Moriarty Andrew S, Paton Lewis W, Snell Kym I E, Riley Richard D, Buckman Joshua E J, Gilbody Simon, Chew-Graham Carolyn A, Ali Shehzad, Pilling Stephen, Meader Nick, Phillips Bob, Coventry Peter A, Delgadillo Jaime, Richards David A, Salisbury Chris, McMillan Dean
Department of Health Sciences, University of York, York, England.
Hull York Medical School, University of York, York, England.
Diagn Progn Res. 2021 Jul 2;5(1):12. doi: 10.1186/s41512-021-00101-x.
Most patients who present with depression are treated in primary care by general practitioners (GPs). Relapse of depression is common (at least 50% of patients treated for depression will relapse after a single episode) and leads to considerable morbidity and decreased quality of life for patients. The majority of patients will relapse within 6 months, and those with a history of relapse are more likely to relapse in the future than those with no such history. GPs see a largely undifferentiated case-mix of patients, and once patients with depression reach remission, there is limited guidance to help GPs stratify patients according to risk of relapse. We aim to develop a prognostic model to predict an individual's risk of relapse within 6-8 months of entering remission. The long-term objective is to inform the clinical management of depression after the acute phase.
We will develop a prognostic model using secondary analysis of individual participant data drawn from seven RCTs and one longitudinal cohort study in primary or community care settings. We will use logistic regression to predict the outcome of relapse of depression within 6-8 months. We plan to include the following established relapse predictors in the model: residual depressive symptoms, number of previous depressive episodes, co-morbid anxiety and severity of index episode. We will use a "full model" development approach, including all available predictors. Performance statistics (optimism-adjusted C-statistic, calibration-in-the-large, calibration slope) and calibration plots (with smoothed calibration curves) will be calculated. Generalisability of predictive performance will be assessed through internal-external cross-validation. Clinical utility will be explored through net benefit analysis.
We will derive a statistical model to predict relapse of depression in remitted depressed patients in primary care. Assuming the model has sufficient predictive performance, we outline the next steps including independent external validation and further assessment of clinical utility and impact.
ClinicalTrials.gov ID: NCT04666662.
大多数抑郁症患者由全科医生（GP）在初级保健机构进行治疗。抑郁症复发很常见（至少50%接受抑郁症治疗的患者在单次发作后会复发），并导致患者出现相当大的发病率且生活质量下降。大多数患者会在6个月内复发，有复发史的患者未来比无复发史的患者更易复发。全科医生面对的患者病情大多未分化，且抑郁症患者达到缓解后，帮助全科医生根据复发风险对患者进行分层的指导有限。我们旨在开发一种预后模型，以预测个体进入缓解期后6至8个月内的复发风险。长期目标是为急性期后抑郁症的临床管理提供依据。
我们将通过对来自七项随机对照试验和一项初级或社区护理环境中的纵向队列研究的个体参与者数据进行二次分析来开发一种预后模型。我们将使用逻辑回归来预测6至8个月内抑郁症复发的结果。我们计划在模型中纳入以下已确定的复发预测因素：残留抑郁症状、既往抑郁发作次数、共病焦虑和首次发作的严重程度。我们将采用“全模型”开发方法，纳入所有可用的预测因素。将计算性能统计量（乐观调整C统计量、总体校准、校准斜率）和校准图（带有平滑校准曲线）。将通过内部-外部交叉验证评估预测性能的可推广性。将通过净效益分析探索临床效用。
我们将推导一个统计模型，以预测初级保健中缓解期抑郁症患者的复发情况。假设该模型具有足够的预测性能，我们概述了后续步骤，包括独立外部验证以及对临床效用和影响的进一步评估。
ClinicalTrials.gov标识符：NCT04666662。
