A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort.

Garbazza, Corrado; Mangili, Francesca; D'Onofrio, Tatiana Adele; Malpetti, Daniele; Riccardi, Silvia; Cicolin, Alessandro; D'Agostino, Armando; Cirignotta, Fabio; Manconi, Mauro (2024). A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort. Psychiatry research, 337, p. 115957. Elsevier 10.1016/j.psychres.2024.115957

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Perinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying risk factors for PND is key to early detect women at increased risk of developing this condition. We applied a machine learning (ML) approach to data from a multicenter cohort study on sleep and mood changes during the perinatal period ("Life-ON") to derive models for PND risk prediction in a cross-validation setting. A wide range of sociodemographic variables, blood-based biomarkers, sleep, medical, and psychological data collected from 439 pregnant women, as well as polysomnographic parameters recorded from 353 women, were considered for model building. These covariates were correlated with the risk of future depression, as assessed by regularly administering the Edinburgh Postnatal Depression Scale across the perinatal period. The ML model indicated the mood status of pregnant women in the first trimester, previous depressive episodes and marital status, as the most important predictors of PND. Sleep quality, insomnia symptoms, age, previous miscarriages, and stressful life events also added to the model performance. Besides other predictors, sleep changes during early pregnancy should therefore assessed to identify women at higher risk of PND and support them with appropriate therapeutic strategies.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0165-1781

Publisher:

Elsevier

Language:

English

Submitter:

Pubmed Import

Date Deposited:

27 May 2024 11:28

Last Modified:

22 Oct 2024 16:08

Publisher DOI:

10.1016/j.psychres.2024.115957

PubMed ID:

38788556

Additional Information:

Collaborators “Life-ON” study group: Daniele Aquilino, Renata Del Giudice, Giulia Fior, Francesca Furia, Barbara Giordano, Alma Martini, Chiara Serrati, Elena Zambrelli
(Department of Neurology, University Hospital, Inselspital, Bern, Switzerland)

Uncontrolled Keywords:

Depression Postpartum Pregnancy Risk factors Sleep Women

BORIS DOI:

10.48350/197084

URI:

https://boris.unibe.ch/id/eprint/197084

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