Can red blood cell distribution width predict severity of obstructive sleep apnea syndrome?

J Clin Sleep Med. 2012 Oct 15;8(5):521-5. doi: 10.5664/jcsm.2146.

Abstract

Study objectives: Red blood cell distribution width (RDW) is a newly recognized risk marker for various diseases. We evaluated the value of RDW in predicting the severity of obstructive sleep apnea syndrome (OSAS).

Methods: From retrospective analyses of 526 patients admitted to our sleep laboratory for polysomnography between January 2010 and July 2011, 108 patients with complete medical records and hemogram analyses were evaluated.

Results: The study population consisted of 108 patients (age: 49.16 ± 11.1 [range 16-76] years; 72 [66.7%] males). In the overall population, the mean RDW was 14.04 (± 2.37), and 31 patients (28.7%) had RDW > 15. RDW increased significantly with increased severity of OSAS (p = 0.046) and was positively correlated with the apnea-hypopnea index (p = 0.002, r = 0.300), even in the non-anemic group (p = 0.013, r = 0.291). The apnea-hypopnea index was significantly higher in the group with high RDW (> 15; p = 0.046). RDW was negatively correlated with sleep time (p = 0.028, r = 0.217), average oxygen saturation of hemoglobin (p = 0.003, r = -0.239), and minimum desaturation value (p = 0.016, r = -0.235).

Conclusions: In patients referred with a clinical diagnosis of OSAS, RDW may be a marker for the severity of the condition. As RDW is usually included in a complete blood count, it could provide an inexpensive tool for triaging OSAS patients for polysomnography evaluation.

Keywords: Apnea-hypopnea index; hemogram; obstructive sleep apnea syndrome; red blood cell distribution width.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Biomarkers / blood
  • Erythrocyte Indices*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Polysomnography
  • Predictive Value of Tests
  • Retrospective Studies
  • Severity of Illness Index
  • Sleep Apnea, Obstructive / blood*
  • Sleep Apnea, Obstructive / diagnosis
  • Statistics, Nonparametric
  • Young Adult

Substances

  • Biomarkers