Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder characterized by repetitive upper airway obstructions during sleep, leading to intermittent hypoxia, oxidative stress, and systemic inflammation.1,2 It is associated with cardiovascular diseases, metabolic disorders, and impaired immune function.3 The global COVID-19 pandemic has highlighted the role of comorbid conditions in influencing disease progression and outcomes.4

OSA’s physiological impacts, particularly chronic inflammation and coagulopathy, overlap significantly with the pathological mechanisms observed in severe COVID-19 cases. Elevated inflammatory markers such as CRP and D-dimer have been identified as key indicators of disease severity and poor prognosis in COVID-19.5 this study aims to elucidate the relevance of OSA to these parameters and assess its impact on patient outcomes, including ICU admission rates, length of stay, and mortality.

This retrospective study analyzed 120 confirmed COVID-19 patients admitted to a tertiary care hospital between April 2020 and December 2022. Patients were classified into three risk groups for OSA using the STOP-Bang score: high, intermediate, and low risk. Clinical and laboratory parameters, including CRP, D-dimer, ICU stay duration, and outcomes, were compared among these groups.

Demographics, STOP-Bang scores, CRP levels, D-dimer levels, ICU stay duration, and outcomes were extracted from medical records. OSA Risk Categories were classified using STOP-Bang scoring: high risk (score ≥5), intermediate risk (score 3-4), and low risk (score ≤2). Inflammatory Markers included elevated CRP (>10 mg/L) and D-dimer (>500 ng/mL).

Descriptive statistics summarized baseline characteristics. Chi-square tests evaluated associations between STOP-Bang risk categories and outcomes such as ICU admissions and mortality. Multivariate logistic regression assessed the independent impact of STOP-Bang scores on outcomes. P-values <0.05 were considered statistically significant.

Table 1: Patient Demographics and Clinical Characteristics

Table 2: STOP-Bang Risk Categories and Clinical Outcomes

STOP-Bang scoring effectively stratified patients based on their risk of severe outcomes. High-risk patients exhibited markedly higher mortality rates compared to intermediate- and low-risk groups (p = 0.00054). Although there was no statistically significant association between STOP-Bang categories and ICU admissions (p = 0.504), the longer ICU stays in OSA patients underscore the severity of their disease progression. These results align with research by Maas et al. (2020), which demonstrated higher ICU admission rates and mortality in OSA patients compared to those without OSA.6,7

STOP-Bang scoring plays a pivotal role in ICU prioritization and resource allocation. High-risk scores prompt clinicians to implement intensified monitoring, early non-invasive ventilation, and targeted therapeutic interventions such as anticoagulation and anti-inflammatory therapies. Intermediate-risk patients benefit from hybrid management strategies, combining ward-level care with the potential for escalation to ICU if clinical deterioration occurs. Low-risk patients can typically be managed in standard care settings, conserving ICU resources for those with greater needs.7

Elevated CRP and D-dimer levels in OSA patients underscore their predisposition to severe inflammation and coagulopathy. These findings support hypotheses linking intermittent hypoxia in OSA to systemic endothelial dysfunction and heightened thrombotic risks. This overlap with COVID-19’s pathophysiology necessitates tailored treatment approaches for OSA patients during the pandemic. These findings are consistent with Tang et al. (2020) and Levi et al. (2020), who identified similar mechanisms in COVID-19 pathogenesis.8–10

The absence of a direct correlation between STOP-Bang scores and ICU admissions underscores the multifactorial nature of ICU utilization. Factors such as pre-existing comorbidities, age, and clinical management strategies likely influence ICU admission decisions. This complexity emphasizes the need for a holistic approach to patient care, integrating risk stratification tools like STOP-Bang with clinical judgment and resource availability.11,12

Gender-based analyses revealed no significant differences in OSA-related impacts, suggesting that STOP-Bang scoring is equally applicable across sexes. This finding reinforces its utility as a non-invasive, universally applicable tool for risk stratification in diverse populations. It also aligns with Jean-Louis et al. (2019), who highlighted OSA's consistent impact across demographic variables.1,13

Despite its utility, STOP-Bang scoring has limitations when applied in the context of COVID-19. Firstly, it was originally designed as a screening tool for OSA and does not directly account for COVID-19-specific factors such as acute respiratory distress syndrome (ARDS) or cytokine storm severity.14 This could partially explain the lack of a significant correlation between STOP-Bang scores and ICU admissions observed in this study.

Additionally, the reliance on self-reported information for some STOP-Bang parameters, such as snoring and daytime sleepiness, may introduce reporting biases, especially in critically ill patients. These subjective components might reduce the tool’s accuracy in hospitalized settings. Furthermore, STOP-Bang does not differentiate between the severity of OSA or account for variations in hypoxia and systemic inflammation, which are critical determinants of COVID-19 outcomes.15

OSA exacerbates the inflammatory and coagulopathic responses in COVID-19 patients, with high STOP-Bang scores correlating with worse clinical outcomes. These findings underscore the importance of integrating STOP-Bang scoring into COVID-19 management protocols alongside biomarkers like CRP and D-dimer. Further research is needed to evaluate the long-term impacts of OSA on COVID-19 recovery and the efficacy of targeted interventions.

1. Jean-Louis G, Zizi F, Clark LT, Brown CD, McFarlane SI. Obstructive Sleep Apnea and Cardiovascular Disease: Role of the Metabolic Syndrome and Its Components. J Clin Sleep Med. 2008 Jun 15;4(3):261–72.
2. Sforza E, Roche F. Chronic intermittent hypoxia and obstructive sleep apnea: an experimental and clinical approach. Hypoxia. 2016 Apr 27;4:99–108.
3. Parish JM, Adam T, Facchiano L. Relationship of Metabolic Syndrome and Obstructive Sleep Apnea. J Clin Sleep Med. 2007 Aug 15;3(5):467–72.
4. Kamilova U, Ermekbaeva A, Nuritdinov N, Khamraev A, Zakirova G. Occurrence of comorbid diseases in patients after COVID-19. J Med Life. 2023 Mar;16(3):447–50.
5. Patel SV, Pathak JM, Parikh RJ, Pandya KJ, Kothari PB, Patel A. Association of Inflammatory Markers With Disease Progression and the Severity of COVID-19. Cureus. 16(2):e54840.
6. 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):1155–7.
7. Chung F, Abdullah HR, Liao P. STOP-Bang Questionnaire: A Practical Approach to Screen for Obstructive Sleep Apnea. Chest. 2016 Mar;149(3):631–8.
8. Tang N, Li D, Wang X, Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020 Apr;18(4):844–7.
9. Levi M, Thachil J, Iba T, Levy JH. Coagulation abnormalities and thrombosis in patients with COVID-19. Lancet Haematol. 2020 Jun;7(6):e438–40.
10. Shamsuzzaman ASM, Winnicki M, Lanfranchi P, Wolk R, Kara T, Accurso V, et al. Elevated C-reactive protein in patients with obstructive sleep apnea. Circulation. 2002 May 28;105(21):2462–4.
11. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020 Feb 15;395(10223):497–506.
12. Simpson A, Puxty K, McLoone P, Quasim T, Sloan B, Morrison DS. Comorbidity and survival after admission to the intensive care unit: A population-based study of 41,230 patients. J Intensive Care Soc. 2021 May;22(2):143–51.
13. Mou J, Pflugeisen BM, Crick BA, Amoroso PJ, Harmon KT, Tarnoczy SF, et al. The discriminative power of STOP-Bang as a screening tool for suspected obstructive sleep apnea in clinically referred patients: considering gender differences. Sleep Breath. 2019 Mar;23(1):65–75.
14. Dosman JA, Karunanayake CP, Fenton M, Ramsden VR, Seeseequasis J, Mike D, et al. STOP-Bang Score and Prediction of Severity of Obstructive Sleep Apnea in a First Nation Community in Saskatchewan, Canada. Clocks Sleep. 2022 Oct 12;4(4):535–48.
15. Boynton G, Vahabzadeh A, Hammoud S, Ruzicka DL, Chervin RD. Validation of the STOPBANG Questionnaire among Patients Referred for Suspected Obstructive Sleep Apnea. J Sleep Disord Treat Care. 2013;2(4):1000121.