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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.

Author information

1
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.
2
Department of General Practice, University of Groningen, University Medical Center Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands.
3
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: c.l.bockting@amc.uva.nl.
4
Department of Epidemiology and Biostatistics, VU University Medical Center, De Boelelaan 1117, PO Box 7057, Amsterdam, The Netherlands.

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.

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.

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.

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.

KEYWORDS:

Depression; Prediction; Recurrence; Relapse

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