Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany.
Jülich Aachen Research Alliance (JARA) - Translational Brain Medicine, Aachen, Germany.
Transl Psychiatry. 2018 Oct 30;8(1):237. doi: 10.1038/s41398-018-0294-4.
The clinical presentation of patients with schizophrenia has long been described to be very heterogeneous. Coherent symptom profiles can probably be directly derived from behavioral manifestations quantified in medical questionnaires. The combination of machine learning algorithms and an international multi-site dataset (n = 218 patients) identified distinctive patterns underlying schizophrenia from the widespread PANSS questionnaire. Our clustering approach revealed a negative symptom patient group as well as a moderate and a severe group, giving further support for the existence of schizophrenia subtypes. Additionally, emerging regression analyses uncovered the most clinically predictive questionnaire items. Small subsets of PANSS items showed convincing forecasting performance in single patients. These item subsets encompassed the entire symptom spectrum confirming that the different facets of schizophrenia can be shown to enable improved clinical diagnosis and medical action in patients. Finally, we did not find evidence for complicated relationships among the PANSS items in our sample. Our collective results suggest that identifying best treatment for a given individual may be grounded in subtle item combinations that transcend the long-trusted positive, negative, and cognitive categories.
精神分裂症患者的临床表现一直以来都被描述为非常多样化。在医学问卷中量化的行为表现可能可以直接推导出一致的症状特征。机器学习算法与国际多站点数据集（n=218 名患者）的结合，从广泛使用的 PANSS 问卷中确定了精神分裂症的独特模式。我们的聚类方法发现了一个阴性症状患者群体，以及一个中度和一个重度群体，进一步支持了精神分裂症亚型的存在。此外，新兴的回归分析揭示了最具临床预测性的问卷项目。PANSS 项目的小子集在单个患者中表现出令人信服的预测性能。这些项目子集包含了精神分裂症的整个症状谱，证实了不同方面的精神分裂症可以提高临床诊断和对患者的医疗效果。最后，我们在样本中没有发现 PANSS 项目之间存在复杂关系的证据。我们的综合结果表明，为特定个体确定最佳治疗方法可能基于超越长期以来被信任的阳性、阴性和认知类别细微的项目组合。
