title: Can we predict sleep-disordered breathing in pregnancy? The clinical utility of symptoms.
abstract: Sleep-disordered breathing (SDB) is reported commonly during pregnancy and is associated with an increased risk of adverse maternal and fetal outcomes, but the majority of these data are based upon self-report measures not validated for pregnancy. This study examined the predictive value of screening questionnaires for SDB administered at two time-points in pregnancy, and attempted to develop an 'optimized predictive model' for detecting SDB in pregnancy. A total of 380 women were recruited from an antenatal clinic in the second trimester of pregnancy. All participants completed the Berlin Questionnaire and the Multivariable Apnea Risk Index (MAP Index) at recruitment, with a subset of 43 women repeating the questionnaires at the time of polysomnography at 37 weeks' gestation. Fifteen of 43 (35%) women were confirmed to have a respiratory disturbance index (RDI) > 5 h(-1) . Prediction of an RDI > 5 h(-1) was most accurate during the second trimester for both the Berlin Questionnaire (sensitivity 0.93, specificity 0.50, positive predictive value 0.50 and negative predictive value 0.93), and the MAP Index [area under the receiver operating characteristic (ROC) curve of 0.768]. A stepwise selection model identified snoring volume, a body mass index (BMI)≥32 kg m(-2) and tiredness upon awakening as the strongest independent predictors of SDB during pregnancy; this model had an area under the ROC curve of 0.952. We conclude that existing clinical prediction models for SDB perform inadequately as a screening tool in pregnancy. The development of a highly predictive model from our data shows promise for a quick and easy screening tool to be validated for future use in pregnancy.
authors.affiliation: Institute for Breathing and Sleep, Austin Health, Heidelberg, Vic., Australia.
authors.forename: Danielle L
authors.initials: DL
authors.lastname: Wilson
authors.forename: Susan P
authors.initials: SP
authors.lastname: Walker
authors.forename: Alison M
authors.initials: AM
authors.lastname: Fung
authors.forename: Fergal
authors.initials: F
authors.lastname: O'Donoghue
authors.forename: Maree
authors.initials: M
authors.lastname: Barnes
authors.forename: Mark
authors.initials: M
authors.lastname: Howard
mesh_terms: D000328:Adult; D015992:Body Mass Index; D015331:Cohort Studies; D005260:Female; D006801:Humans; D016015:Logistic Models; D008403:Mass Screening; D017286:Polysomnography; D011237:Predictive Value of Tests; D011247:Pregnancy; D011248:Pregnancy Complications / Q000175:diagnosis* / Q000503:physiopathology; D011262:Pregnancy Trimester, Second; D012372:ROC Curve; D012306:Risk; D012891:Sleep Apnea Syndromes / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology; D012913:Snoring / Q000150:complications / Q000175:diagnosis; D011795:Surveys and Questionnaires; D055815:Young Adult
keywords: obstructive sleep apnea; polysomnography; receiver operator characteristic curve; screening; sensitivity and specificity