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.
J Psychiatr Res. 2018 Sep;104:1-7. doi: 10.1016/j.jpsychires.2018.06.006. Epub 2018 Jun 8.
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.
许多研究探讨了抑郁复发/再发的预测因素，但没有基于已确立的风险因素的简单工具可以评估个体的风险。我们在缓解后复发性抑郁患者中开发并验证了这样的预测工具。
该工具使用来自缓解后复发性抑郁患者的实用随机对照试验的数据（n=235）进行开发，并使用类似的缓解后复发性抑郁患者使用维持性抗抑郁药的随机对照试验的数据（n=209）进行外部验证。使用 Cox 回归，以 2 年内复发/再发的时间作为结果，以已确立的风险因素作为预测因素。计算了性能指标和绝对风险评分，创建了一个实用的风险评分，并对该工具进行了外部验证。
验证数据集中 2 年累积复发/再发率为 46.2%。该工具包括既往抑郁发作次数、残留抑郁症状、最后一次抑郁发作严重程度和治疗情况。C 统计量和校准斜率分别为 0.56 和 0.81。该工具将患者分为复发/再发风险等级为 37%、55%和 72%的三组。外部验证的 C 统计量和校准斜率分别为 0.59 和 0.56，Kaplan-Meier 曲线表明该工具可以区分风险等级。
这是第一项基于抑郁复发/再发的已确立风险因素开发简单预测工具的研究，可评估个体风险。由于模型的整体性能较差，因此在推荐将其应用于临床实践之前，需要进行更多的研究来提高其性能。
