Jin H, Wu S, Vidyanti I, Di Capua P, Wu B
Shinyi Wu, PhD, School of Social Work and Epstein Department of Industrial and Systems Engineering, University of Southern California, Edward R. Roybal Institute on Aging, 1150 South Olive Street, Suite 1400, Los Angeles, CA 90015, USA, E-mail:
Methods Inf Med. 2015;54(6):553-9. doi: 10.3414/ME14-02-0009. Epub 2015 Nov 18.
This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare".
Depression is a common and often undiagnosed condition for patients with diabetes. It is also a condition that significantly impacts healthcare outcomes, use, and cost as well as elevating suicide risk. Therefore, a model to predict depression among diabetes patients is a promising and valuable tool for providers to proactively assess depressive symptoms and identify those with depression.
This study seeks to develop a generalized multilevel regression model, using a longitudinal data set from a recent large-scale clinical trial, to predict depression severity and presence of major depression among patients with diabetes.
Severity of depression was measured by the Patient Health Questionnaire PHQ-9 score. Predictors were selected from 29 candidate factors to develop a 2-level Poisson regression model that can make population-average predictions for all patients and subject-specific predictions for individual patients with historical records. Newly obtained patient records can be incorporated with historical records to update the prediction model. Root-mean-square errors (RMSE) were used to evaluate predictive accuracy of PHQ-9 scores. The study also evaluated the classification ability of using the predicted PHQ-9 scores to classify patients as having major depression.
Two time-invariant and 10 time-varying predictors were selected for the model. Incorporating historical records and using them to update the model may improve both predictive accuracy of PHQ-9 scores and classification ability of the predicted scores. Subject-specific predictions (for individual patients with historical records) achieved RMSE about 4 and areas under the receiver operating characteristic (ROC) curve about 0.9 and are better than population-average predictions.
The study developed a generalized multilevel regression model to predict depression and demonstrated that using generalized multilevel regression based on longitudinal patient records can achieve high predictive ability.
本文是《医学信息方法》关于“医疗保健中的大数据与分析”重点主题的一部分。
抑郁症对于糖尿病患者来说是一种常见且常常未被诊断出的病症。它也是一种会显著影响医疗保健结果、使用情况和成本以及增加自杀风险的病症。因此，一个用于预测糖尿病患者抑郁症的模型对于医疗服务提供者主动评估抑郁症状和识别抑郁症患者而言是一个有前景且有价值的工具。
本研究旨在利用近期一项大规模临床试验的纵向数据集开发一个广义多层次回归模型，以预测糖尿病患者的抑郁严重程度和重度抑郁症的存在情况。
通过患者健康问卷PHQ - 9评分来衡量抑郁严重程度。从29个候选因素中选择预测变量，以建立一个二级泊松回归模型，该模型可以对所有患者进行总体平均预测，并对有历史记录的个体患者进行特定个体预测。新获得的患者记录可以与历史记录合并，以更新预测模型。均方根误差（RMSE）用于评估PHQ - 9评分的预测准确性。该研究还评估了使用预测的PHQ - 9评分将患者分类为患有重度抑郁症的分类能力。
为该模型选择了两个时间不变和10个随时间变化的预测变量。合并历史记录并使用它们更新模型可能会提高PHQ - 9评分的预测准确性以及预测评分的分类能力。特定个体预测（针对有历史记录的个体患者）的RMSE约为4，受试者操作特征（ROC）曲线下面积约为0.9，并且优于总体平均预测。
该研究开发了一个广义多层次回归模型来预测抑郁症，并证明基于纵向患者记录使用广义多层次回归可以实现较高的预测能力。
