Background: Asthma is the most common chronic disease affecting pregnancy, and poor asthma control has been associated with adverse pregnancy outcomes. However, the trajectory of asthma control during pregnancy is not well understood or characterized.
Objective: To identify and characterize trajectories of gestational asthma control in a US-based prospective pregnancy cohort.
Methods: A k-means algorithm for joint longitudinal data was used to cluster pregnant women with and without asthma into gestational asthma control trajectories on the basis of daily activity limitation, nighttime symptoms, inhaler use, and respiratory symptoms.
Results: Among 308 women with asthma, 2 trajectories of gestational asthma control were identified and labeled "same" (n = 184; 59.5%) or "worse" (n = 124; 40.5%). Contrary to previous studies, we did not observe women with better asthma control in pregnancy. Women belonging to the "worse" trajectory experienced frequent and stable activity limitation and inhaler use, as well as frequent and increasing nighttime symptoms (∼3 d/gestational week) and respiratory symptoms (∼5 times/wk). Women belonging to the "same" trajectory experienced infrequent and stable activity limitation, inhaler use, and respiratory symptoms, as well as infrequent and slightly increasing (∼1 d/gestational week) nighttime symptoms. Results from pregnant women without asthma (n = 107) suggest that pregnancy alone was not responsible for changes in symptoms over time.
Conclusions: In this US-based obstetric cohort receiving care according to standard clinical practice, gestational asthma control worsened for about 40% of women.
Keywords: Algorithms; Asthma; Exacerbations; Machine learning; Pregnancy.
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