3/5 Biomarkers/Biotypes, Course of Early Psychosis and Specialty Services (BICEPS)

项目摘要 越来越多的证据表明，在协调专业护理（CSC）服务中， 改善结果和生活。早期病程精神病（EP）的结局是异质性的，从早期的 完全恢复到从疾病开始的治疗抵抗和功能下降。这种异质性限制了 我们能够预测治疗计划所需的个体水平结果，并定制治疗类型、持续时间 和治疗干预的强度。生物标志物以及临床和人口统计学特征， 疾病可以预测结果，但单独来看，其预后价值有限。我们的双极-精神分裂症 中间表型网络（BSNIP）联盟最近开发、复制和验证了一种 生物标志物（EEG，眼动测试和神经认知）的分类（生物型1，2和3），在一个 特发性精神病（精神分裂症、情感障碍或双相情感障碍）病例的跨诊断样本 患有精神病的疾病），年龄范围为18-35岁。在这项研究中，我们将利用这种分类， 沿着临床和生物标志物数据，以预测第1、6和12次随访期间的疾病轨迹和结局 在5个B-SNIP研究中心的CSC诊所的320名EP患者中进行了为期3个月的研究。首先，我们将描述结果 在EP中的轨迹和生物型结构。我们现有的数据表明，EP中的生物型结构将相同 就像我们的大样本一样。其次，我们将调查九个生物因素和三个生物因素的预测价值， 我们预测EP人口 生物型结构 类似于在慢性精神病受试者中看到的，即，生物型1、2和3）（假设1）。生物型-3，和 Biotye-2例，将具有最佳结局（使用症状， 认知和功能测量）;生物型-1将具有CSC治疗的最差结果，在所有目标中 时间点（假设2）。值得注意的是，生物型1和生物型2病例将具有相同水平的认知功能 在基线。 通过B-SNIP识别的生物型用于症状和功能结局。将 表现出不同的结果临床轨迹（好，中等和差），并将有一个 最后，我们将研究临床（如诊断，病程， 药物滥用和治疗依从性）和生物标志物（包括神经影像学）特征， 模型，并将开发一个可行的生物标志物电池和预测算法，用于社区CSC 全国各地的网站。因此，我们将为现场提供一种预测CSC治疗中EP病例成功的方法 改善临床实践，提高现有治疗资源的有效利用。

PROJECT SUMMARY There is increasing evidence that early intervention for psychosis in coordinated specialty care (CSC) services improves outcomes and lives. The outcome of early course psychosis (EP) is heterogeneous, ranging from early full recovery to treatment resistance and functional decline from the onset of illness. This heterogeneity limits our ability to predict individual level outcomes needed for treatment planning and for tailoring the type, duration and intensity of therapeutic interventions. Biomarkers as well as clinical and demographic features, early in the illness can predict outcome, but taken individually, their prognostic value is limited. Our Bipolar- Schizophrenia Network for Intermediate Phenotypes (BSNIP) consortium has recently developed, replicated and validated a biomarker (EEG, eye movement testing, and neurocognition) based categorization (Biotypes 1, 2 and 3) in a trans-diagnostic sample of cases with idiopathic psychosis (schizophrenia, schizoaffective disorder, or bipolar disorder with psychosis), ranging from 18-35 years of age. In this study, we will leverage this categorization, along with clinical and biomarker data to predict illness trajectory and outcome during follow-up at 1, 6 and 12 months in 320 EP patients across CSC clinics at the five B-SNIP sites. First, we will characterize outcome trajectories and Biotype structure in EP. Our available data indicate the Biotype structure will be the same in EP as in our large sample. Second, we will investigate the predictive value of the nine bio-factors and the three We predict that the EP population Biotype structure similar to that seen in chronic psychosis subjects, i.e., Biotypes 1, 2 and 3) (hypothesis 1). Biotype-3, and Biotye-2 cases, will have the best outcomes (defined both categorically, and dimensionally, using symptomatic, cognitive and functional measures); Biotype-1 will have the worst outcomes to CSC treatment, across all target time points (hypothesis 2). Notably, Biotype-1 and Biotype-2 cases will have the same level of cognition function at baseline. Biotypes identified by B-SNIP for symptomatic and functional outcome. will manifest distinct outcome clinical trajectories (good, intermediate and poor) and will have a Finally, we will investigate the predictive value of clinical (such as diagnosis, illness duration, substance abuse, and treatment adherence), and biomarker (including neuroimaging) features in a multi-variate model and will develop a feasible biomarker battery and predictive algorithm for application in community CSC sites nation-wide. We will thus provide to the field a means for predicting success of EP cases in CSC treatment to improve clinical practice and to enhance efficient use of available treatment resources.