A Potential New Algorithm for Predicting Obstructive Sleep Apnea in Pregnancy

A new algorithm combining subjective and objective measures to predict obstructive sleep apnea (OSA) in pregnant women was created using retrospectively analyzed data, according to a study published in the Annals of the American Thoracic Society.

Full polysomnography and results from the Multivariable Apnea Prediction Questionnaire from pregnant women during the first and third trimester were used along with Obstructive Sleep Apnea/Hypopnea Syndrome Score and Facco’s apnea predictive model to train an algorithm predicting OSA risk. Sensitivity and specificity of the resulting model were evaluated.

Of the 121 women pregnant during the first and 87 women pregnant during the third trimester, the prevalence of OSA during the first and third trimester was 10.7% and 24.1%, respectively. The best model for predicting OSA incorporated body mass index, age, and presence of tongue enlargement. It has since been abbreviated BATE because of these traits. The area under the curve was 0.86 and 0.87 during the first and third trimesters, respectively. The negative posttest probabilities ranged from 0.03 to 0.07. The sensitivity and specificity were 79.0% and 82.3%, respectively, using first trimester data to predict the risk for OSA in the third trimester.

“The findings of this prospective study showed that a model consisting of [body mass index], age, and tongue enlargement (BATE) was most accurate in predicting OSA risk in our study population consisting of primarily African-American women,” the authors wrote. “To the best of our knowledge, this study is the first to examine whether first trimester questionnaire and physical exam variables can predict OSA risk in the third trimester.”

Disclosures: Dr Gurubhagavatula has received a research grant from BluTech, Inc.

Reference

Izci-Balserak B, Zhu B, Gurubhagavatula I, Keenan BT, Pien GW. A screening algorithm for obstructive sleep apnea in pregnancy [published June 4, 2019]. Ann Am Thorac Soc. doi:10.1513/AnnalsATS.201902-131OC