Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada.
School of Psychology, University of Aberdeen, Aberdeen, UK.
J Affect Disord. 2024 Jul 15;357:148-155. doi: 10.1016/j.jad.2024.04.098. Epub 2024 Apr 24.
Anxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on their overall well-being is exacerbated. Early identification of anxiety disorders using machine learning (ML) can potentially mitigate the adverse consequences associated with these disorders.
We applied ML to the data from the Canadian Longitudinal Study on Aging (CLSA) to predict the onset of anxiety disorders approximately three years in the future. We used Shapley value-based methods to determine the top factor for prediction. We also investigated whether anxiety onset can be predicted by baseline depression-related predictors alone.
Our model was able to predict anxiety onset accurately (Area under the Receiver Operating Characteristic Curve or AUC = 0.814 ± 0.016 (mean ± standard deviation), balanced accuracy = 0.741 ± 0.016, sensitivity = 0.743 ± 0.033, and specificity = 0.738 ± 0.010). The top predictive factors included prior depression or mood disorder diagnosis, high frailty, anxious personality, and low emotional stability. Depression and mood disorders are well known comorbidity of anxiety; however a prior depression or mood disorder diagnosis could not predict anxiety onset without other factors.
While our findings underscore the importance of a prior depression diagnosis in predicting anxiety, they also highlight that it alone is inadequate, signifying the necessity to incorporate additional predictors for improved prediction accuracy.
Our study showcases promising prospects for using machine learning to develop personalized prediction models for anxiety onset in middle-aged and older adults using easy-to-access survey data.
焦虑障碍是中年及以上人群中最常见的心理健康障碍之一。由于老年人更容易患有多种合并症或身体虚弱，焦虑障碍对他们整体健康的影响更加严重。使用机器学习（ML）早期识别焦虑障碍可能潜在地减轻与这些障碍相关的不良后果。
我们将 ML 应用于加拿大老龄化纵向研究（CLSA）的数据中，以预测大约三年后焦虑障碍的发病。我们使用基于 Shapley 值的方法确定预测的首要因素。我们还研究了基线与抑郁相关的预测因素是否可以单独预测焦虑的发病。
我们的模型能够准确预测焦虑的发病（接收器工作特征曲线下面积或 AUC=0.814±0.016（平均值±标准偏差），平衡准确性=0.741±0.016，敏感性=0.743±0.033，特异性=0.738±0.010）。首要预测因素包括先前的抑郁或心境障碍诊断、高脆弱性、焦虑型人格和低情绪稳定性。抑郁和心境障碍是焦虑的已知合并症；然而，没有其他因素，仅先前的抑郁或心境障碍诊断无法预测焦虑的发病。
虽然我们的研究结果强调了先前的抑郁诊断在预测焦虑中的重要性，但它们也突出表明，仅靠这一点是不够的，这表明需要纳入其他预测因素以提高预测准确性。
我们的研究展示了使用机器学习的前景，可使用易于获取的调查数据为中年及以上成年人开发焦虑发病的个性化预测模型。
