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J Psychiatr Res. 2018 Sep;104:1-7. doi: 10.1016/j.jpsychires.2018.06.006. Epub 2018 Jun 8.

Development and validation of a clinical prediction tool to estimate the individual risk of depressive relapse or recurrence in individuals with recurrent depression.

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Department of Clinical Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands; Top Referent Traumacentrum, GGZ Drenthe, Altingerweg 1, 9411 PA Beilen, The Netherlands.
Department of General Practice, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.
Department of Clinical Psychology, University of Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, The Netherlands; Department of Psychiatry, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands. Electronic address:
Department of Epidemiology and Biostatistics, VU University Medical Center, De Boelelaan 1117, PO Box 7057, Amsterdam, The Netherlands.



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.


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.


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.


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.


Depression; Prediction; Recurrence; Relapse

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