Department of General Practice & Elderly Care Medicine, Amsterdam Public Health Research Institute, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands.
Department of Epidemiology & Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands.
J Affect Disord. 2018 Aug 1;235:105-113. doi: 10.1016/j.jad.2018.04.026. Epub 2018 Apr 6.
Given the poor prognosis of late-life depression, it is crucial to identify those at risk. Our objective was to construct and validate a prediction rule for an unfavourable course of late-life depression.
For development and internal validation of the model, we used The Netherlands Study of Depression in Older Persons (NESDO) data. We included participants with a major depressive disorder (MDD) at baseline (n = 270; 60-90 years), assessed with the Composite International Diagnostic Interview (CIDI). For external validation of the model, we used The Netherlands Study of Depression and Anxiety (NESDA) data (n = 197; 50-66 years). The outcome was MDD after 2 years of follow-up, assessed with the CIDI. Candidate predictors concerned sociodemographics, psychopathology, physical symptoms, medication, psychological determinants, and healthcare setting. Model performance was assessed by calculating calibration and discrimination.
111 subjects (41.1%) had MDD after 2 years of follow-up. Independent predictors of MDD after 2 years were (older) age, (early) onset of depression, severity of depression, anxiety symptoms, comorbid anxiety disorder, fatigue, and loneliness. The final model showed good calibration and reasonable discrimination (AUC of 0.75; 0.70 after external validation). The strongest individual predictor was severity of depression (AUC of 0.69; 0.68 after external validation).
The model was developed and validated in The Netherlands, which could affect the cross-country generalizability.
Based on rather simple clinical indicators, it is possible to predict the 2-year course of MDD. The prediction rule can be used for monitoring MDD patients and identifying those at risk of an unfavourable outcome.
鉴于老年期抑郁症预后较差,因此识别高危人群至关重要。我们的目的是构建和验证预测老年期抑郁症不良病程的规则。
为了开发和验证模型,我们使用了荷兰老年人抑郁研究(NESDO)的数据。我们纳入了基线时有重性抑郁障碍(MDD)的参与者(n=270;60-90 岁),使用复合国际诊断访谈(CIDI)进行评估。为了验证模型的外部有效性,我们使用了荷兰抑郁和焦虑研究(NESDA)的数据(n=197;50-66 岁)。结局是 2 年随访后的 MDD,使用 CIDI 进行评估。候选预测指标包括社会人口统计学、精神病理学、躯体症状、药物治疗、心理决定因素和医疗保健环境。通过计算校准和区分度来评估模型性能。
111 名受试者(41.1%)在 2 年随访后患有 MDD。2 年后发生 MDD 的独立预测因素为(年龄较大)年龄、(早期)抑郁发作、抑郁严重程度、焦虑症状、共病焦虑障碍、疲劳和孤独感。最终模型显示出良好的校准和合理的区分度(AUC 为 0.75;外部验证后为 0.70)。最强的个体预测指标是抑郁严重程度(AUC 为 0.69;外部验证后为 0.68)。
该模型是在荷兰开发和验证的,这可能会影响跨国的普遍适用性。
基于相当简单的临床指标,可以预测 MDD 的 2 年病程。该预测规则可用于监测 MDD 患者并识别那些预后不良的高危人群。