|---|---|
| Acute coronary syndrome# | |||||
| COPD–OSA | COPD | 1.55 (1.41–1.70) | <0.001 | 1.27 (1.16–1.40) | <0.001 |
| COPD–OSA | OSA | 2.57 (2.31–2.86) | <0.001 | 1.58 (1.42–1.76) | <0.001 |
| OSA | COPD | 0.60 (0.57–0.64) | <0.001 | 0.81 (0.76–0.86) | <0.001 |
| Heart failure¶ | |||||
| COPD–OSA | COPD | 1.80 (1.70–1.90) | <0.001 | 1.69 (1.60–1.79) | <0.001 |
| COPD–OSA | OSA | 3.47 (3.26–3.71) | <0.001 | 2.01 (1.89–2.15) | <0.001 |
| OSA | COPD | 0.52 (0.50–0.54) | <0.001 | 0.84 (0.81–0.88) | <0.001 |
| Stroke+ | |||||
| COPD–OSA | COPD | 1.00 (0.92–1.10) | 0.93 | 1.05 (0.96–1.15) | 0.27 |
| COPD–OSA | OSA | 1.83 (1.66–2.02) | <0.001 | 1.17 (1.07–1.30) | 0.001 |
| OSA | COPD | 0.55 (0.52–0.57) | <0.001 | 0.89 (0.85–0.94) | <0.001 |
OSA: obstructive sleep apnoea. Prevalent cases for each incident analysis are excluded. #: Adjusted for age, sex, smoking history, heart failure, stroke, diabetes, hypertension, obesity and hypercholesterolaemia. ¶: Adjusted for age, sex, smoking history, unstable angina, stroke, diabetes, hypertension, obesity and hypercholesterolaemia (myocardial infarction was omitted from the model due to collinearity). +: Adjusted for age, sex, smoking history, heart failure, myocardial infarction, unstable angina, stroke, diabetes, hypertension, obesity and hypercholesterolaemia.
Sensitivity analysis using competing risk model showed that sub-distribution HRs (sHRs) remained significant when comparing COPD and OSA with COPD–OSA overlap for incident ACS and heart failure. The adjusted sHR comparing OSA with COPD–OSA overlap for incident stroke was no longer significant (sHR 0.98, 0.89–1.10). The results of the sensitivity analysis are shown in supplementary table S7.
Discussion
The findings of this database derived cohort study in England demonstrate that people with COPD–OSA overlap have a significantly higher CVD and all-cause mortality rate compared with OSA patients alone, even after adjustment of anthropometric and cardiovascular confounding variables. This increased mortality was not seen when COPD–OSA overlap patients were compared with patients with COPD alone, suggesting that the increased mortality observed in the COPD–OSA overlap population relative to OSA alone, may be attributable to the presence of COPD rather than an additive effect of OSA, although this needs further exploration. We have also demonstrated that people with COPD–OSA overlap have a significantly higher risk of developing ACS and heart failure compared with people with COPD or OSA alone suggesting an increased cardiovascular burden in the overlap population. These findings are new and are likely to have potential implications in managing patients with COPD–OSA overlap.
Previous studies have reported similar rates of cardiovascular comorbidities in COPD populations, OSA populations and COPD–OSA overlap [5, 8, 24]. In line with previous work, this study demonstrated that patients with OSA alone and COPD alone have an increased cardiovascular mortality rate compared with age and sex-matched controls [25, 26]. Both conditions individually lead to a chronic inflammatory state, intermittent hypoxia and increased oxidative stress, and with COPD to hyperinflation, which are all likely to contribute to CVD mortality [3–5]. Our study showed that 17% of all COPD deaths and 23% of OSA deaths were attributed to cardiovascular causes. This is similar to previously published data and supports the validity of our other findings [25].
To our knowledge only one previous study has investigated cardiovascular mortality in patients with COPD–OSA overlap and they found that patients with COPD–OSA overlap had a higher CVD mortality rate compared with COPD alone (adjusted HR 2.81, 1.40–5.64) [27]. This differs from our work. However, their sample size was significantly smaller (n=624). No study has previously compared COPD–OSA overlap with an OSA population. Our study found no additional effect of OSA on the COPD–OSA population with regards to cardiovascular or all-cause mortality. This is important as it differs from the currently accepted statement that mortality in COPD–OSA overlap is higher than COPD alone.
There could be several possible reasons for this that need to be taken into consideration. First, no reliable information on continuous positive airway pressure (CPAP) therapy use and adherence was available from the GP (primary care) record database meaning treatment effects could not be investigated. However, several large, randomised trials have not demonstrated a survival impact with CPAP use [28]. Second, while a previous meta-analysis of 16 studies (n=24 308) demonstrated severe OSA was associated with increased cardiovascular mortality, an association between mild or moderate OSA was less clear [24]. We were unable to identify the severities of obstructive sleep apnoea among our population, and it is therefore possible that patients with severe OSA with COPD overlap may have an increased or additive risk compared with COPD alone. Third, there is growing evidence that other measures, such as hypoxic burden give better insight into individual cardiovascular risk compared with the apnoea–hypopnoea index (AHI) [29]. It is possible that many people with COPD in the cohort had undiagnosed OSA, although this would tend to bias against finding a difference in outcomes. We cannot exclude that some of the controls and those with OSA only may also have had undiagnosed COPD. Fourth, the data had an inherent immortal bias, the exposure groups were fixed, thus a patient may have had COPD for several years prior to the overlap diagnosis, and therefore an additional risk was not seen. Therefore, it is possible that an increased risk was not found due to the limitations of the study cohort.
People with COPD–OSA overlap had a higher cardiovascular burden compared with people with COPD or OSA alone with a largest effect size seen in the incidence of heart failure. This is important, as it is likely that a greater vigilance is needed regarding cardiovascular comorbidity in COPD–OSA overlap. It is possible that earlier and more aggressive treatment of cardiovascular risk factors in COPD–OSA overlap may reduce future cardiovascular burden. While in an asymptomatic patient with COPD, the addition of OSA and management with CPAP therapy may not alter cardiovascular or all-cause mortality, it may decrease the incidence of ACS and/or heart failure and therefore healthcare use and burden. There is also good evidence from retrospective data that it may prevent future COPD exacerbations and hospitalisations, both of which will add to a patients’ CVD burden [30]. Furthermore, patients with COPD–OSA overlap are recognised has having an increased cardiovascular vulnerability at the time of diagnosis with a higher burden of subclinical myocardial injury resulting in a higher risk.
This work in part provides an answer to one of the key research priorities from the American Thoracic Society with regard to COPD–OSA overlap syndrome. A large sample size and an English population-based COPD, OSA and COPD–OSA overlap cohort identified using previously validated code lists are key strengths of this study. To our knowledge, this is the largest study to date investigating CVD outcomes in these cohorts. There are some limitations to this work that need to be considered. First, we have not captured disease severity based on the degree of airflow obstruction or AHI. However, given the large population, we are likely to have captured the whole spectrum of disease from mild to severe, meaning the work has generalised applicability. Second, we did not capture treatment effects, specifically with CPAP therapy within the OSA population. This is important as it is becoming clearer that CPAP usage over 4 h is likely to mitigate CVD risk. Third, the populations were based on coding by general practitioners in patient records. This may vary by practice and undoubtedly result in some level of misclassification. However, despite these limitations, this large dataset provides useful insights to CV risk in these populations. Fourth the entry into the study for the COPD–OSA overlap population was at the point of the second diagnosis, this may have introduced a risk of immortal time bias. Fifth, there were substantial baseline differences between groups in cardiovascular comorbidities and risk factors, which may lead to some bias; however, these were taken into account in the multivariable Cox proportional hazards model. Sixth COPD exacerbations or hospitalisations were not recorded, and these could have had an independent effect on CVD mortality and morbidity. Finally, interim/follow-up assessments between baseline and the end of the study were not performed and therefore unmeasured factors arising during this period may have also influenced the observed outcomes.
In conclusion this study has improved our understanding of the CVD risk profile in patients with COPD–OSA overlap. While the mortality outcomes were not higher in COPD–OSA overlap compared with COPD alone in this study, the morbidity rates were higher. This has consequences regarding relevant outcome measures in future studies and suggests that earlier identification of COPD–OSA overlap with increased vigilance surrounding CVD optimisation may potentially reduce future cardiovascular morbidity.
Acknowledgments
The data are provided by patients and collected by the NHS as part of their care and support. The interpretation and conclusions contained in this study are those of the authors alone. Participants were limited to those with linkage to the Hospital Episode Statistics and Office for National Statistics mortality databases (copyright ©2022, re-used with the permission of The Health and Social Care Information Centre; all rights reserved). Nicola Foster (University College London) helped with some of the statistical analysis for the final paper.
Footnotes
Provenance: Submitted article, peer reviewed
Ethics statement: The study was approved by the UK MHRA Independent Scientific Advisory Committee (RDG 23_002767), under Section 251 (NHS Social Care Act 2006).
Author contributions: A.J. Shah, S. Mandal and J.R. Hurst developed the protocol, aims and objectives and research questions. The protocol was reviewed by M. Qummer Ul Arfeen, S. Denaxas and N. Fitzpatrick prior to submission and approval by the Clinical Practice Research Datalink. The results were analysed by A.J. Shah and S. Mandal. The initial draft manuscript was prepared by A.J. Shah. All authors subsequently reviewed, amended and contributed to the final manuscript.
Conflict of interest: A.J. Shah reports support for the present study from the Royal College of Physicians and support for attending meetings from Dolby Vivisol. M. Qummer Ul Arfeen, S. Denaxas, N. Fitzpatrick and S. Mandal report no conflicts of interest. P. Murphy reports grant from Fisher & Paykel, Resmed, Breas Medical and Philips Respironics; consultancy fees from Resmed; payment or honoraria for lectures, presentations, manuscript writing or educational events from Philips Respironics, Resmed, Fisher & Paykel, Chiesi, Genzyme, Breas Medical and Lowenstein; support for attending meetings from Breas Medical, Resmed, Philips and Lowenstein; and receipt of equipment, materials, drugs, medical writing, gifts or other services from Philips Respironics, Resmed, Fisher & Paykel and Breas Medical. J.R. Hurst reports grants from AstraZeneca and Life Detection Technologies; consultancy fees from AstraZeneca, Boehringer Ingelheim, Chiesi, GSK, Sanofi-Regeneron and Takeda; payment or honoraria for lectures, presentations, manuscript writing or educational events from AstraZeneca, Boehringer Ingelheim, Chiesi, GSK, Sanofi-Regeneron, Sanofi and Takeda; and support for attending meetings from AstraZeneca.
Support statement: This study was supported by a Royal College of Physicians Research Fellowship Award. Funding information for this article has been deposited with the Open Funder Registry.
Supplementary material
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Supplementary material
00419-2026.SUPPLEMENT
References
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