title: Modelling fluid accumulation in the neck using simple baseline fluid metrics: implications for sleep apnea.
abstract: Obstructive sleep apnea (OSA) is a common respiratory disorder among adults. Recently we have shown that sedentary lifestyle causes an increase in diurnal leg fluid volume (LFV), which can shift into the neck at night when lying down to sleep and increase OSA severity. The purpose of this work was to investigate various metrics that represent baseline fluid retention in the legs and examine their correlation with neck fluid volume (NFV) and to develop a robust model for predicting fluid accumulation in the neck. In 13 healthy awake non-obese men, LFV and NFV were recorded continuously and simultaneously while standing for 5 minutes and then lying supine for 90 minutes. Simple regression was used to examine correlations between baseline LFV, baseline neck circumference (NC) and change in LFV with the outcome variables: change in NC (ΔNC) and in NFV (ΔNFV90) after lying supine for 90 minutes. An exhaustive grid search was implemented to find combinations of input variables which best modeled outcomes. We found strong positive correlations between baseline LFV (supine and standing) and ΔNFV90. Models developed for predicting ΔNFV90 included baseline standing LFV, baseline NC combined with change in LFV after lying supine for 90 minutes. These correlations and the developed models suggest that a greater baseline LFV might contribute to increased fluid accumulation in the neck. These results give more evidence that sedentary lifestyle might play a role in the pathogenesis of OSA by increasing the baseline LFV. The best models for predicting ΔNC include baseline LFV and NC; they improved accuracies of estimating ΔNC over individual predictors, suggesting that a combination of baseline fluid metrics is a good predictor of the change in NC while lying supine. Future work is aimed at adding additional baseline demographic features to improve model accuracy and eventually use it as a screening tool to predict severity of OSA prior to sleep.
authors.forename: Daniel
authors.initials: D
authors.lastname: Vena
authors.forename: A
authors.initials: A
authors.lastname: Yadollahi
authors.forename: T Douglas
authors.initials: TD
authors.lastname: Bradley
mesh_terms: D000328:Adult; D000465:Algorithms; D018495:Fluid Shifts; D006801:Humans; D007866:Leg / Q000503:physiopathology; D008297:Male; D008954:Models, Biological; D009333:Neck / Q000503:physiopathology*; D020181:Sleep Apnea, Obstructive / Q000503:physiopathology*; D016683:Supine Position / Q000502:physiology; D014851:Wakefulness / Q000502:physiology