Abstract
Study Objectives:
To investigate the association between moderate or severe obstructive sleep apnea treated with home continuous positive airway pressure (CPAP) and severe coronavirus disease 2019 (COVID-19).
Methods:
Retrospective study of patients admitted for COVID-19. Patients with obstructive sleep apnea treated with home CPAP were identified and for each of them we selected 5 patients admitted consecutively in the following hours. The main outcome of the study was the development of severe COVID-19, defined as 1) death or 2) a composite outcome of death or the presence of severe hypoxemic respiratory failure at or during admission. The association between CPAP-treated obstructive sleep apnea and these outcomes was estimated by logistic regression analysis after applying inverse probability of treatment weighting using a propensity score–weighting approach.
Results:
Of the 2,059 patients admitted, 81 (3.9%) were receiving treatment with home CPAP. Among the 486 patients included in the study, 19% died and 39% presented the composite outcome. The logistic regression analysis did not show an association of CPAP treatment either with death (odds ratio [OR]: 0.684; 95% confidence interval [CI]: 0.332–1.409; P = .303) or with the composite outcome (OR: 0.779; 95% CI: 0.418–1.452; P = .432). Death was associated with age (OR: 1.116; 95% CI: 1.08–1.152; P < .001) and number of comorbidities (OR: 1.318; 95% CI: 1.065–1.631; P = .012), and the composite outcome was associated with male sex (OR: 2.067; 95% CI: 1.19–3.589; P = .01) and number of comorbidities (OR: 1.241; 95% CI: 1.039–1.484; P = .018).
Conclusions:
In hospitalized patients with COVID-19, prior obstructive sleep apnea treated with home CPAP is not independently associated with worse outcomes.
Citation:
Sampol J, Sáez M, Martí S, et al. Impact of home CPAP-treated obstructive sleep apnea on COVID-19 outcomes in hospitalized patients. J Clin Sleep Med. 2022;18(7):1857–1864.
Keywords: COVID-19, obstructive sleep apnea, CPAP
BRIEF SUMMARY
Current Knowledge/Study Rationale: A potential role for obstructive sleep apnea on worse coronavirus disease 2019 (COVID-19) outcomes has been proposed; however, the available results are conflicting, and we have scarce data evaluating the impact of COVID-19 in patients with moderate or severe obstructive sleep apnea treated with home continuous positive airway pressure. Focused on this group of patients, this study evaluates whether the presence of continuous positive airway (CPAP)–treated obstructive sleep apnea is associated with a worse evolution if patients are admitted for COVID-19.
Study Impact: In hospitalized patients for COVID-19, the presence of moderate or severe obstructive sleep apnea treated with home CPAP was not independently associated with worse outcomes. Although more studies are needed to evaluate the potential link between obstructive sleep apnea and COVID-19, our results support its continued use during the pandemic.
INTRODUCTION
The current coronavirus disease 2019 (COVID-19) pandemic associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has affected millions of people around the world. The clinical spectrum of the disease ranges from asymptomatic infection to critical illness characterized primarily by acute respiratory distress syndrome. Studies of the impact of patients’ characteristics and comorbidities have identified several risk factors for the development of severe COVID-19, including older age, male sex, obesity, and comorbidity burden.1–4
Obstructive sleep apnea (OSA) is a highly prevalent disease.5 The disruption of sleep and intermittent hypoxia associated with OSA cause a proinflammatory state and a dysregulation of the renin-angiotensin aldosterone axis, which may be conducive to the development of severe COVID-19.6,7 Some authors have associated OSA with the need for hospital admission, respiratory failure, or death in COVID-19 patients,4,8–11 but others have not found that the presence of OSA determines a worse prognosis in this setting.12,13 Two problems interfere with the interpretation of available data. First, OSA and severe COVID-19 share characteristics and risk factors14 favoring the presence of a confounding bias in statistical adjustments. Furthermore, we know that the vast majority of patients with OSA are undiagnosed5 and patients without OSA in previous studies may actually have undetected OSA. Second, these studies did not detail the severity of OSA or include patients with mild OSA, and their treatment was not evaluated. Home continuous positive airway pressure (CPAP), the most frequent treatment for patients with moderate or severe OSA, is known to reverse the pathophysiological alterations caused by obstructive episodes of the upper airway and may therefore exert a protective effect by blocking the mechanistic pathways suggested to link OSA with COVID-19. In this connection, a trend toward a better prognosis was identified in patients with OSA treated with CPAP compared to those not treated.15 In contrast, in a multicenter study of diabetic patients admitted for COVID-19, treated OSA emerged as the comorbidity with the highest risk associated with early death after admission,16 although the authors did not specify the type of OSA treatment. To our knowledge, the use of CPAP has not been associated with a greater severity of respiratory viral diseases. On the contrary, it has been suggested that CPAP-treated patients with OSA have lower rates of hospitalization from influenza than nontreated patients.17
We present a retrospective analysis of patients with COVID-19 admitted to a large university hospital in Barcelona. Based on the previous considerations, our study focused on patients with moderate or severe OSA treated with CPAP, and we used propensity-score methods to reduce the effects of confounding. Our aim was to determine if moderate or severe OSA treated with home CPAP is independently associated with severe COVID-19.
METHODS
The study was conducted at Vall d’Hebron University Hospital, the largest tertiary care hospital in Catalonia and the designated center for COVID-19 patients in the northwest urban area of Barcelona during the study period. The study was approved by the hospital’s institutional review board (PR[AG]478/2020). The need for informed consent was waived owing to the purely observational nature of the study and the use of retrospective data collected for routine clinical practice. The article follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cohort studies.
The study period covered the first 2 months of the outbreak in the city. All consecutive patients with OSA treated with CPAP from February 27 to April 23, 2020, requiring hospital admission and presenting confirmed SARS-CoV-2 were included. For each patient with OSA treated with CPAP, we selected the next 5 non–CPAP-treated patients consecutively admitted for COVID-19 in the following hours. There were no exclusion criteria. Patients were admitted through the emergency department, and SARS-CoV-2 infection was confirmed by a positive polymerase chain reaction test of a nasopharyngeal sample. Patients were identified through the Hospital’s Electronic Health Records, which also includes the comorbidities recorded and medications prescribed in each contact the patient had with the Catalan Public Health System.
In our health system, OSA treatment with CPAP is provided free of charge after prescription by a sleep unit according to national guidelines.18 All prescriptions are included in an electronic database, and adherence to treatment is periodically assessed by hour meter readings of the CPAP device. Prescriptions are renewed periodically in patients with an adherence to CPAP > 3 h/night; if this is not the case, CPAP is withdrawn. Given that the database of CPAP users and the Hospital Health Records include their Social Security numbers, patients can be identified on admission to hospital. Patient characteristics, comorbidities, first recorded inpatient laboratory tests, and clinical outcomes were manually collected. Individual comorbidities were recorded, as were body mass index (calculated as weight in kilograms divided by height in meters squared). Outcomes were no longer recorded after discharge. Patients’ CPAP use was assessed by means of the last hour meter readings done during the 6 months prior to admission.
The main outcome of the study was the development of severe COVID-19, characterized as: 1) death during admission or 2) a composite outcome of death or the presence of severe hypoxemic respiratory failure, defined as the need for mechanical ventilation (invasive or noninvasive) or high-level supplemental oxygen (via high-flow nasal cannula or nonrebreathing face mask at a flow rate of 15 L/min or greater) at the time of or during hospitalization. Patients requiring noninvasive respiratory support were mainly admitted to high-dependency respiratory units, while those requiring invasive mechanical ventilation were admitted to intensive care units. CPAP treatment with patients’ home devices was suspended throughout the admission due to its potential role as an aerosol generator. During this early phase of the pandemic, the hospital treatment protocol consisted of lopinavir/ritonavir 400/100 mg BID for 7–14 days plus hydroxychloroquine 400 mg/12 h on the first day, followed by 200 mg/12 h for 4 days and heparin at prophylactic doses. In severe cases, immunomodulatory treatment with tocilizumab or anakinra and remdesivir was added.
Statistical analysis
Continuous variables are presented as means (standard deviation) and medians with interquartile range, and the categorical variables are presented as counts and percentages. The Mann-Whitney U test was used for continuous variables, and the Pearson χ2 or Fisher exact tests for categorical variables to compare groups. Logistic regression was used to assess the characteristics associated with the development of severe COVID-19. Covariates were selected prior to analysis and consisted of variables associated with severe COVID in previous research: age, sex, obesity, and number of comorbidities.1–4 Since the variables reported to be associated with the development of severe COVID-19 are also associated with the use of CPAP in our population,19 we used propensity-score methods to reduce the effects of confounding. In 24 patients, information on the presence of obesity was lacking and no imputations were made. The propensity scores of being treated with CPAP were estimated using multivariate logistic regression for age, sex, obesity, and number of comorbidities. These propensity scores were used to calculate the inverse probability of treatment weighting (IPTW). The balance of covariates between the groups before and after IPTW was checked using the absolute value of standardized difference between the groups.20 In the first logistic regression model, the effect of treatment with CPAP not adjusted for the covariates was calculated. In the second model, the odds ratios of severe COVID-19 associated with the use of CPAP and the presence of the different covariates were estimated using a multivariate logistic regression model adjusted to the IPTW values.21 The threshold for statistical significance was set at 0.05. Sample size was not calculated, and it was determined by the time window of the study and our decision to include 5 untreated patients for each patient treated with home CPAP to increase the representativeness of the sample. All statistical tests were 2-sided and performed using R software, version 3.6.1 (R Project for Statistical Computing, Vienna, Austria).
RESULTS
During the study period, 2,059 patients diagnosed with COVID-19 were admitted. Among them, 81 (3.9%) were patients with OSA treated with CPAP. Median time on CPAP was 49.7 months (interquartile range: 23.1–76.2) and median use of CPAP, checked 3.4 (1.6) months before admission, was 5.9 h/night (interquartile range: 4.4–7.1). As we included 5 patients not treated with CPAP for each of them, the final sample size was 486 patients. Table 1 shows patients’ characteristics. Patients with home CPAP were older, predominantly male, more frequently obese, and presented a higher number of comorbidities. In the non-CPAP group, a previous diagnosis of OSA was found in 5 patients, 2 mild, and 3 moderate or severe.
Table 1.
Patient characteristics.
| CPAP-Treated Patients (n = 81) | Non–CPAP-Treated Patients (n = 405) | P | |
|---|---|---|---|
| Sex (F/M) | 24 (29.6%)/57 (70.4%) | 166 (41%)/239 (59%) | .026 |
| Age (years) | 69.4 (12.6) | 61.2 (16.5) | < .001 |
| BMI (kg/m2) | 32.6 (4.9) | 29.3 (4.8) | < .001 |
| Obesity* (BMI ≥ 30 kg/m2) | 55 (70.5%) | 168 (43.8%) | < .001 |
| Cognitive decline | 9 (11.1%) | 30 (7.4%) | .39 |
| Cancer† | 8 (9.9%) | 17 (4.2%) | .105 |
| Arterial hypertension | 55 (67.9%) | 171 (42.2%) | < .001 |
| Ischemic heart disease | 20 (24.7%) | 35 (8.6%) | < .001 |
| Heart failure | 12 (14.8%) | 28 (6.9%) | .025 |
| Arrhythmia | 13 (16%) | 30 (7.4%) | .043 |
| Asthma | 4 (4.9%) | 21 (5.2%) | .018 |
| COPD | 13 (16%) | 30 (7.4%) | .036 |
| Diabetes | 26 (32.1%) | 78 (19.3%) | < .001 |
| Kidney failure | 18 (22.2%) | 45 (11.1%) | < .001 |
| Liver disease | 3 (3.7%) | 13 (3.2%) | .13 |
| Cerebrovascular accident | 12 (14.8%) | 39 (9.6%) | < .001 |
| Immunosuppression | 2 (2.5%) | 3 (0.7%) | .088 |
| Comorbidities§ | 2.5 (2) | 1.3 (1.6) | < .001 |
*Data available for 462 patients. †Active solid tumor or blood cancer. §Number of comorbidities; comorbidities included: asthma, COPD, hypertension, diabetes, heart failure, arrhythmia, ischemic heart disease, kidney failure, active cancer, cerebrovascular disease, liver disease, immunosuppression, and cognitive decline. BMI = body mass index, COPD = chronic obstructive pulmonary disease, CPAP = continuous positive airway pressure, F = female, M = male.
Table 2 shows the characteristics of the presentation of COVID-19 in the 2 groups. No differences were detected between patients using CPAP and the control group in their first evaluation in the emergency department, except for a lower frequency of fever. Most patients were hypoxic and had elevated levels of ferritin, lactate dehydrogenase, interleukin 6, C-reactive protein, and d-dimer. There were no differences in any of these parameters, except for a greater elevation in the C-reactive protein value in the CPAP-treated group.
Table 2.
Presentation characteristics of patients admitted for COVID-19.
| OSA-CPAP | Non–CPAP-Treated Patients | P | |
|---|---|---|---|
| Temperature > 38° | 58 (71.6%) | 333 (82.2%) | .032 |
| SaO2/FiO2 ratio | 365.5 (118.8) | 384.5 (106.8) | .586 |
| Leukocytes (×103 cells per µL) | 7.0 (2.6) | 7.7 (4.2) | .43 |
| Neutrophils (%) | 74.0 (9.8) | 76.1 (10.6) | .056 |
| Lymphocytes (%) | 19.2 (8.3) | 18.2 (18.6) | .01 |
| AST (U/L) | 43.0 (20.1) | 49.5 (30.4) | .123 |
| ALT (U/L) | 34.4 (17.5) | 42.2 (33.0) | .387 |
| Creatinine (mg/dL) | 1.1 (0.9) | 1.0 (0.8) | .144 |
| Ferritin (ng/mL) | 740 (445–1055.6) | 568 (320–1220) | .224 |
| (n = 62) | (n = 321) | ||
| LDH (UI/L) | 372 (279.5–489) | 340 (277–462.5) | .653 |
| (n = 59) | (n = 299) | ||
| IL-6 (pg/mL) | 62 (31.9–102) | 57.6 (29.2–101.6) | .741 |
| (n = 65) | (n = 335) | ||
| C-reactive protein (mg/dL) | 12.6 (7.7–23.8) | 10.1 (4.4–18.7) | .035 |
| (n = 67) | (n = 340) | ||
| d-Dimer (ng/mL) | 377.5 (216–639.8) | 279 (183–552) | .053 |
| (n = 66) | (n = 323) |
Data are n (%), mean (standard deviation), or median (interquartile range); number of patients (n) is specified if fewer patients were assessed than the total number of patients. ALT = alanine aminotransferase, AST = aspartate aminotransferase, COVID-19 = coronavirus disease 2019, CPAP = continuous positive airway pressure, FiO2 = fraction of inspired oxygen, IL-6 = interleukin 6, LDH = lactate dehydrogenase, OSA = obstructive sleep apnea, SaO2 = arterial oxygen saturation.
Ninety patients (19%) died during admission: 18 (22.2%) in the OSA-CPAP group and 72 (17.8%) in the control group (P = .347). Table 3 compares their characteristics with those of the survivors. Deceased patients were older, 78.1 (9.4) years vs 59 (15.3) years (P < .001) and had a higher number of comorbidities, 2.9 (1.8) comorbidities per person vs 1.2 (1.5), P < .001, with significant differences between the groups in the frequency of cardiovascular disease (hypertension, ischemic heart disease, heart failure, or arrhythmia), chronic obstructive pulmonary disease, chronic kidney disease, diabetes, cerebrovascular disease, kidney failure, cancer, and cognitive decline. In the CPAP-treated group, adherence to treatment was similar between patients who died and survivors: 5.4 (1.02) vs 5.8 (1.4) h/night, respectively (P = 0.285).
Table 3.
Clinical characteristics of the patients who died/survived.
| Survived (n = 396) | Died (n = 90) | P | |
|---|---|---|---|
| Sex (female) | 158 (39.9%) | 32 (35.6%) | .52 |
| Age (years) | 59 (15.3) | 78.1 (9.4) | < .001 |
| Obesity (BMI ≥ 30 kg/m2)* | 173 (46.4%) | 50 (56.2%) | .123 |
| Cognitive decline | 19 (4.8%) | 20 (22.2%) | < .001 |
| Cancer† | 15 (3.8%) | 10 (11.1%) | .014 |
| Arterial hypertension | 160 (40.4%) | 66 (73.3%) | < .001 |
| Ischemic heart disease | 34 (8.6%) | 21 (23.3%) | < .001 |
| heart failure | 25 (6.3%) | 15 (16.7%) | .003 |
| Arrhythmia | 25 (6.3%) | 18 (20%) | < .001 |
| Asthma | 20 (5.1%) | 5 (5.6%) | .794 |
| COPD | 26 (6.6%) | 17 (18.9%) | < .001 |
| Diabetes | 73 (18.4%) | 31 (34.4%) | .001 |
| Kidney failure | 39 (9.8%) | 24 (26.7%) | < .001 |
| Liver disease | 13 (3.3%) | 3 (3.3%) | 1 |
| Cerebrovascular accident | 30 (7.6%) | 21 (23.3%) | < .001 |
| Immunosuppression | 4 (1%) | 1 (1.1%) | 1 |
| Comorbidities§ | 1.2 (1.5) | 2.9 (1.8) | < .001 |
Data are n (%) or mean (standard deviation). *Data available for 462 patients. †Active solid tumor or blood cancer. §Number of comorbidities; comorbidities included: asthma, COPD, hypertension, diabetes, heart failure, arrhythmia, ischemic heart disease, kidney failure, active cancer, cerebrovascular disease, liver disease, immunosuppression, and cognitive decline. BMI = body mass index, COPD = chronic obstructive pulmonary disease.
As for the composite outcome, 190 patients (39%) presented death or severe respiratory failure: 33 (41%) in the OSA-CPAP group and 157 (39%) in the control group (P = .739). Their characteristics are detailed in Table 4. Patients with this outcome were older and predominantly male. They also had a greater number of comorbidities, with higher frequencies of hypertension, chronic obstructive pulmonary disease, chronic kidney disease, diabetes, cerebrovascular disease, and cognitive decline. As in the case of death, this outcome was observed more frequently in patients with obesity, although the difference was not statistically significant. In the OSA-CPAP group, no differences were found in the daily use of CPAP between the patients who did or did not present the composite outcome: 5.6 (1.1) vs 5.8 (1.5) respectively (P = .630).
Table 4.
Characteristics of patients with the composite outcome death or severe respiratory failure.
| Death or Severe Respiratory Failure | No (n = 296) | Yes (n = 190) | P |
|---|---|---|---|
| Sex (female) | 134 (45.3%) | 56 (29%) | < .001 |
| Age (years) (SD) | 59.6 (16.2) | 67.1 (15.1) | < .001 |
| Obesity (BMI ≥ 30 kg/m2)* | 122 (44.5%) | 101 (53.7%) | .064 |
| Cognitive decline | 18 (6.1%) | 21 (11.1%) | .072 |
| Cancer† | 14 (4.7%) | 11 (5.8%) | .76 |
| Hypertension | 115 (38.9%) | 111 (58.4%) | < .001 |
| Ischemic heart disease | 27 (9.1%) | 28 (14.7%) | .078 |
| Heart failure | 19 (6.4%) | 21 (11.1%) | .1 |
| Arrhythmia | 22 (7.4%) | 21 (11.1%) | .227 |
| Asthma | 15 (5.1%) | 10 (5.3%) | 1 |
| COPD | 19 (6.4%) | 24 (12.6%) | .029 |
| Diabetes | 51 (17.2%) | 53 (27.9%) | .007 |
| Kidney failure | 26 (8.8%) | 37 (19.5%) | .001 |
| Liver disease | 8 (2.7%) | 8 (4.2%) | .517 |
| Cerebrovascular accident | 19 (6.4%) | 32 (16.8%) | < .001 |
| Immunosuppression | 2 (0.7%) | 3 (1.6%) | .384 |
| Comorbidities§ | 1.2 (1.5) | 2 (1.8) | < .001 |
Data are n (%) or mean (SD). *Data available for 462 patients. †Active solid tumor or blood cancer. §Number of comorbidities; comorbidities included: asthma, COPD, hypertension, diabetes, heart failure, arrhythmia, ischemic heart disease, kidney failure, active cancer, cerebrovascular disease, liver disease, immunosuppression, and cognitive decline. BMI = body mass index, COPD = chronic obstructive pulmonary disease. SD = standard deviation.
To assess the effect of CPAP treatment on the 2 outcomes of the study controlling for the confounding effect of the baseline characteristics, a propensity score analysis was performed. Propensity scores associated with CPAP treatment were determined in a logistic regression model with age, sex, comorbidities, and obesity as covariates. From the total of 486 patients, data from the 462 patients for whom complete information was available for all covariates were used. From this model, the inverse probability of treatment weights (IPTW) was estimated. The effect of treatment with CPAP was estimated using logistic regression (Table 5). In the first model, the effect of CPAP treatment was not adjusted for the covariates. In the second, the effect of this treatment was adjusted for age, sex, obesity, and comorbidity using the IPTW values.
Table 5.
Logistic regression analysis.
| Variable | Outcome: Death | Outcome: Death or Respiratory Failure | ||
|---|---|---|---|---|
| OR* | P | OR* | P | |
| OSA-CPAP* | 1.208 (0.650–2.151) | .535 | 1.017 (0.616–1.662) | .948 |
| OSA-CPAP† | 0.684 (0.332–1.409) | .303 | 0.779 (0.418–1.452) | .432 |
| Age (years)† | 1.116 (1.080–1.152) | < .001 | 1.019 (0.997–1.041) | .097 |
| Sex (male)† | 1.663 (0.769–3.595) | .197 | 2.067 (1.190–3.589) | .01 |
| Obesity (yes)† | 1.218 (0.614–2.419) | .573 | 1.363 (0.758–2.45) | .301 |
| Comorbidities†§ | 1.318 (1.065–1.631) | .012 | 1.241 (1.039–1.484) | .018 |
*Model 1: without adjustment. †Model 2: adjusted with IPTW according to the propensity scores for age, sex, obesity, and comorbidity. §Number of comorbidities; comorbidities included: asthma, COPD, hypertension, diabetes, heart failure, arrhythmia, ischemic heart disease, kidney failure, active cancer, cerebrovascular disease, liver disease, immunosuppression, and cognitive decline. The analysis included 462 patients in both models. CPAP = continuous positive airway pressure, IPTW = inverse propensity-score treatment weighted, OR = odds ratio calculated with an IPTW logistic regression analysis, OSA = obstructive sleep apnea.
OSA treated with CPAP was not associated with death during admission or with the composite outcome of death or severe respiratory failure. However, these outcomes were associated with a higher number of comorbidities, older age, and male sex. Obesity was not associated with greater severity of COVID-19 among our patients.
DISCUSSION
In this study, patients requiring hospitalization due to COVID-19 frequently presented evolution to severe respiratory failure or death. However, this poor evolution was not independently associated with the presence of home CPAP-treated moderate or severe OSA.
Previous studies of the impact of COVID-19 in patients with OSA have presented conflicting findings. Some point to an increased risk of severe COVID-19,4,8–11 while others do not report differences with respect to patients without OSA.12,13 However, none of these studies assessed whether the patients received home CPAP treatment. In a large series of diabetic patients hospitalized for COVID-19, Cariou et al16 found that treated OSA was a risk factor for death. However, those authors did not specify the type of OSA treatment, and our results do not suggest that CPAP use favors the development of severe COVID-19. In fact, in agreement with Cade et al,15 our findings suggest that CPAP treatment may actually protect against OSA’s putative contribution to the development of severe COVID-19. Unlike Cade et al,15 who selected CPAP patients via natural language processing of the electronic medical record, we selected CPAP users with known adherence to treatment. Furthermore, given the coexistence of multiple common risk factors between OSA and severe COVID-19, we estimated association between OSA treated with CPAP and outcomes by logistic regression analysis using a propensity score-weighting approach.
The proinflammatory effects of OSA have been proposed as a mechanism that favors severe COVID-19, and it is known that CPAP treatment is associated with a fall in serum markers of inflammation.22 Indeed, in patients treated with CPAP, values of some of these markers at the time of hospitalization were similar to those of the control group. Due to its potential role as a source of aerosols, we withdrew CPAP treatment during hospitalization for COVID-19, and so its potential protective effect may have been compromised; however, the effects of CPAP on markers of systemic inflammation associated with a worse prognosis of COVID-19 are known to be partially or totally maintained for at least 2 weeks,23 and we can hypothesize that they endured (at least partially) in our patients.
In our study, the main factors associated with the development of severe COVID-19 were age, male sex, and the presence of a higher burden of comorbidities, as previously described.1–4 Obesity, identified as another risk factor for severe COVID 19 in most studies,24 was more frequent among our patients with severe COVID-19, but we did not find an independent association with this poor disease evolution. However, obesity was present in the majority of OSA patients treated with CPAP and, in the rest of our patients, its prevalence was also more than twice that in our population.25 These findings support that obesity constitutes a risk factor for hospital admission in COVID-19.
Our study is the first to focus on the impact of COVID-19 on patients with OSA who use CPAP. It has the strength that the organization of patient care in our area during the period studied allowed us to assess practically all the patients admitted for COVID-19, and the control system of CPAP treatment in our public health system allowed us to identify all patients using home CPAP who required admission. Furthermore, the treatment guidelines were protocolized in our center and no differences are to be expected between CPAP users and nonusers.
In addition to its retrospective nature and small sample size, several limitations of the study should also be mentioned. First, during the study period, the ability to confirm COVID-19 infection was restricted to the hospital setting, and mild or asymptomatic cases were not detected. As a consequence, we could not determine whether CPAP-treated OSA is a risk factor for being hospitalized from COVID-19. Second, OSA not treated with CPAP is not routinely reported and its prevalence is underestimated in our health system’s electronic records. Thus, we only detected a small number of patients with OSA not treated with CPAP and we were unable to compare the impact of COVID-19 in patients with OSA who were CPAP treated and those who were not. This is a common issue among the available studies. Although the prevalence of previously unrecognized OSA in hospitalized patients is now acknowledged,26 large series evaluating comorbidities in patients admitted for COVID-19 have not included OSA.2,3,27–30 In the only study in which the presence of OSA in patients admitted for COVID-19 was prospectively assessed with a sleep study, Perger et al10 found OSA in 37% of their patients, a much higher prevalence than that reported in retrospective studies like ours. The characteristics of our study population suggest a high prevalence of OSA among our patients not treated with home CPAP, and we cannot rule out the possibility that the presence of untreated OSA could have favored a worse prognosis in some patients of this group. Third, in the prepandemic era, telemedicine was not generalized in our Health System in the assessment of patients with OSA who were CPAP treated, and we do not know the pattern of CPAP use in the days prior to hospital admission. However, our patients were long-term users of CPAP with good adherence. Additionally, they presented an inflammatory marker profile similar patients who were non-CPAP-treated when they arrived at the emergency room, and an increase in the use of CPAP has been reported during the pandemic in patients with OSA.31–33 All of this makes us think that our patients maintained the use of CPAP in the days prior to their admission.
In the first phase of the pandemic, the aerosol-generating potential of CPAP raised considerable concern34 because of the risk that a patient infected with SARS-CoV-2 might transmit the infection to his/her cohabitants. However, scientific societies recommended continuing with the use of CPAP35 and as previously mentioned overall adherence to treatment has increased.31–33 The precise role of aerosol particles in transmitting COVID-19 is controversial and it has been pointed out that aerosol-generating medical procedures produce less aerosol than a patient coughing.36 As far as we know, no increase in COVID-19 cases has been described among the household contacts of CPAP users. However, OSA patients are concerned about how COVID-19 may interfere with their disease.31 The future of the pandemic is uncertain and there is a possibility of annual outbreaks.37 Withdrawal of CPAP treatment has been associated with the rapid reappearance of OSA, drowsiness, and associated cardiovascular risk markers.23,38 Therefore, the finding that CPAP-treated OSA is not associated with a worse prognosis of COVID-19 is clinically relevant and strengthens the support for its use.
In conclusion, our study did not identify a worse prognosis in patients with moderate or severe OSA treated with home CPAP hospitalized for COVID-19. These data thus support its continued use during the pandemic. Well-designed prospective studies are now needed to determine the effect of COVID-19 on patients with OSA, and whether our results are due to a protective effect of CPAP.
DISCLOSURE STATEMENT
All authors have seen and approved this manuscript. Dr. G. Sampol reports a grant from AstraZeneca outside the submitted work, and personal fees from Resmed for participation on an advisory board, outside the submitted work. All other authors report no conflicts of interest.
ACKNOWLEDGMENTS
The authors thank Miriam Mota-Foix and Santi Perez-Hoyos from the Statistics and Bioinformatics Unit (UEB) Vall Hebron Hospital Research Institute (VHIR) for conducting statistical analysis.
Vall d’Hebron COVID-19 Working Group:
Júlia Sampol: Respiratory Department, Hospital Universitari Vall d’Hebron, Barcelona, Spain; Multidisciplinary Sleep Unit, Hospital Universitari Vall d’Hebron, Barcelona, Spain; Department of Medicine, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain; CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain. jsampol@vhebron.net
Marta Miarons: Pharmacy Department, Vall d’Hebron University Hospital, Universitat Autónoma de Barcelona, Barcelona, Spain. mmiarons@vhebron.net
Adrián Sánchez-Montalvá: Infectious Diseases Department, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain. adsanche@vhebron.net
Salvador Augustin: Hepatology Department, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain. saugusti@vhebron.net
Alfredo Guillén: Systemic Diseases Section, Internal Medicine Department, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain. alguille@vhebron.net
Alba Vimes-Fortis: Clinical Pharmacology Service, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain. avimes@vhebron.net
ABBREVIATIONS
- COVID-19
coronavirus disease 2019
- CPAP
continuous positive airway pressure
- IPTW
inverse probability of treatment weighting
- OR
odds ratio
- OSA
obstructive sleep apnea
- SARS-CoV-2
severe acute respiratory syndrome coronavirus 2
Contributor Information
for the Vall d’Hebron COVID-19 Working Group:
Júlia Sampol, Marta Miarons, Adrián Sánchez-Montalvá, Salvador Augustin, Alfredo Guillén, and Alba Vimes-Fortis
REFERENCES
- 1. Richardson S , Hirsch JS , Narasimhan M , et al. ; the Northwell COVID-19 Research Consortium . Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area . JAMA. 2020. ; 323 ( 20 ): 2052 – 2059 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Guan WJ , Liang WH , Zhao Y , et al. ; China Medical Treatment Expert Group for COVID-19 . Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis . Eur Respir J. 2020. ; 55 ( 5 ): 2000547 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Cummings MJ , Baldwin MR , Abrams D , et al . Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study . Lancet. 2020. ; 395 ( 10239 ): 1763 – 1770 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Ioannou GN , Locke E , Green P , et al . Risk factors for hospitalization, mechanical ventilation, or death among 10-131 US veterans with SARS-CoV-2 infection . JAMA Netw Open. 2020. ; 3 ( 9 ): e2022310 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Benjafield AV , Ayas NT , Eastwood PR , et al . Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis . Lancet Respir Med. 2019. ; 7 ( 8 ): 687 – 698 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Meira E Cruz M , Miyazawa M , Gozal D . Putative contributions of circadian clock and sleep in the context of SARS-CoV-2 infection . Eur Respir J. 2020. ; 55 ( 6 ): 2001023 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. McSharry D , Malhotra A . Potential influences of obstructive sleep apnea and obesity on COVID-19 severity . J Clin Sleep Med. 2020. ; 16 ( 9 ): 1645 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Maas MB , Kim M , Malkani RG , Abbott SM , Zee PC . Obstructive sleep apnea and risk of COVID-19 infection, hospitalization and respiratory failure . Sleep Breath. 2021. ; 25 ( 2 ): 1155 – 1157 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Strausz S , Kiiskinen T , Broberg M , et al. ; FinnGen . Sleep apnoea is a risk factor for severe COVID-19 . BMJ Open Respir Res. 2021. ; 8 ( 1 ): 6 – 11 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Perger E , Soranna D , Pengo M , Meriggi P , Lombardi C , Parati G . Sleep-disordered breathing among hospitalized patients with COVID-19 . Am J Respir Crit Care Med. 2021. ; 203 ( 2 ): 239 – 241 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Bhatraju PK , Ghassemieh BJ , Nichols M , et al . Covid-19 in critically ill patients in the Seattle region—case series . N Engl J Med. 2020. ; 382 ( 21 ): 2012 – 2022 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Mashaqi S , Lee-Iannotti J , Rangan P , et al . Obstructive sleep apnea and COVID-19 clinical outcomes during hospitalization: a cohort study . J Clin Sleep Med. 2021. ; 17 ( 11 ): 2197 – 2204 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Goldstein CA , Rizvydeen M , Conroy DA , et al . The prevalence and impact of pre-existing sleep disorder diagnoses and objective sleep parameters in patients hospitalized for COVID-19 . J Clin Sleep Med. 2021. ; 17 ( 5 ): 1039 – 1050 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Miller MA , Cappuccio FP . A systematic review of COVID-19 and obstructive sleep apnoea . Sleep Med Rev. 2021. ; 55 : 101382 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Cade BE , Dashti HS , Hassan SM , Redline S , Karlson EW . Sleep apnea and COVID-19 mortality and hospitalization . Am J Respir Crit Care Med. 2020. ; 202 ( 10 ): 1462 – 1464 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Cariou B , Hadjadj S , Wargny M , et al. ; CORONADO Investigators . Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study . Diabetologia. 2020. ; 63 ( 8 ): 1500 – 1515 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Mok EM , Greenough G , Pollack CC . Untreated obstructive sleep apnea is associated with increased hospitalization from influenza infection . J Clin Sleep Med. 2020. ; 16 ( 12 ): 2003 – 2007 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Mediano O , González Mangado N , Montserrat JM , et al . International consensus document on obstructive sleep apnea . Arch Bronconeumol. 2022. ; 58 ( 1 ): 52 – 68 . [DOI] [PubMed] [Google Scholar]
- 19. Turino C , Bertran S , Gavaldá R , et al . Characterization of the CPAP-treated patient population in Catalonia . PLoS One. 2017. ; 12 ( 9 ): e0185191 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Austin PC , Stuart EA . Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies . Stat Med. 2015. ; 34 ( 28 ): 3661 – 3679 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Austin PC . An introduction to propensity score methods for reducing the effects of confounding in observational studies . Multivariate Behav Res. 2011. ; 46 ( 3 ): 399 – 424 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Baessler A , Nadeem R , Harvey M , et al . Treatment for sleep apnea by continuous positive airway pressure improves levels of inflammatory markers—a meta-analysis . J Inflamm (Lond). 2013. ; 10 ( 1 ): 13 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Kohler M , Stoewhas AC , Ayers L , et al . Effects of continuous positive airway pressure therapy withdrawal in patients with obstructive sleep apnea: a randomized controlled trial . Am J Respir Crit Care Med. 2011. ; 184 ( 10 ): 1192 – 1199 . [DOI] [PubMed] [Google Scholar]
- 24. Popkin BM , Du S , Green WD , et al . Individuals with obesity and COVID-19: A global perspective on the epidemiology and biological relationships . Obes Rev. 2020. ; 21 ( 11 ): e13128 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Vivanco-Hidalgo RM , Vela-Vallespín E , Clèries M , et al . Informe sobre les característiques sociodemogràfiques, clíniques i els factors pronòstics dels pacients amb el diagnòstic de COVID-19 a Catalunya: resum executiu. Barcelona, Spain: Agency for Health Quality and Assessment of Catalonia (AQuAS); 2020. . Available from: https://scientiasalut.gencat.cat/handle/11351/4914 . Accessed October 26, 2021.
- 26. Chan MTV , Wang CY , Seet E , et al. ; Postoperative Vascular Complications in Unrecognized Obstructive Sleep Apnea (POSA) Study Investigators . Association of unrecognized obstructive sleep apnea with postoperative cardiovascular events in patients undergoing major noncardiac surgery . JAMA. 2019. ; 321 ( 18 ): 1788 – 1798 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Grasselli G , Zangrillo A , Zanella A , et al. ; COVID-19 Lombardy ICU Network . Baseline characteristics and outcomes of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy . JAMA. 2020. ; 323 ( 16 ): 1574 – 1581 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Fosbøl EL , Butt JH , Østergaard L , et al . Association of angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use with COVID-19 diagnosis and mortality . JAMA. 2020. ; 324 ( 2 ): 168 – 177 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Kabarriti R , Brodin NP , Maron MI , et al . Association of race and ethnicity with comorbidities and survival among patients with COVID-19 at an urban medical center in New York . JAMA Netw Open. 2020. ; 3 ( 9 ): e2019795 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Geleris J , Sun Y , Platt J , et al . Observational study of hydroxychloroquine in hospitalized patients with Covid-19 . N Engl J Med. 2020. ; 382 ( 25 ): 2411 – 2418 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Thorpy M , Figuera-Losada M , Ahmed I , et al . Management of sleep apnea in New York City during the COVID-19 pandemic . Sleep Med. 2020. ; 74 : 86 – 90 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Attias D , Pepin JL , Pathak A . Impact of COVID-19 lockdown on adherence to continuous positive airway pressure by obstructive sleep apnoea patients . Eur Respir J. 2020. ; 56 ( 1 ): 2001607 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. del Campo F , López G , Arroyo A , et al . Study of adherence to continuous positive airway pressure treatment in patients with obstructive sleep apnea syndrome in the confinement during the COVID-19 pandemic . Arch Bronconeumol. 2020. ; 56 ( 12 ): 818 – 819 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Barker J , Oyefeso O , Koeckerling D , Mudalige NL , Pan D . COVID-19: community CPAP and NIV should be stopped unless medically necessary to support life . Thorax. 2020. ; 75 ( 5 ): 367 [DOI] [PubMed] [Google Scholar]
- 35. Voulgaris A , Ferini-Strambi L , Steiropoulos P . Sleep medicine and COVID-19. Has a new era begun? Sleep Med. 2020. ; 73 : 170 – 176 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Wilson NM , Marks GB , Eckhardt A , et al . The effect of respiratory activity, non-invasive respiratory support and facemasks on aerosol generation and its relevance to COVID-19 . Anaesthesia. 2021. ; 76 ( 11 ): 1465 – 1474 . [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Murray CJL , Piot P . The potential future of the COVID-19 pandemic: will SARS-CoV-2 become a recurrent seasonal infection? JAMA. 2021. ; 325 ( 13 ): 1249 – 1250 . [DOI] [PubMed] [Google Scholar]
- 38. Roeder M , Sievi NA , Kohler M , Schwarz EI . Predictors of changes in subjective daytime sleepiness in response to CPAP therapy withdrawal in OSA: a post-hoc analysis . J Sleep Res. 2021. ; 30 ( 2 ): e13078 . [DOI] [PubMed] [Google Scholar]