Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/11787
Title: Can we predict sleep-disordered breathing in pregnancy? The clinical utility of symptoms.
Authors: Wilson, Danielle L;Walker, Susan P;Fung, Alison M;O'Donoghue, Fergal J;Barnes, Maree;Howard, Mark E
Affiliation: Institute for Breathing and Sleep, Austin Health, Heidelberg, Vic., Australia
Issue Date: 10-Jun-2013
Citation: Journal of Sleep Research 2013; 22(6): 670-8
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.
Internal ID Number: 23745721
URI: http://ahro.austin.org.au/austinjspui/handle/1/11787
DOI: 10.1111/jsr.12063
URL: http://www.ncbi.nlm.nih.gov/pubmed/23745721
Type: Journal Article
Subjects: obstructive sleep apnea
polysomnography
receiver operator characteristic curve
screening
sensitivity and specificity
Adult
Body Mass Index
Cohort Studies
Female
Humans
Logistic Models
Mass Screening
Polysomnography
Predictive Value of Tests
Pregnancy
Pregnancy Complications.diagnosis.physiopathology
Pregnancy Trimester, Second
Questionnaires
ROC Curve
Risk
Sleep Apnea Syndromes.complications.diagnosis.physiopathology
Snoring.complications.diagnosis
Young Adult
Appears in Collections:Journal articles

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