Morelli Davide, Dolezalova Nikola, Ponzo Sonia, Colombo Michele, Plans David
Huma Therapeutics Ltd., London, United Kingdom.
Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.
Front Psychiatry. 2021 Aug 13;12:689026. doi: 10.3389/fpsyt.2021.689026. eCollection 2021.
The burden of depression and anxiety in the world is rising. Identification of individuals at increased risk of developing these conditions would help to target them for prevention and ultimately reduce the healthcare burden. We developed a 10-year predictive algorithm for depression and anxiety using the full cohort of over 400,000 UK Biobank (UKB) participants without pre-existing depression or anxiety using digitally obtainable information. From the initial 167 variables selected from UKB, processed into 429 features, iterative backward elimination using Cox proportional hazards model was performed to select predictors which account for the majority of its predictive capability. Baseline and reduced models were then trained for depression and anxiety using both Cox and DeepSurv, a deep neural network approach to survival analysis. The baseline Cox model achieved concordance of 0.7772 and 0.7720 on the validation dataset for depression and anxiety, respectively. For the DeepSurv model, respective concordance indices were 0.7810 and 0.7728. After feature selection, the depression model contained 39 predictors and the concordance index was 0.7769 for Cox and 0.7772 for DeepSurv. The reduced anxiety model, with 53 predictors, achieved concordance of 0.7699 for Cox and 0.7710 for DeepSurv. The final models showed good discrimination and calibration in the test datasets. We developed predictive risk scores with high discrimination for depression and anxiety using the UKB cohort, incorporating predictors which are easily obtainable smartphone. If deployed in a digital solution, it would allow individuals to track their risk, as well as provide some pointers to how to decrease it through lifestyle changes.
全球抑郁症和焦虑症的负担正在上升。识别出患这些疾病风险增加的个体将有助于针对他们进行预防，并最终减轻医疗负担。我们利用超过40万英国生物银行（UKB）参与者的完整队列，开发了一种针对抑郁症和焦虑症的10年预测算法，这些参与者在基线时没有抑郁症或焦虑症，使用的是可通过数字方式获取的信息。从UKB中最初选择的167个变量，经过处理变成429个特征，使用Cox比例风险模型进行迭代向后消除，以选择占其预测能力大部分的预测因子。然后使用Cox和DeepSurv（一种用于生存分析的深度神经网络方法）对抑郁症和焦虑症的基线模型和简化模型进行训练。基线Cox模型在抑郁症和焦虑症验证数据集上的一致性分别为0.7772和0.7720。对于DeepSurv模型，相应的一致性指数分别为0.7810和0.7728。经过特征选择后，抑郁症模型包含39个预测因子，Cox模型的一致性指数为0.7769，DeepSurv模型为0.7772。简化后的焦虑症模型有53个预测因子，Cox模型的一致性为0.7699，DeepSurv模型为0.7710。最终模型在测试数据集中显示出良好的区分度和校准度。我们利用UKB队列开发了对抑郁症和焦虑症具有高区分度的预测风险评分，纳入了可通过智能手机轻松获取的预测因子。如果将其部署在数字解决方案中，将使个人能够跟踪自己的风险，并提供一些关于如何通过改变生活方式来降低风险的建议。
