Does obstructive sleep apnea increase the risk of acute sequelae of SARS-CoV-2?

In a recent article published in a magazine sleepyresearchers coordinated to investigate the impact of pre-existing obstructive sleep apnea (OSA) as a risk factor for the acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). created an analysis. [PASC] Both children and adults. The research team used electronic health record (EHR) data from his three research networks within the National Institutes of Health (NIH)-funded REsearching COVID to Enhance Recovery (RECOVER) initiative.

study: Risk of acute sequelae of SARS-CoV-2 infection with a pre-COVID-19 diagnosis of obstructive sleep apnea: an analysis based on electronic health records from the RECOVER initiative. Image credit: p.ill.i / Shutterstock

Background

Previous studies have confirmed a positive relationship between OSA and 2019 acute coronavirus disease (COVID-19) outcome. OSA, characterized by repeated airway obstruction during sleep, is highly prevalent in the United States, affecting nearly 20% of adults. Therefore, further research is needed as a potential risk factor for PASC. Recent estimates put PASC affecting 7% to 54% of his COVID-19 patients, even after full recovery.

The risk of PASC varies by gender, age and certain pre-existing health conditions such as hypertension and diabetes, making OSA a likely risk factor for PASC. Nevertheless, little research has investigated and elucidated the impact of pre-existing conditions such as OSA on his risk of developing PASC. In addition, studies should look beyond acute outcomes in COVID-19 survivors with pre-existing OSA.

About research

This study is the first real-world data analysis conducted across multiple data sources between 1 March 2018 and 1 March 2020, using different Long COVID definitions and different approaches. to identify high-risk COVID-19 patients. PASC with existing OSA. Specifically, the investigators considered evidence of OSA in her diagnosed within 2 years prior to the study period.

The National COVID Cohort Collaborative (N3C) and Patient-Centered Clinical Research Network (PCORnet) included an adult population aged 18 years and older. His PEDSnet division of the latter also covered the pediatric population. N3C analyzed data from over 15 million patients from 77 centers, and PCORnet analyzed from 11 million patients from 19 centers. Similarly, PEDSnet has selected 8.5 million patients from his network of eight pediatric health systems.

The Clinical Science Core (CSC) at New York University Langone Health coordinated all three RECOVER research networks, but each network used a computable phenotype based on diagnosis to uniquely find the likelihood of PASC patients. (CP) was creating its own definition. In particular, algorithms designed to identify patients diagnosed with OSA from EHR have excellent validity. Also, N3C and PCORnet limited their analyzes to adults 21 years of age or older, while PEDSnet restricted them to children under 21 years of age to better distinguish between adult and child results.

The team trained all CPs on a rules-based definition of testing, clinical diagnosis, and medication for patients who visited the Long COVID-19 Clinic.Eligibility criteria are that the patient has demonstrated evidence of novel coronavirus infection between March 1, 2020 and February 28, 2022, usually SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) positive, or antigen test.

In addition, researchers International Statistical Classification of Diseases and Related Health Problems – Clinical Modification (ICD-10-CM)) and ICD-9-CM Diagnostic code or Systematized Medical Nomenclature-Clinical Terminology (SNOMED-CT) code to identify pre-existing OSA patients.

Finally, the researchers used a logistic regression model to determine the unadjusted and adjusted odds ratios (OR) for the association between pre-existing OSA diagnosis and the likelihood of developing PASC with 95% confidence intervals (CI). estimated by

research result

Regardless of the approach used to identify probable PASC patients, the current analysis showed that pre-existing OSA increases the risk of PASC-like symptoms in adult patients. Even after adjusting for other comorbidities, this positive association was slightly attenuated but still significant. Sensitivity analyzes that adjusted for pre-existing hypertension and diabetes instead of comorbidity scores did not change the findings for adults.

Conversely, the apparent positive association between pre-existing OSA and the likelihood of PASC in children ceased to be significant after adjusting for comorbidities. Furthermore, sensitivity analyzes adjusting for childhood asthma, hypertension and diabetes changed the observed associations and yielded different effect estimates.

Obesity was most prevalent in the PCORnet and was responsible for visible differences in OSA-related PASC outcomes across all three networks. In part, obesity and similar comorbidities confounded and reduced the strength of the association with PASC.

Conclusion

PASC is not a cohesive disease, does not yet have an accepted case definition, and patient presentation is highly variable. The study authors therefore used different PASC definitions to overcome these challenges while investigating the association between OSA and PASC risk in adults and children. Finally, we found a consistently positive association between OSA and PASC risk regardless of the data source, approach, or PASC definition applied.

However, the authors did not investigate the most common symptoms in OSA patients with probable PASC. Therefore, future studies should investigate the association of OSA and other pre-existing conditions with specific PASC variations and affected COVID-19 patient trajectories.