Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancy

Abstract Background Perinatal depression is estimated to affect ~ 12% of pregnancies and is linked to numerous negative outcomes. There is currently no model to predict perinatal depression at multiple time-points during and after pregnancy. Methods A prospective cohort design where 858 participants filled in a baseline self-reported survey at week 4–10 of pregnancy (that included social economics, health history, various psychiatric measures), with follow-up until 3 months after delivery. Our primary outcome was an Edinburgh Postnatal Depression Score (EPDS) score of 12 or more (a proxy for perinatal depression) assessed during each trimester and again at two time periods after delivery. Five gradient boosting machines were trained to predict the risk of having EPDS score > = 12 at each of the five follow-up periods. We investigated different predictor sets. Results We developed five prognostic models: PND-T1 (trimester 1), PND-T2 (trimester 2), PND-T3 (trimester 3), PND-A1 (after delivery 1) and PND-A2 (delayed onset after delivery) that calculate personalised risks while only requiring that women be asked 21 questions from 3 validated psychometric scales at weeks 4–10 of pregnancy. C-statistics ranged between 0.69 (0.65–0.73) and 0.77 (0.74–0.80). At 50% sensitivity the precision ranged between 30%-50% across the models, generally identifying groups of patients with double the average risk. Conclusions The five models can predict risk of perinatal depression within each trimester and in two post-natal periods using survey responses as early as week 4 of pregnancy. The models need to be externally validated and prospectively tested to ensure generalizability to any pregnant patient.