Hull York Medical School and Department of Health Sciences, University of York, York, Yorkshire, UK
Hull York Medical School and Department of Health Sciences, University of York, York, Yorkshire, UK.
BMJ Ment Health. 2024 Oct 28;27(1):e301226. doi: 10.1136/bmjment-2024-301226.
Relapse of depression is common and contributes to the overall associated morbidity and burden. We lack evidence-based tools to estimate an individual's risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention.
The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care.
Multilevel logistic regression models were developed, using individual participant data from seven primary care-based studies (n=1244), to predict relapse of depression. The model was internally validated using bootstrapping, and generalisability was explored using internal-external cross-validation.
Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p<0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p<0.001) were associated with relapse. The validated model had low discrimination (C-statistic 0.60 (0.55-0.65)) and miscalibration concerns (calibration slope 0.81 (0.31-1.31)). On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.28-0.67), p<0.001); this remained statistically significant after correction for multiple significance testing.
We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. Relationship status warrants further research to explore its role as a prognostic factor for relapse.
Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. Where possible, this could be guided by the presence or absence of known prognostic factors (eg, residual depressive symptoms) and targeted towards these.
NCT04666662.
抑郁症的复发很常见，会导致整体相关发病率和负担增加。我们缺乏基于证据的工具来评估个体在初级保健治疗后的复发风险，这可能有助于我们更有效地针对预防复发。
旨在开发和验证一种预测初级保健中抑郁症复发风险的预后模型。
使用来自七项基于初级保健的研究（n=1244）的个体参与者数据，使用多水平逻辑回归模型来预测抑郁症的复发。使用自举法对内进行验证，并通过内部-外部交叉验证探索通用性。
残留的抑郁症状（OR：1.13（95%CI：1.07-1.20），p<0.001）和基线抑郁严重程度（OR：1.07（1.04-1.11），p<0.001）与复发相关。验证后的模型具有较低的区分度（C 统计量为 0.60（0.55-0.65））和校准问题（校准斜率为 0.81（0.31-1.31））。在二次分析中，处于恋爱关系与降低复发风险相关（OR：0.43（0.28-0.67），p<0.001）；在进行多次显著检验校正后，这仍然具有统计学意义。
在使用常规记录的指标时，我们无法在初级保健数据中准确预测抑郁症复发的风险。恋爱关系值得进一步研究，以探索其作为复发预后因素的作用。
在我们能够根据复发风险对患者进行准确分层之前，普遍的预防复发方法可能是最有益的，无论是在急性期治疗期间还是缓解后。在可能的情况下，可以根据是否存在已知的预后因素（例如，残留的抑郁症状）来指导这种方法，并针对这些因素进行治疗。
NCT04666662。
