Risk Prediction Model for Late Life Depression: Development and Validation on Three Large European Datasets

Assessing the risk to develop a specific disease is the first step towards prevention, both at individual and population level. The development and validation of Risk Prediction Models (RPMs) is the norm within different fields of medicine but still underused in psychiatry, despite the global impact of mental disorders. In particular, there is a lack of RPMs to assess the risk of developing depression, the first worldwide cause of disability and harbinger of functional decline in old age. We present DRAT-up, the first prospective RPM to identify late life depression among community-dwelling subjects aged 60 to 75. The development of DRAT-up was based on appraisal of relevant literature, extraction of robust risk estimates and integration into model parameters. A unique feature is the ability to estimate risk even in the presence of missing values. To assess the properties of DRAT-up a validation study was conducted on three European cohorts, namely ELSA, InCHIANTI and TILDA, with 20206, 1359, and 3124 eligible samples, respectively. The model yielded accurate risk estimation in the three datasets from a small number of predictors. The Brier scores were 0.054, 0.133, and 0.041, while the values of Area Under the Curve (AUC) were 0.761, 0.736, and 0.768, respectively. Sensitivity analyses suggest robustness to missing values: setting any individual feature to unknown caused Brier scores to increase of 0.004, and AUCs to decrease of 0.045 in the worst cases. DRAT-up can be readily used for clinical purposes and to aid policy making in the field of mental health.