@article{ref1,
title="Development and validation of a clinical prediction tool to estimate the individual risk of depressive relapse or recurrence in individuals with recurrent depression",
journal="Journal of psychiatric research",
year="2018",
author="Klein, Nicola S. and Holtman, Gea A. and Bockting, Claudi L. H. and Heymans, Martijn W. and Burger, Huibert",
volume="104",
number="",
pages="1-7",
abstract="OBJECTIVES: Many studies examined predictors of depressive relapse/recurrence but no simple tool based on well-established risk factors is available that estimates the risk within an individual. We developed and validated such a prediction tool in remitted recurrently depressed individuals. <br><br>METHODS: The tool was developed using data (n = 235) from a pragmatic randomised controlled trial in remitted recurrently depressed participants and externally validated using data (n = 209) from a similar randomised controlled trial of remitted recurrently depressed participants using maintenance antidepressants. Cox regression was used with time to relapse/recurrence within 2 years as outcome and well-established risk factors as predictors. Performance measures and absolute risk scores were calculated, a practically applicable risk score was created, and the tool was externally validated. <br><br>RESULTS: The 2-year cumulative proportion relapse/recurrence was 46.2% in the validation dataset. The tool included number of previous depressive episodes, residual depressive symptoms, severity of the last depressive episode, and treatment. The C-statistic and calibration slope were 0.56 and 0.81 respectively. The tool stratified participants into relapse/recurrence risk classes of 37%, 55%, and 72%. The C-statistic and calibration slope in the external validation were 0.59 and 0.56 respectively, and Kaplan Meier curves showed that the tool could differentiate between risk classes. <br><br>CONCLUSIONS: This is the first study that developed a simple prediction tool based on well-established risk factors of depressive relapse/recurrence, estimating the individual risk. Since the overall performance of the model was poor, more studies are needed to enhance the performance before recommending implementation into clinical practice.<br><br>Copyright © 2018 Elsevier Ltd. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0022-3956",
doi="10.1016/j.jpsychires.2018.06.006",
url="http://dx.doi.org/10.1016/j.jpsychires.2018.06.006"
}