Kim Kiwon, Ryu Je Il, Lee Bong Ju, Na Euihyeon, Xiang Yu-Tao, Kanba Shigenobu, Kato Takahiro A, Chong Mian-Yoon, Lin Shih-Ku, Avasthi Ajit, Grover Sandeep, Kallivayalil Roy Abraham, Pariwatcharakul Pornjira, Chee Kok Yoon, Tanra Andi J, Tan Chay-Hoon, Sim Kang, Sartorius Norman, Shinfuku Naotaka, Park Yong Chon, Park Seon-Cheol
Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul 05355, Korea.
Department of Neurosurgery, Hanyang University College of Medicine, Seoul 05355, Korea.
J Pers Med. 2022 Jul 26;12(8):1218. doi: 10.3390/jpm12081218.
Psychotic symptoms are rarely concurrent with the clinical manifestations of depression. Additionally, whether psychotic major depression is a subtype of major depression or a clinical syndrome distinct from non-psychotic major depression remains controversial. Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants, we developed a machine-learning-algorithm-based prediction model for concurrent psychotic symptoms in patients with depressive disorders. The advantages of machine learning algorithms include the easy identification of trends and patterns, handling of multi-dimensional and multi-faceted data, and wide application. Among 1171 patients with depressive disorders, those with psychotic symptoms were characterized by significantly higher rates of depressed mood, loss of interest and enjoyment, reduced energy and diminished activity, reduced self-esteem and self-confidence, ideas of guilt and unworthiness, psychomotor agitation or retardation, disturbed sleep, diminished appetite, and greater proportions of moderate and severe degrees of depression compared to patients without psychotic symptoms. The area under the curve was 0.823. The overall accuracy was 0.931 (95% confidence interval: 0.897-0.956). Severe depression (degree of depression) was the most important variable in the prediction model, followed by diminished appetite, subthreshold (degree of depression), ideas or acts of self-harm or suicide, outpatient status, age, psychomotor retardation or agitation, and others. In conclusion, the machine-learning-based model predicted concurrent psychotic symptoms in patients with major depression in connection with the "severity psychosis" hypothesis.
精神病性症状很少与抑郁症的临床表现同时出现。此外，精神病性重度抑郁症是重度抑郁症的一个亚型还是一种与非精神病性重度抑郁症不同的临床综合征仍存在争议。利用亚洲抗抑郁药处方模式研究的数据，我们开发了一种基于机器学习算法的预测模型，用于预测抑郁症患者并发的精神病性症状。机器学习算法的优点包括易于识别趋势和模式、处理多维度和多方面的数据以及广泛的应用。在1171名抑郁症患者中，与没有精神病性症状的患者相比，有精神病性症状的患者具有明显更高的抑郁情绪、兴趣和愉悦感丧失、精力和活动减少、自尊和自信降低、内疚和无价值感、精神运动性激越或迟缓、睡眠障碍、食欲减退的发生率，以及更高比例的中度和重度抑郁症患者。曲线下面积为0.823。总体准确率为0.931（95%置信区间：0.897 - 0.956）。重度抑郁症（抑郁程度）是预测模型中最重要的变量，其次是食欲减退、阈下（抑郁程度）、自伤或自杀观念或行为、门诊状态、年龄、精神运动性迟缓或激越等。总之，基于机器学习的模型结合“严重精神病性”假说预测了重度抑郁症患者并发的精神病性症状。
