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

项目总结 越来越多的证据表明，在协调专科护理(CSC)服务中对精神病进行早期干预 改善结果和生活。早期精神病(EP)的结果是不同的，从早期到 完全恢复到治疗抵抗和发病后的功能衰退。这种异质性限制了 我们预测治疗计划和定制类型、持续时间所需的个体水平结果的能力 以及治疗干预的强度。生物标志物以及临床和人口统计学特征，在早期 疾病可以预测结果，但单独来看，它们的预后价值是有限的。我们的躁郁症精神分裂症 中间表型网络(BSNIP)联盟最近开发、复制和验证了 基于生物标记物(脑电、眼动测试和神经认知)的分类(生物型1、2和3) 特发性精神病(精神分裂症、分裂情感障碍或双相情感障碍)患者的跨诊断样本 精神障碍)，年龄从18-35岁不等。在这项研究中，我们将利用这种分类， 以及临床和生物标记物数据，以预测1、6和12个月的随访期间的疾病轨迹和结果 在5个B-SNIP站点的CSC诊所对320名EP患者进行了3个月的检查。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中的生物型结构将是相同的 就像我们的大样本一样。其次，我们将考察九个生物因素和三个因素的预测价值。 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，以及 Biotye-2病例将具有最好的结果(明确地和维度地定义，使用症状， 认知和功能测量)；在所有目标中，生物型1将产生最差的CSC治疗结果 时间点(假设2)。值得注意的是，生物型1和生物型2的病例具有相同水平的认知功能 在基线上。 由B-SNIP确定的症状和功能结果的生物型。将要 显示不同的结果临床轨迹(良好、中等和较差)，并将有 最后，我们将探讨临床(如诊断、病程、 药物滥用和治疗依从性)和生物标记物(包括神经成像)在多变量中的特征 模型，并将开发一种可行的生物标志物电池和预测算法，用于社区社区卫生服务中心 全国范围内的网站。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.