0621 A machine learning model to predict the risk of perinatal depression from sleep data in healthy pregnant women
| العنوان: | 0621 A machine learning model to predict the risk of perinatal depression from sleep data in healthy pregnant women |
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| المؤلفون: | Corrado Garbazza, Francesca Mangili, Tatiana Adele D'Onofrio, Daniele Malpetti, Armando D’Agostino, Alessandro Cicolin, Fabio Cirignotta, Mauro Manconi |
| المصدر: | SLEEP. 46:A273-A273 |
| بيانات النشر: | Oxford University Press (OUP), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Physiology (medical), Neurology (clinical) |
| الوصف: | Introduction Perinatal depression (PND) is a complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying predisposing factors for PND during early pregnancy is key to early detection of women who may be at greater risk of developing this condition. Machine Learning (ML) techniques have recently been applied in a few studies, mostly to predict postpartum depression. None of them, however, considered sleep data in model building, neither focused on PND. Methods By analyzing data from a multicenter, prospective cohort study on sleep and mood changes during the perinatal period (the “Life-ON” study), we constructed a ML model for PND risk prediction and tested it in a cross-validation setting. PND was assessed using an EPDS score >12 during 9 visits from early pregnancy until 6 months postpartum. In a bivariate analysis, 47 sociodemographic, psychological, blood-based, medical/gynecological, and subjective sleep variables, collected from 439 pregnant women (33.7±4.2 yrs.) during the first trimester of gestation, as well as 33 polysomnographic (PSG) parameters, recorded from 353 women (33.6±4.2 yrs.) during the second trimester, were correlated with PND. A support vector machine (SVM) model was then trained using the 10-features with the highest permutation importance to predict the individual risk of PND for each woman. Results Among all variables considered, sleep quality (PSQI) and insomnia symptoms (ISI) (p< 0.001), daytime sleepiness (ESS) and RLS severity (IRLS) (p< 0.05), as well as the PSG parameters Apnea Index (p=0.001), number of central hypopneas and percentage of sleep stage N2 (p< 0.05), were all positively correlated to PND. The PSQI and ISI scores were also selected by the SVM classifier, which achieved a mean AUROC of 0.777 and an AUPRC of 0.393, corresponding to a sensitivity of 54.3% and a specificity of 82.6% in identifying women at risk for PND. Conclusion In our data-driven ML model to predict the risk of PND during early pregnancy, subjective poor sleep quality and insomnia symptoms identified women at greater risk of developing PND, while none of the PSG variables improved model performance. Support (if any) Swiss National Science Foundation (grant: 320030_160250/1). The Italian Ministry of Health and Emilia-Romagna Region (grant: PE-2011-02348727). |
| تدمد: | 1550-9109 0161-8105 |
| DOI: | 10.1093/sleep/zsad077.0621 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::bc13bd3c4a8bdbe6a8af106e52f4116b https://doi.org/10.1093/sleep/zsad077.0621 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi...........bc13bd3c4a8bdbe6a8af106e52f4116b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15509109 01618105 |
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| DOI: | 10.1093/sleep/zsad077.0621 |