Identification of psychopathological course types in schizophrenic psychoses

精神分裂症是一种异质性疾病。ICD-10和DSM-IV对精神分裂症的现代诊断标准未能在精神病理过程和神经生物学结果方面识别同质性组。然而，对病程和结果进行细分可能会改善精神分裂症的个体化治疗。此外，精神病理亚型的识别对于神经生物学研究是重要的，例如，对于基因-表型相关性。到目前为止，已经进行了几次尝试，以建立精神分裂症精神病的课程类型学。然而，这些传统的课程类型仅仅基于临床判断，没有得到系统的实证研究的证实。在此背景下，该项目旨在识别和表征精神分裂症障碍纵向过程中的精神病理表型。因此，将建立150名精神分裂症障碍患者的队列，并对其进行为期2年的纵向评估。将使用标准化的评级工具(在两年内进行5次访问)进行精神病理学表征。此外，我们将为每个患者创建一份叙述性的病例报告，该报告应反映疾病的精神病理过程。基于这个数据库，我们将对精神病理评定量表进行统计分析，如潜在班级增长分析(LCGA)或因素混合分析(FMA)，以确定经验表型。虽然LCGA对应于定性上不同的亚型，但FMA允许在一个统计模型中执行分类和维度数据分析，以便识别亚型和亚型内精神病理症状的严重差异。精神病理学病例报告可以帮助评估统计分析结果的临床可信度。此外，精神分裂症障碍的传统课程类型将被应用于精神病理病例记录。此外，我们将尝试使用适当的评级标准来实施传统的课程类型(例如，莱昂哈德和瓦特及其同事的课程类型)。在这个数据库的基础上，我们将检查这些概念的可靠性和有效性。统计分析的结果将与传统的课程类型学进行比较，以检验其一致性和差异性。最后，我们将与临床研究小组241(精神病纵向病程的基因-表型关系和神经生物学)合作，尝试将已识别的精神病理表型与基因数据相关联。

Schizophrenia is a heterogeneous disorder. Modern diagnostic criteria for schizophrenia in ICD-10 and DSM-IV fail to identify homogeneous groups with respect to psychopathological course and neurobiological findings. A subdivision with respect to course and outcome, however, could improve the individual treatment of schizophrenia. Furthermore, the identification of psychopathological subtypes is important for neurobiological research, e.g. for genotype-phenotype correlations. As yet, several attempts have been made in order to establish a course typology of schizophrenic psychoses. These traditional course typologies, however, are based merely upon clinical judgment without a confirmation by systematic empirical research. Against this background, the project aims to identify and to characterize psychopathological phenotypes in the longitudinal course of schizophrenic disorders. Therefore, a cohort of 150 patients suffering from schizophrenic disorders with a longitudinal assessment over a period of 2 years will be established. The psychopathological characterization will be performed using standardized rating instruments (5 visits over a period of 2 years). In addition, we will create a narrative case report of each patient which should reflect the psychopathological course of the illness. Based upon this database we will perform statistical analyses like latent class growth analysis (LCGA) or factor-mixture analysis (FMA) on psychopathological rating scales in order to identify empirical phenotypes. While LCGA corresponds to qualitatively distinct subtypes, FMA allows performing categorical and dimensional data analyses in one statistical model in order to identify both subtypes and severity differences of psychopathological symptoms within subtypes. Psychopathological case reports can help to evaluate the clinical plausibility of the results of statistical analyses. Furthermore, traditional course typologies for schizophrenic disorders will be applied to the psychopathological case records. Moreover, we will try to operationalize traditional course typologies (e.g. those of Leonhard and Watt and coworkers) using suitable rating scales. Based on this database we will examine the reliability and the validity of these concepts. The results of the statistical analyses will be compared with traditional course typologies in order to examine accordance and differences. Lastly, in cooperation with Clinical Research Group 241 (Genotype-phenotype relationships and neurobiology of the longitudinal course of psychosis) we will try to correlate the identified psychopathological phenotypes with genotype data.