
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-24-00547
00030
10.1097/MD.0000000000038838
3
6700
Research Article
Observational Study
Questionnaire and polysomnographic evaluation of obstructive sleep apnea in a cohort of post-COVID-19 patients
Chibante Fernanda Oliveira MD fernanda.chibante@gmail.com
a*
Faria Anamélia Costa MD costafaria@gmail.com
a
Ribeiro-Alves Marcelo PhD mribalves@gmail.com
b
da Costa Claudia Henrique PhD a
Lopes Agnaldo José PhD agnaldolopesuerj@gmail.com
a
Mafort Thiago Thomaz PhD tmafort@gmail.com
a
https://orcid.org/0000-0002-5071-6045
Rufino Rogerio PhD a
a Rio de Janeiro State University, Rio de Janeiro, Brazil
b Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.
* Correspondence: Fernanda Oliveira Chibante, Discipline of Pulmonology, Rio de Janeiro State University, Rio de Janeiro, Brazil (e-mail: fernanda.chibante@gmail.com).
13 9 2024
13 9 2024
103 37 e3883816 1 2024
09 6 2024
14 6 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

To evaluate the efficiency of 5 screening questionnaires for obstructive sleep apnea (OSA), OSA frequency, and the association between OSA and COVID-19 severity in recent COVID-19 cases, and to compare the use of the oxygen desaturation index (ODI) as an alternative measure for the respiratory disturbance index (RDI). This open cohort study recruited patients with recent COVID-19 (within 30–180 days) diagnosed using reverse transcription polymerase chain reaction. Participants were screened for OSA using the following 5 sleep disorder questionnaires prior to undergoing type I polysomnography: the Sleep Apnea Clinical Score (SACS), Epworth Sleepiness Scale (ESS), STOP-Bang score, No-Apnea score, and Berlin questionnaire. Polysomnography revealed that 77.5% of the participants had OSA and that these patients exhibited higher COVID-19-related hospitalization (58%) than those exhibited by non-apneic patients. The Kappa coefficient showed reasonable agreement between RDI > 5/h and No-Apnea score, RDI > 15/h and Berlin questionnaire score, and Epworth Sleepiness Scale and STOP-Bang score, but only moderate agreement between RDI > 15/h and No-Apnea score. An OSA-positive No-Apnea score increased the specificity of the SACS to 100% when RDI > 5/h. The intraclass correlation coefficient showed 95.2% agreement between RDI > 5/h and ODI > 10/h. The sequential application of the No-Apnea score and SACS was the most efficient screening method for OSA, which had a moderately high incidence among the post-COVID-19 group. We demonstrated an association between OSA and COVID-19 related hospitalization and that ODI could be a simple method with good performance for diagnosing OSA in this population.

COVID-19
obstructive sleep apnea
polysomnography
post-COVID-19 syndrome
SARS-CoV-2
sleep disorder questionnaires
FundaÃ§Ã£o Carlos Chagas Filho de Amparo Ã Pesquisa do Estado do Rio de Janeiro 10.13039/501100004586 200.311/2023 Rogerio RufinoFundaÃ§Ã£o Carlos Chagas Filho de Amparo Ã Pesquisa do Estado do Rio de Janeiro 10.13039/501100004586 Claudia Henrique da CostaFundaÃ§Ã£o Carlos Chagas Filho de Amparo Ã Pesquisa do Estado do Rio de Janeiro 10.13039/501100004586 Agnaldo JosÃ© LopesOPEN-ACCESSTRUE
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pmc1. Introduction

Since the onset of the COVID-19 pandemic in December 2019, new genetic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains with different virulence and transmissibility levels have emerged. Acute symptoms have a broad clinical spectrum, ranging from mild infection to acute respiratory failure and death. Post-COVID-19 syndrome is defined as symptom persistence or delayed onset 12 weeks after acute infection.[1] Risk factors for severe COVID-19 and disease persistence include increased age, underlying clinical comorbidities such as cardiovascular disease, vaccination status, SARS-CoV-2 strain, and host genetics.[2,3]

Obstructive sleep apnea (OSA) is a well-defined risk factor for cardiometabolic diseases[4,5]; however, its impact on COVID-19 severity has not yet been confirmed. The prevalence of OSA has shown exponential growth in recent decades, exceeding 50% in some countries in certain age groups.[6] Numerous questionnaires have been developed as screening tools to ensure accurate OSA diagnosis, including the Sleep Apnea Clinical Score (SACS), Epworth Sleepiness Scale (ESS), STOP-Bang score, and No-Apnea score. Each instrument is regularly used in clinical practice and scientific research and has varying sensitivity and specificity for determining OSA risk in different disease, such as COPD, interstitial lung disease, among others.[7,8] Nevertheless, polysomnography is considered the gold standard for diagnosing OSA.

This study aimed to evaluate the efficiency of 5 sleep disorder questionnaires as screening tools for OSA in a group of patients with a recent diagnosis of COVID-19 and determine which one would provide the best results for use in relation to OSA frequency, the association between OSA and COVID-19 severity. Due to the high frequency of patients with post-Covid syndrome, it is important to determine which screening tool would be most advisable for the primary care setting. Furthermore, evaluate the oxygen desaturation index (ODI) accuracy as a substitute measure for the respiratory disturbance index (RDI) in this cohort.

2. Methods

2.1. Study design

This open, prospective, single-center cohort study recruited patients from the post-COVID-19 outpatient clinic of the Rio de Janeiro State University with a recent diagnosis of COVID-19, but not necessarily with confirmed post-COVID-19 syndrome. COVID-19 patients diagnosed by reverse transcription polymerase chain reaction 30 to 180 days prior to recruitment and aged > 18 years were included. The exclusion criteria were pregnancy, a previous diagnosis of OSA, home oxygen therapy usage, or COVID-19 post-SARS-CoV-2 vaccination. The study was approved by the Rio de Janeiro State University Research Ethics Committee (CAAE: 30135320.0.0000.5259).

2.2. Study intervention

The patients underwent type I polysomnography (PSG1) using the Alice 5 diagnostic system (Philips Respironics) between April 2020 and August 2021. The PSG1 recordings were then scored according to the American Academy of Sleep Medicine manual, version 2.6.[8] The patients completed 5 sleep disorder questionnaires on the night of the PSG1: SACS, ESS, Berlin questionnaire, STOP-Bang, and No-Apnea.[9–13] The concordance between the questionnaire results and the PSG1 results (RDI > 5/h and RDI > 15/h) was assessed using Kappa coefficient analysis. The predictive parameters of each questionnaire for RDI > 5/h and RDI > 15/h, OSA positivity on PSG1, OSA severity, the association between OSA and COVID-19 severity, and the degree of agreement between RDI > 5/h and ODI > 10/h using the intraclass correlation coefficient were also evaluated.

2.3. Statistical analysis

The participants were grouped according to their sociodemographic and clinical characteristics, distinct clinical-epidemiological characteristics, and COVID-19 severity. The nonparametric Kruskal–Wallis tests were used for continuous numerical variables, while Chi-squared tests were conducted for comparing the relative frequencies of the different levels of categorical variables. Kappa tests were used to analyze agreement among binary results (i.e., those suggestive or non-suggestive of OSA) based on cutoff values commonly used in clinical practice or obtained from the validated instruments mentioned above. The predictive ability of either single or sequential application of screening instruments was estimated by accuracy, sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and false-positive and negative ratios with 95% confidence intervals (CIs). All statistical analyses were performed using R software version 4.1.2, including the packages “base” for descriptive and model fittings and “epicalc” for agreement/kappa analyses. P-values ≤ .05 were considered statistically significant.

2.4. Study measures

The log-transformed measures suggestive of sleep apnea (e.g., the apnea/hypopnea [AHI], obstructive apnea, central apnea, and hypopnea indices) were compared with other measures extracted from the PSG1 reports (e.g., sleep efficiency, arousal, N1 sleep stage, N2 sleep stage, N3 sleep stage, rapid eye movement sleep stage, minor peripheral oxygen saturation [Spo2], Spo2 < 90%, ODI, and periodic limb movement index) after applying proper validated assessment methods (e.g., Mallampati score, SACS, ESS, Berlin, STOP-Bang, and No-Apnea) and other clinical-epidemiological characteristics. The expected mean marginal values were obtained using fixed effects multiple linear regression (log-linear) models, which included group effects. The main effect was corrected for confounding variables (e.g., sex, age, body mass index, type II diabetes mellitus, systemic arterial hypertension, chronic heart failure, chronic obstructive pulmonary disease, and time since the positive real-time PCR result in months) in the systematic component of the models. Estimated marginal mean values with 95% CIs, computed using the “emmeans” function, comprised all confounders in the multiple linear models as equally weighted marginal mean values. Contrasts were then constructed from these estimated marginal mean values using pairwise P-values adjusted for the number of comparisons in analyses with ≥3 clinical groups using Tukey’s honest significant difference test. For the adjusted models, graphical analyses of the residuals were performed to confirm their randomness.

Finally, the likelihood of receiving suggestive sleep apnea results from PSG1 examination measures was assessed using maximum likelihood odds ratios estimates and corresponding 95% CIs based on unconditional logistic fixed effect model fit. The main effect was corrected for the aforementioned confounding variables in the systematic component of the models.

3. Results

Among the 123 participants interviewed, 53 were excluded due to serological-based COVID-19 diagnosis, 2 due to a previous diagnosis of OSA before COVID-19 onset, 2 due to COVID-19 onset after vaccination, 23 due to unavailability for PSG1 testing, and 1 due to non-COVID-19-related mortality. In total, 42 participants were included in this study with a mean age of 54.9 (±12.1) years, consisting of 28 females and 13 (30.95%) patients with severe COVID-19, defined in this study as the need for intensive care unit (ICU) admission. The most frequent comorbidities were obesity (24/42), hypertension (18/42), and type II diabetes mellitus (13/42), while 6 patients (14.28%) had no comorbid disease.

Data from 2 participants were excluded from analysis owing to poor sleep efficiency during PSG1 (<50%). Overall, OSA was diagnosed in 31 participants (77.5%), classified as mild, moderate, and severe in 13 (32.5%), 10 (25%), and 8 (20%) patients, respectively (Fig. 1).[8]

Figure 1. Flowchart of the study. Observation: flowchart of recruitment and severity classification of COVID and obstructive sleep apnea.

Of the apneic patients, 18 (58%) required hospitalization for COVID-19 treatment; however, no association was found between OSA and ICU admission was fond (Table 1).

Table 1 COVID-19 patient data.

	Population	Severe COVID-19*	P-value	
No (n = 29)	Yes (n = 13)	
Sex (female)	28 (66.7%)	20 (69%)	8 (61.5%)	–	
Age (years)	57.9 (IQR = 16.8)	58.9 (IQR = 16.07)	49.9 (IQR = 13.5)	.151	
Smoking (packs/year)	5 (11.9%)	4 (13.8%)	1 (7.7%)	.032	
RDI (/h)	11.9 (IQR = 16.98)	10.1 (IQR = 10.21)	18.5 (IQR = 24.68)	.368	
ODI (/h)	11.2 (IQR = 17.43)	7.6 (IQR = 14.94)	17.7 (IQR = 19.84)	.384	
N1 (%)	12.2 (IQR = 8.88)	11.7 (IQR = 9.85)	13.3 (IQR = 7.53)	.345	
N2 (%)	53.4 (IQR = 10.02)	53.4 (IQR = 9.87)	53.8 (IQR = 10.15)	.825	
N3 (%)	15.2 (IQR = 14.57)	16.8 (IQR = 13.61)	11.2 (IQR = 11.07)	.330	
REM (%)	15.9 (IQR = 4.6)	15.3 (IQR = 4.4)	16.8 (IQR = 6.13)	.174	
AAI (/h)	15.0 (IQR = 10.07)	14.6 (IQR = 10.12)	17.2 (IQR = 9.83)	.316	
PLM (/h)	1.9 (IQR = 10.01)	1.9 (IQR = 10.01)	2.6 (IQR = 7.73)	.611	
Asthma	5 (11.9%)	3 (10.3%)	2 (15.4%)	1	
COPD	5 (11.9%)	4 (13.8%)	1 (7.7%)	.961	
DM2	13 (31%)	11 (37.9%)	2 (15.4%)	.271	
SAH	19 (45.2%)	12 (41.4%)	7 (53.8%)	.678	
Obesity	25 (59.5%)	16 (55.2%)	9 (69.2%)	.604	
Hospitalization
>15 days	23 (54.8%)	10 (34.5%)	13 (100%)	<.001	
O2 (low flow)	23 (54.8%)	10 (34.5%)	13 (100%)	<.001	
ICU	13 (31%)	0 (0%)	13 (100%)	<.001	
NIV	12 (28.6%)	2 (6.9%)	10 (76.9%)	<.001	
IV	4 (9.5%)	0 (0%)	4 (30.8%)	.01	
AAI = arousal-awakening index, BMI = body mass index, COPD = chronic obstructive pulmonary disease, DM2 = type II diabetes mellitus, ICU = intensive care unit, IV = invasive ventilation, N1 = sleep stage 1, N2 = sleep stage 2, N3 = sleep stage 3, NIV = noninvasive ventilation, O2 = oxygen, ODI = oxygen desaturation index, PLM = periodic limb movement index, RDI = respiratory disturbance index, REM = rapid eye movement, SAH = systemic arterial hypertension.

* Required ICU admission.

The Kappa coefficient showed reasonable agreement between RDI > 5/h and the No-Apnea score, RDI > 15/h and the Berlin questionnaire, and ESS and the STOP-Bang score. RDI > 15/h exhibited moderate agreement the No-Apnea score (Table 2). The degree of agreement between the RDI > 5/h and the ODI > 10/h was exceedingly high, with an intraclass correlation coefficient of 95.2% (CI 95%: 90.9–97.4).

Table 2 Sleep disorder screening questionnaire score agreement with respiratory disturbance index.

Score	RDI > 5/h	RDI > 15/h	
Agreement (%)	Kappa	P	Agreement (%)	Kappa	P	
Berlin	65.0	0.133	.192	62.5	0.275	.027	
ESS	40.0	‐0.006	.524	62.5	0.223	.072	
No-Apnea	67.5	0.270	.032	75.0	0.510	<.001	
SACS	35.0	0.042	.283	62.5	0.198	.061	
STOP-Bang	65.0	0.133	.192	62.5	0.275	.027	
ESS = Epworth Sleepiness Scale, ODI = oxygen desaturation index, RDI = respiratory disturbance index, SACS = Sleep Apnea Clinical Score.

Upon analyzing the predictive values of the 5 sleep disorder questionnaires for RDI > 5/h and RDI > 15/h, the Berlin questionnaire, STOP-Bang score, and No-Apnea score showed high sensitivity and high NPV for RDI > 15/h, while the SACS had the highest specificity and showed high PPVs (Table 3). Moreover, models with sequential administration of 2 questionnaires/scores were found to increase diagnostic screening accuracy. Notably, the sequential No-Apnea+/SACS model increased the specificity of SACS to 100% in patients with RDI > 5h (Table 4 and Fig. 2).

Table 3 Predictive values of OSA screening questionnaires/scores.

	RDI > 5/h	RDI > 15/h	
Berlin	ESS	No-Apnea	SACS	Stop-Bang	Berlin	ESS	No-Apnea	SACS	Stop-Bang	
Accuracy	0.577	0.494	0.672	0.541	0.577	0.643	0.608	0.762	0.593	0.643	
Sensitivity	0.709	0.322	0.677	0.193	0.709	0.833	0.444	0.888	0.227	0.833	
Specificity	0.444	0.666	0.666	0.888	0.444	0.454	0.772	0.636	0.909	0.454	
PPV	0.814	0.775	0.875	0.857	0.814	0.556	0.615	0.666	0.714	0.555	
NPV	0.307	0.222	0.375	0.242	0.307	0.769	0.629	0.875	0.606	0.769	
ESS = Epworth Sleepiness Scale, NPV = negative predictive value, PPV = positive predictive value, RDI = respiratory disturbance index, SACS = Sleep Apnea Clinical Score.

Table 4 Sequential model for positive in No-Apnea score for obstructive sleep apnea with SACS.

	RDI > 5/h	RDI > 15/h	
Accuracy	0.596	0.616	
Sensitivity	0.193	0.277	
Specificity	1.0	0.954	
PPV	1.0	0.833	
NPV	0.264	0.617	
NPV = negative predictive value, PPV = positive predictive value, RDI = respiratory disturbance index, SACS = Sleep Apnea Clinical Score.

Figure 2. Sequential analysis of questionnaires. RDI = respiratory disturbance index; SACS = Sleep Apnea Clinical Score. Observation: using 2 questionnaires in non-apnea sequence and the SACS we have high specificity.

4. Discussion

This study aimed to determine whether individuals with untreated OSA had a greater susceptibility to SARS-CoV2 infection and/or more unfavorable outcomes than those observed in non-apneic individuals. Using PSG1 and screening tools for diagnosing OSA, we found a high frequency of OSA in our cohort post-COVID-19 patients (77.5%). We believe OSA facilitated SARS-CoV2 infection because it is a pro-inflammatory condition that impairs immune function, increases angiotensin-converting enzyme activity, and is strongly associated with the main risk factors for severe COVID-19, that is, cardiovascular disease.[4,5,14–18]

No association was found between OSA and COVID-19 severity when severity was defined based on the need for ICU admission. However, a high frequency (58%) of hospitalization for COVID-19 in patients with OSA was observed, which was consistent with previous studies.[19]

Sleep disorder questionnaires are used to screen patients with a high diagnostic probability of OSA.[20] In this study, the SACS showed the greatest specificity for all indices (RDI > 5/h and RDI > 15/h); hence, it was employed in all of the sequential models. Conversely, the ESS failed to demonstrate good association with OSA in the post-COVID-19 population and was not considered a suitable tool for sequential analysis. The Berlin questionnaire showed lower values for all the predictive parameters studied in the post-COVID-19 population than those reported originally.[12] Patients with a positive STOP-Bang score (≥3) and an AHI > 5/h showed lower sensitivity and NPV but higher specificity and PPV than those reported originally, while the AHI > 15/h group showed higher values for all the predictive parameters studied.[10] Similarly, the predictive parameters of the No-Apnea score in the post-COVID-19 population showed lower sensitivity but higher specificity for AHI > 5/h and AHI > 15/h than those reported originally.[15]

Sequential analysis of 2 instruments can be performed to increase the accuracy of the questionnaires/scores, first using a high-sensitivity tool followed by a high-specificity tool. We found the best sequential model was a positive No-Apnea score, which indicated high OSA probability, followed by SACS, which resulted in increased SACS specificity, even reaching 100% when RDI > 5/h. This finding highlights the superior performance of this sequence as a diagnostic screening method for OSA in post-COVID-19 patients.

Pulse oxygen saturation measurement is a noninvasive and continuous method that can affect Spo2 and can be reduced in cold exposure, sympathetic nerve excitation, shock and arteriosclerosis. ODI is a polysomnographic parameter that indicates the severity of intermittent nocturnal hypoxemia in OSA.[8] ODI reflects the frequency and duration of nocturnal decline in oxygen saturation. Although not part of the diagnostic criteria or OSA severity definition, its agreement with the RDI showed that ODI can be a good and simple method to diagnose OSA in post-COVID-19 populations.[21,22]

This study had several limitations. First, the study model did not determine whether OSA was already present or if it developed after SARS-CoV2 infection. Second, COVID-19 waves were a complicating factor and interrupted PSG1 assessments for biosafety reasons on different occasions during the study period. Lastly, the start of widespread vaccination motivated the early termination of patient recruitment, as we chose not to study COVID-19 severity in heterogeneous groups (unvaccinated vs vaccinated).[23]

In conclusion, although it has not yet been established as a risk factor for COVID-19, OSA has been a frequent comorbidity in patients hospitalized for COVID-19. This study emphasizes the urgency of diagnosing and treating patients with OSA, especially in countries with low vaccination rates, as new highly transmissible SARS-CoV-2 variants continue to emerge globally.

Author contributions

Conceptualization: Fernanda Oliveira Chibante, Anamélia Costa Faria, Claudia Henrique da Costa, Rogerio Rufino.

Data curation: Fernanda Oliveira Chibante, Anamélia Costa Faria.

Formal analysis: Fernanda Oliveira Chibante, Marcelo Ribeiro-Alves, Rogerio Rufino.

Funding acquisition: Rogerio Rufino.

Investigation: Marcelo Ribeiro-Alves, Rogerio Rufino.

Methodology: Fernanda Oliveira Chibante, Claudia Henrique da Costa, Rogerio Rufino.

Supervision: Rogerio Rufino.

Writing – original draft: Fernanda Oliveira Chibante, Agnaldo José Lopes, Thiago Thomaz Mafort, Rogerio Rufino.

Writing – review & editing: Fernanda Oliveira Chibante, Agnaldo José Lopes, Thiago Thomaz Mafort, Rogerio Rufino.

Abbreviations:

AHI apnea/hypopnea

CI confidence interval

ESS Epworth Sleepiness Scale

NPV negative predictive values

ODI oxygen desaturation index

OSA obstructive sleep apnea

PPV positive predictive values

PSG1 type I polysomnography

RDI respiratory disturbance index

SACS sleep apnea clinical score

SARS-CoV-2 severe acute respiratory syndrome coronavirus 2

Spo2 peripheral oxygen saturation

This project was supported by Rio de Janeiro State Research Foundation (FAPERJ)—n.200.311/2023.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

How to cite this article: Chibante FO, Faria AC, Ribeiro-Alves M, Costa CHd, Lopes AJ, Mafort TT, Rufino R. Questionnaire and polysomnographic evaluation of obstructive sleep apnea in a cohort of post-COVID-19 patients. Medicine 2024;103:37(e38838).
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