Chondros Patty, Davidson Sandra, Wolfe Rory, Gilchrist Gail, Dowrick Christopher, Griffiths Frances, Hegarty Kelsey, Herrman Helen, Gunn Jane
Department of General Practice, The University of Melbourne, Australia.
Department of General Practice, The University of Melbourne, Australia.
J Affect Disord. 2018 Feb;227:854-860. doi: 10.1016/j.jad.2017.11.042. Epub 2017 Nov 13.
Depression trajectories among primary care patients are highly variable, making it difficult to identify patients that require intensive treatments or those that are likely to spontaneously remit. Currently, there are no easily implementable tools clinicians can use to stratify patients with depressive symptoms into different treatments according to their likely depression trajectory. We aimed to develop a prognostic tool to predict future depression severity among primary care patients with current depressive symptoms at three months.
Patient-reported data from the diamond study, a prospective cohort of 593 primary care patients with depressive symptoms attending 30 Australian general practices. Participants responded affirmatively to at least one of the first two PHQ-9 items. Twenty predictors were pre-selected by expert consensus based on reliability, ease of administration, likely patient acceptability, and international applicability. Multivariable mixed effects linear regression was used to build the model.
The prognostic model included eight baseline predictors: sex, depressive symptoms, anxiety, history of depression, self-rated health, chronic physical illness, living alone, and perceived ability to manage on available income. Discrimination (c-statistic =0.74; 95% CI: 0.70-0.78) and calibration (agreement between predicted and observed symptom scores) were acceptable and comparable to other prognostic models in primary care.
More complex model was not feasible because of modest sample size. Validation studies needed to confirm model performance in new primary care attendees.
A brief, easily administered algorithm predicting the severity of depressive symptoms has potential to assist clinicians to tailor treatment for adult primary care patients with current depressive symptoms.
初级保健患者的抑郁轨迹高度可变，难以识别需要强化治疗的患者或可能自发缓解的患者。目前，临床上没有易于实施的工具可用于根据抑郁轨迹将有抑郁症状的患者分层进行不同治疗。我们旨在开发一种预后工具，以预测有当前抑郁症状的初级保健患者三个月后的抑郁严重程度。
数据来自钻石研究中患者报告的数据，这是一项对593名有抑郁症状的初级保健患者的前瞻性队列研究，这些患者来自澳大利亚的30家全科诊所。参与者对PHQ-9前两项中的至少一项做出肯定回答。基于可靠性、易于管理、患者可能的接受度和国际适用性，通过专家共识预先选择了20个预测因素。使用多变量混合效应线性回归建立模型。
预后模型包括八个基线预测因素：性别、抑郁症状、焦虑、抑郁病史、自我健康评分、慢性身体疾病、独居以及对可用收入的管理能力感知。辨别力（c统计量=0.74；95%置信区间：0.70-0.78）和校准（预测症状评分与观察症状评分之间的一致性）是可接受的，并且与初级保健中的其他预后模型相当。
由于样本量适中，更复杂的模型不可行。需要进行验证研究以确认新的初级保健就诊者中模型的性能。
一种简短、易于管理的预测抑郁症状严重程度的算法有可能帮助临床医生为有当前抑郁症状的成年初级保健患者量身定制治疗方案。
