Kim S Betts1, Steve Kisely2, Rosa Alati3. 1. School of Public Health, Building 400, Kent Street, Bentley, Curtin University, WA, 6101, Australia. Electronic address: kim.betts@curtin.edu.au. 2. School of Medicine, University of Queensland, Brisbane, Australia. Electronic address: s.kisely@uq.edu.au. 3. School of Public Health, Building 400, Kent Street, Bentley, Curtin University, WA, 6101, Australia; Institute for Social Science Research, University of Queensland, Brisbane, Australia. Electronic address: rosa.alati@curtin.edu.au.
Abstract
AIMS: The accurate identification of mothers at risk of postpartum psychiatric admission would allow for preventive intervention or more timely admission. We developed a prediction model to identify women at risk of postpartum psychiatric admission. METHODS: Data included administrative health data of all inpatient live births in the Australian state of Queensland between January 2009 and October 2014. Analyses were restricted to mothers with one or more indicator of mental health problems during pregnancy (n = 75,054 births). The predictors included all maternal data up to and including the delivery, and neonatal data recorded at delivery. We used multiple machine learning methods to predict hospital admission in the 12 months following delivery in which the primary diagnosis was recorded as an ICD-10 psychotic, bipolar or depressive disorders. RESULTS: The boosted trees algorithm produced the best performing model, predicting postpartum psychiatric admission in the validation data with good discrimination [AUC = 0.80; 95% CI = (0.76, 0.83)] and achieving good calibration. This model outperformed benchmark logistic regression model and an elastic net model. In addition to indicators of maternal metal health history, maternal and neonatal anthropometric measures and social/lifestyle factors were strong predictors. CONCLUSION: Our results indicate the potential of a big data approach when aiming to identify mothers at risk of postpartum psychiatric admission. Mothers at risk could be followed-up and supported after neonatal discharge to either remove the need for admission or facilitate more timely admission.
AIMS: The accurate identification of mothers at risk of postpartum psychiatric admission would allow for preventive intervention or more timely admission. We developed a prediction model to identify women at risk of postpartum psychiatric admission. METHODS: Data included administrative health data of all inpatient live births in the Australian state of Queensland between January 2009 and October 2014. Analyses were restricted to mothers with one or more indicator of mental health problems during pregnancy (n = 75,054 births). The predictors included all maternal data up to and including the delivery, and neonatal data recorded at delivery. We used multiple machine learning methods to predict hospital admission in the 12 months following delivery in which the primary diagnosis was recorded as an ICD-10 psychotic, bipolar or depressive disorders. RESULTS: The boosted trees algorithm produced the best performing model, predicting postpartum psychiatric admission in the validation data with good discrimination [AUC = 0.80; 95% CI = (0.76, 0.83)] and achieving good calibration. This model outperformed benchmark logistic regression model and an elastic net model. In addition to indicators of maternal metal health history, maternal and neonatal anthropometric measures and social/lifestyle factors were strong predictors. CONCLUSION: Our results indicate the potential of a big data approach when aiming to identify mothers at risk of postpartum psychiatric admission. Mothers at risk could be followed-up and supported after neonatal discharge to either remove the need for admission or facilitate more timely admission.
Authors: Andrew M Novick; Melissa Kwitowski; Jack Dempsey; Danielle L Cooke; Allison G Dempsey Journal: Curr Psychiatry Rep Date: 2022-07-23 Impact factor: 8.081