Risk Prediction Models for Depression in Community-Dwelling Older Adults

Abstract

ObjectiveTo develop streamlined Risk Prediction Models (Manto RPMs) for late-life depression. DesignProspective study. SettingThe Survey of Health, Ageing and Retirement in Europe (SHARE) study. ParticipantsParticipants were community residing adults aged 55 years or older. MeasurementsThe outcome was presence of depression at a 2-year follow up evaluation. Risk factors were identified after a literature review of longitudinal studies. Separate RPMs were developed in the 29,116 participants who were not depressed at baseline and in the combined sample of 39,439 of non-depressed and depressed subjects. Models derived from the combined sample were used to develop a web-based risk calculator. ResultsThe authors identified 129 predictors of late-life depression after reviewing 227 studies. In non-depressed participants at baseline, the RPMs based on regression and Least Absolute Shrinkage and Selection Operator (LASSO) penalty (34 and 58 predictors, respectively) and the RPM based on Artificial Neural Networks (124 predictors) had a similar performance (AUC: 0.730–0.743). In the combined depressed and non-depressed participants at baseline, the RPM based on neural networks (35 predictors; AUC: 0.807; 95% CI: 0.80–0.82) and the model based on linear regression and LASSO penalty (32 predictors; AUC: 0.81; 95% CI: 0.79–0.82) had satisfactory accuracy. ConclusionsThe Manto RPMs can identify community-dwelling older individuals at risk for developing depression over 2 years. A web-based calculator based on the streamlined Manto model is freely available at https://manto.unife.it/ for use by individuals, clinicians, and policy makers and may be used to target prevention interventions at the individual and the population levels.