Jin Haomiao, Wu Shinyi
Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, United States.
Edward R Roybal Institute on Aging, University of Southern California, Los Angeles, CA, United States.
JMIR Form Res. 2019 Oct 1;3(4):e13610. doi: 10.2196/13610.
Clinical guidelines recommend screening for depression in the general adult population but recognizes that the optimum interval for screening is unknown. Ideal screening intervals should match the patient risk profiles.
This study describes a predictive analytics approach for mining clinical and patient-reported data from a large clinical study for the identification of primary care patients at high risk for depression to match depression screening intervals with patient risk profiles.
This paper analyzed data from a large safety-net primary care study for diabetes and depression. A regression-based data mining technique was used to examine 53 demographics, clinical variables, and patient-reported variables to develop three prediction models for major depression at 6, 12, and 18 months from baseline. Predictors with the strongest predictive power that require low information collection efforts were selected to develop the prediction models. Predictive accuracy was measured by the area under the receiver operating curve (AUROC) and was evaluated by 10-fold cross-validation. The effectiveness of the prediction algorithms in supporting clinical decision making for six "typical" types of patients was demonstrated.
The analysis included 923 patients who were nondepressed at the study baseline. Five patient-reported variables were selected in the prediction models to predict major depression at 6, 12, and 18 months: (1) Patient Health Questionnaire 2-item score; (2) the Sheehan Disability Scale; (3) previous problems with depression; (4) the diabetes symptoms scale; and (5) emotional burden of diabetes. All three depression prediction models had an AUROC>0.80, comparable with published depression prediction studies. Among the 6 "typical" types of patients, the algorithms suggest that patients who reported impaired daily functioning by health status are at an elevated risk for depression in all three periods.
This study demonstrated that leveraging patient-reported data and prediction models can help improve identification of high-risk patients and clinical decisions about the depression screening interval for diabetes patients. Implementation of this approach can be coupled with application of modern technologies such as telehealth and mobile health assessment for collecting patient-reported data to improve privacy, reducing stigma and costs, and promoting a personalized depression screening that matches screening intervals with patient risk profiles.
临床指南建议对普通成年人群进行抑郁症筛查，但认识到最佳筛查间隔尚不清楚。理想的筛查间隔应与患者风险概况相匹配。
本研究描述了一种预测分析方法，用于从一项大型临床研究中挖掘临床和患者报告的数据，以识别抑郁症高危的初级保健患者，使抑郁症筛查间隔与患者风险概况相匹配。
本文分析了一项针对糖尿病和抑郁症的大型安全网初级保健研究的数据。使用基于回归的数据挖掘技术来检查53个人口统计学、临床变量和患者报告的变量，以建立从基线起6个月、12个月和18个月时重度抑郁症的三个预测模型。选择预测能力最强且信息收集工作量低的预测因子来建立预测模型。预测准确性通过受试者工作特征曲线下面积（AUROC）来衡量，并通过10倍交叉验证进行评估。展示了预测算法在支持六种“典型”类型患者的临床决策方面的有效性。
分析包括923名在研究基线时无抑郁症的患者。在预测模型中选择了五个患者报告的变量来预测6个月、12个月和18个月时的重度抑郁症：（1）患者健康问卷2项得分；（2）希恩残疾量表；（3）既往抑郁症问题；（4）糖尿病症状量表；（5）糖尿病的情感负担。所有三个抑郁症预测模型的AUROC>0.80，与已发表的抑郁症预测研究相当。在六种“典型”类型的患者中，算法表明，在所有三个时期，报告日常功能因健康状况受损的患者患抑郁症的风险都有所升高。
本研究表明，利用患者报告的数据和预测模型有助于改善高危患者的识别以及糖尿病患者抑郁症筛查间隔的临床决策。这种方法的实施可以与远程医疗和移动健康评估等现代技术相结合，用于收集患者报告的数据，以提高隐私性、减少耻辱感和成本，并促进与患者风险概况相匹配的个性化抑郁症筛查。
