A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort
Corrado Garbazza, Francesca Mangili, Tatiana Adele D'Onofrio, Daniele Malpetti, Silvia Riccardi, Alessandro Cicolin, Armando D’Agostino, Fabio Cirignotta, Mauro Manconi, Daniele Aquilino, Simone Baiardi, Alessandra Bianconcini, Mariapaola Canevini, Alessandro Cicolin, Fabio Cirignotta, Armando D’Agostino, Renata del Giudice, Valentina Fanti, Filippos Filippakos, Giulia Fior, Cristina Fonti, Francesca Furia, Orsola Gambini, Corrado Garbazza, Alessandra Giordano, Barbara Giordano, Mauro Manconi, Anna Maria Marconi, Alma Martini, Susanna Mondini, Nicoletta Piazza, Erika Raimondo, Silvia Riccardi, Nicola Rizzo, Rossella Santoro, Chiara Serrati, Giuliana Simonazzi, Hans‐Christian Stein, Elena Zambrelli
Abstract
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.