Predictive value of red cell distribution width for overlap syndrome in obstructive sleep apnea

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AKYOL GÜRSES A., Akyildiz U. O.

Frontiers in Neurology, vol.15, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 15
  • Publication Date: 2024
  • Doi Number: 10.3389/fneur.2024.1415410
  • Journal Name: Frontiers in Neurology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, Directory of Open Access Journals
  • Keywords: cardiovascular risk, chronic obstructive pulmonary disease, inflammation, OSAS, overlap syndrome, red cell distribution width (RDW)
  • Gazi University Affiliated: Yes


Purpose: Obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) are prevalent disorders, and the concurrence so-called overlap syndrome (OVS) is not rare either. Early recognition of OVS is essential because this group is more prone to cardiovascular morbidities and requires effective multidisciplinary follow-up. This study aimed to evaluate RDW in patients with severe OSAS and investigate whether it can predict OVS. Patients and methods: 96 patients were retrospectively analyzed, of whom 66 were found to have severe OSAS alone and 30 OVS during diagnostic workups. Demographic, polysomnographic, and laboratory results, including RDW, were compared between groups. Multivariate logistic regression was used to determine independent associates of OVS. Results: Gender and body mass index (BMI) were similar, however, the mean age and RDW were higher in the OVS group (p:0.008, p:0.002). The increase in RDW remained significant after adjustment for age, BMI, and cardiovascular risk factors. An RDW value of >13.65% was shown to have a 78.3% sensitivity and 60% specificity for predicting OVS in severe OSAS (p:0.004). Conclusion: The results suggest that RDW can be a reliable indicator for diagnosing OVS in OSAS. It can help in identifying the subset of patients who would benefit from proper consultations and multidisciplinary follow-up, leading to appropriate treatment of each disease component and effective monitoring to prevent adverse cardiovascular outcomes.