Screening for severe obstructive sleep apnea syndrome in hypertensive outpatients

Indira Gurubhagavatula, Barry G Fields, Christian R Morales, Sharon Hurley, Grace W Pien, Lindsay C Wick, Bethany A Staley, Raymond R Townsend, Greg Maislin, Indira Gurubhagavatula, Barry G Fields, Christian R Morales, Sharon Hurley, Grace W Pien, Lindsay C Wick, Bethany A Staley, Raymond R Townsend, Greg Maislin

Abstract

The authors attempted to validate a 2-stage strategy to screen for severe obstructive sleep apnea syndrome (s-OSAS) among hypertensive outpatients, with polysomnography (PSG) as the gold standard. Using a prospective design, outpatients with hypertension were recruited from medical outpatient clinics. Interventions included (1) assessment of clinical data; (2) home sleep testing (HST); and (3) 12-channnel, in-laboratory PSG. The authors developed models using clinical or HST data alone (single-stage models) or clinical data in tandem with HST (2-stage models) to predict s-OSAS. For each model, area under receiver operating characteristic curves (AUCs), sensitivity, specificity, negative likelihood ratio, and negative post-test probability (NPTP) were computed. Models were then rank-ordered based on AUC values and NPTP. HST used alone had limited accuracy (AUC=0.727, NPTP=2.9%). However, models that used clinical data in tandem with HST were more accurate in identifying s-OSAS, with lower NPTP: (1) facial morphometrics (AUC=0.816, NPTP=0.6%); (2) neck circumference (AUC=0.803, NPTP=1.7%); and Multivariable Apnea Prediction Score (AUC=0.799, NPTP=1.5%) where sensitivity, specificity, and NPTP were evaluated at optimal thresholds. Therefore, HST combined with clinical data can be useful in identifying s-OSAS in hypertensive outpatients, without incurring greater cost and patient burden associated with in-laboratory PSG. These models were less useful in identifying obstructive sleep apnea syndrome of any severity.

© 2013 Wiley Periodicals, Inc.

Figures

Figure 1
Figure 1
Flow diagram. PSGs indicates polysomnograms; HSTs, home sleep tests; MVAPs, multivariable apnea prediction scores.
Figure 2
Figure 2
Study design. HST indicates home sleep test; AHI, Apnea‐Hypopnea Index; s‐OSAS, severe obstructive sleep apnea associated with sleepiness.
Figure 3
Figure 3
Frequency distribution of obstructive sleep apnea by body mass index category (BMI). s‐OSAS indicates severe obstructive sleep apnea syndrome.
Figure 4
Figure 4
Bland‐Altman analysis. Unattended sleep studies tended to underestimate in‐laboratory AHI for an approximate value of 3.5 to 3.8 log units/hour, which corresponds to an average of portable Apnea‐Hypopnea Index (AHI) + polysomnography (PSG) AHI of 33 to 45 per hour. We used log transformation of the AHIs in our prediction models to reduce the undue influence of large values. uAHI indicates AHI from unattended home sleep test.

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