Kessler R C, van Loo H M, Wardenaar K J, Bossarte R M, Brenner L A, Cai T, Ebert D D, Hwang I, Li J, de Jonge P, Nierenberg A A, Petukhova M V, Rosellini A J, Sampson N A, Schoevers R A, Wilcox M A, Zaslavsky A M
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Mol Psychiatry. 2016 Oct;21(10):1366-71. doi: 10.1038/mp.2015.198. Epub 2016 Jan 5.
Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.
重度抑郁症（MDD）病程的异质性使临床决策变得复杂。尽管利用症状特征或生物标志物来开发具有临床实用价值的预后亚型的努力成效有限，但最近一份报告显示，根据世界卫生组织世界心理健康（WMH）调查中终生患MDD的受访者关于首次发作特征和共病的自我报告所开发的机器学习（ML）模型，能够较为准确地预测MDD的持续性、慢性程度和严重程度。我们报告了在一个独立的前瞻性全国住户样本中对模型进行验证的结果，该样本中有1056名在基线时患有终生MDD的受访者。将WMH的ML模型应用于这些基线数据，以生成预测结果分数，并将其与基线后10 - 12年评估的观察分数进行比较。还将ML模型的预测准确性与传统逻辑回归模型的预测准确性进行了比较。基于ML的受试者工作特征曲线下面积（高慢性程度为0.63，其他前瞻性结果为0.71 - 0.76）始终高于逻辑模型（0.62 - 0.70），尽管后者模型包含更多预测因素。在后续具有高持续性慢性程度的受访者中，共有34.6 - 38.1%以及具有严重程度指标的受访者中有40.8 - 55.8%处于基线ML预测风险分布的前20%，而在后续住院的受访者中只有0.9%以及有自杀未遂情况的受访者中有1.5%处于ML预测风险分布的最低20%。这些结果证实，可以从患者基线自我报告中生成具有临床实用价值的MDD风险分层模型，并且在开发此类模型方面，ML方法优于传统方法。
