Dynamic prediction and identification of cases at risk of relapse following completion of low-intensity cognitive behavioural therapy
Dynamic prediction and identification of cases at risk of relapse following completion of low-intensity cognitive behavioural therapy
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DOI：
10.1080/10503307.2020.1733127
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发表时间：
2020-03
影响因子：
3.9
通讯作者：
Ben Lorimer;Jaime Delgadillo;S. Kellett;James Lawrence
中科院分区：
文献类型：
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作者：
Ben Lorimer;Jaime Delgadillo;S. Kellett;James Lawrence
摘要目的：低强度认知行为疗法（LiCBT）可以帮助缓解抑郁和焦虑的急性症状，但有些患者在完成治疗后会复发。关于复发的风险因素知之甚少，限制了我们预测其发生的能力。因此，本研究旨在开发一种动态预测工具，以识别复发风险较高的病例。方法：使用机器学习方法（XGBoost）分析LiCBT患者的纵向队列研究数据。样本包括n = 317例治疗完成者，他们每月随访一次，持续12个月（n = 223例复发; 70%）。开发了一套XGBoost算法，以便在患者旅程的四个不同时间点以动态方式预测和调整估计的复发风险（与维持缓解相比）。结果如下：交叉验证设计中的预测准确性指数表明具有足够的普遍性（AUC范围= 0.72-0.84; PPV范围= 71.2-75.3%; NPV范围= 56.0-74.8%）。年轻、失业、（非）线性治疗反应和残留症状被确定为重要的预测因素。讨论：随着新信息的收集，有可能识别有复发风险的病例，并且预测准确性随着时间的推移而提高。早期识别加上有针对性的复发预防可以大大提高LiCBT的长期有效性。
Abstract Objective: Low-intensity cognitive behavioural therapy (LiCBT) can help to alleviate acute symptoms of depression and anxiety, but some patients relapse after completing treatment. Little is known regarding relapse risk factors, limiting our ability to predict its occurrence. Therefore, this study aimed to develop a dynamic prediction tool to identify cases at high risk of relapse. Method: Data from a longitudinal cohort study of LiCBT patients was analysed using a machine learning approach (XGBoost). The sample included n = 317 treatment completers who were followed-up monthly for 12 months (n = 223 relapsed; 70%). An ensemble of XGBoost algorithms was developed in order to predict and adjust the estimated risk of relapse (vs maintained remission) in a dynamic way, at four separate time-points over the course of a patient’s journey. Results: Indices of predictive accuracy in a cross-validation design indicated adequate generalizability (AUC range = 0.72–0.84; PPV range = 71.2–75.3%; NPV range = 56.0–74.8%). Younger age, unemployment, (non-)linear treatment responses, and residual symptoms were identified as important predictors. Discussion: It is possible to identify cases at risk of relapse and predictive accuracy improves over time as new information is collected. Early identification coupled with targeted relapse prevention could considerably improve the longer-term effectiveness of LiCBT.