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 levels. 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 the depression risk assessment tool DRAT-up, the first prospective RPM to identify late-life depression among community-dwelling subjects aged 60-75. 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, the English Longitudinal Study of Ageing, the Invecchiare nel Chianti, and the Irish Longitudinal Study on Ageing, with 20 206, 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, respectively, 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 the Brier scores to increase by 0.004 and the AUCs to decrease by 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.