Zhu Ting, Jiang Jingwen, Hu Yao, Zhang Wei
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Med-X Center for Informatics, Sichuan University, Chengdu, China.
Transl Psychiatry. 2022 Apr 23;12(1):170. doi: 10.1038/s41398-022-01937-7.
Patients with major depressive disorder (MDD) are at high risk of psychiatric readmission while the factors associated with such adverse illness trajectories and the impact of the same factor at different follow-up times remain unclear. Based on machine learning (ML) approaches and real-world electronic medical records (EMR), we aimed to predict individual psychiatric readmission within 30, 60, 90, 180, and 365 days of an initial major depression hospitalization. In addition, we examined to what extent our prediction model could be made interpretable by quantifying and visualizing the features that drive the predictions at different follow-up times. By identifying 13,177 individuals discharged from a hospital located in western China between 2009 and 2018 with a recorded diagnosis of MDD, we established five prediction-modeling cohorts with different follow-up times. Four different ML models were trained with features extracted from the EMR, and explainable methods (SHAP and Break Down) were utilized to analyze the contribution of each of the features at both population-level and individual-level. The model showed a performance on the holdout testing dataset that decreased over follow-up time after discharge: AUC 0.814 (0.758-0.87) within 30 days, AUC 0.780 (0.728-0.833) within 60 days, AUC 0.798 (0.75-0.846) within 90 days, AUC 0.740 (0.687-0.794) within 180 days, and AUC 0.711 (0.676-0.747) within 365 days. Results add evidence that markers of depression severity and symptoms (recurrence of the symptoms, combination of key symptoms, the number of core symptoms and physical symptoms), along with age, gender, type of payment, length of stay, comorbidity, treatment patterns such as the use of anxiolytics, antipsychotics, antidepressants (especially Fluoxetine, Clonazepam, Olanzapine, and Alprazolam), physiotherapy, and psychotherapy, and vital signs like pulse and SBP, may improve prediction of psychiatric readmission. Some features can drive the prediction towards readmission at one follow-up time and towards non-readmission at another. Using such a model for decision support gives the clinician dynamic information of the patient's risk of psychiatric readmission and the specific features pulling towards readmission. This finding points to the potential of establishing personalized interventions that change with follow-up time.
重度抑郁症（MDD）患者有较高的精神科再入院风险，然而，与这种不良疾病轨迹相关的因素以及同一因素在不同随访时间的影响仍不明确。基于机器学习（ML）方法和真实世界电子病历（EMR），我们旨在预测首次重度抑郁住院后30、60、90、180和365天内的个体精神科再入院情况。此外，我们通过量化和可视化在不同随访时间驱动预测的特征，研究了我们的预测模型在多大程度上可以变得可解释。通过识别2009年至2018年期间在中国西部一家医院出院的13177名记录诊断为MDD的个体，我们建立了五个具有不同随访时间的预测建模队列。使用从EMR中提取的特征训练了四种不同的ML模型，并利用可解释方法（SHAP和Break Down）在人群水平和个体水平分析每个特征的贡献。该模型在留出测试数据集上的表现随出院后的随访时间而下降：30天内AUC为0.814（0.758 - 0.87），60天内AUC为0.780（0.728 - 0.833），90天内AUC为0.798（0.75 - 0.846），180天内AUC为0.740（0.687 - 0.794），365天内AUC为0.711（0.676 - 0.747）。结果进一步证明，抑郁严重程度和症状的标志物（症状复发、关键症状组合、核心症状数量和躯体症状），以及年龄、性别、支付类型、住院时间、合并症、治疗模式（如使用抗焦虑药、抗精神病药、抗抑郁药（尤其是氟西汀、氯硝西泮、奥氮平和阿普唑仑）、物理治疗和心理治疗），还有生命体征如脉搏和收缩压，可能会改善精神科再入院的预测。一些特征在一个随访时间可能会驱动预测再入院，而在另一个随访时间则驱动预测不入院。使用这样的模型进行决策支持可为临床医生提供患者精神科再入院风险的动态信息以及促使再入院的具体特征。这一发现指出了建立随随访时间变化的个性化干预措施的潜力。
