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

3/5 生物标志物/生物型，早期精神病课程和专业服务 (BICEPS)

• 批准号: 10683286
• 负责人: GODFREY D PEARLSON
• 金额: $ 32.18万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683286
• 关键词: Accounting; Age Years; Biological; Biological Markers; Bipolar Disorder; Boston; Categories; Chronic; Clinic; Clinical; Cognition; Cognitive; Communities; Coordinated Specialty Care; Data; Data Collection; Diagnosis; Diagnostic; Dimensions; Disease remission; Early Intervention; Electroencephalography; Enrollment; Eye Movements; Funding; Goals; Grant; Heterogeneity; Image; Impaired cognition; Impairment; Individual; Intervention; Measures; Modeling; Multivariate Analysis; Neurobiology; Neurocognition; Neurocognitive; Neurosciences Research; Onset of illness; Outcome; Participant; Patients; Phenotype; Population; Population Characteristics; Predictive Value; Procedures; Psychoses; Psychotic Disorders; Recovery; Resources; Sampling; Schizoaffective Disorders; Schizophrenia; Schizophreniform Disorder; Services; Site; Stimulus; Structure; Subgroup; Substance abuse problem; Testing; Therapeutic Intervention; Time; Variant; biomarker development; biomarker identification; biotypes; care outcomes; care systems; clinical practice; clinical predictors; cognitive function; community setting; cost; design; early detection biomarkers; early psychosis; early satiety; follow-up; functional decline; functional outcomes; imaging biomarker; improved; improved outcome; individual variation; medical specialties; meetings; neuroimaging; outcome prediction; pharmacologic; phenotypic biomarker; prediction algorithm; prognostic value; programs; psychosocial; psychotic; recruit; response; success; therapy resistant; treatment adherence; treatment planning; treatment program

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.

项目摘要 有越来越多的证据表明，在协调专业护理（CSC）服务中精神病的早期干预 改善结果和生活。早期精神病（EP）的结果是异质的，从早期到 从疾病发作开始完全恢复到治疗耐药性和功能下降。这种异质性限制 我们预测治疗计划所需的个人水平成果和调整类型的能力 治疗干预措施的强度。生物标志物以及临床和人口特征，早期 疾病可以预测结果，但是单独采取的，其预后价值是有限的。我们的双极精神分裂症 中间表型网络（BSNIP）联盟最近已经开发，复制和验证了 基于生物标志物（EEG，眼动测试和神经认知）基于A中的分类（生物型1、2和3） 特发性精神病病例的跨诊断样本（精神分裂症，精神分裂症或双极性 精神病障碍），年龄在18-35岁之间。在这项研究中，我们将利用这种分类， 以及临床和生物标志物数据，以预测1、6和12的随访期间的疾病轨迹和结果 在五个B-SNIP部位的CSC诊所的320名EP患者中，几个月。首先，我们将表征结果 EP中的轨迹和生物型结构。我们的可用数据表明生物型结构在EP中是相同的 就像我们的大样本中一样。其次，我们将研究九个生物因子和三个的预测值 我们预测EP人群 生物型结构 与在慢性精神病学科中看到的相似，即生物型1、2和3）（假设1）。 Biotype-3，和 Biotye-2病例将具有最佳的结果（在症状上，均和尺寸定义，使用症状， 认知和功能措施）；生物型1将在所有目标中都具有最差的CSC处理结果 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 基线。 B-SNIP鉴定出有症状和功能结果的生物型。将要 明显明显的结果临床轨迹（良好，中间和差），将有一个 最后，我们将研究临床的预测价值（例如诊断，疾病持续时间， 多变量的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 模型并将开发可行的生物标志物电池和预测算法，以应用于社区CSC 在全国范围内。因此，我们将向该领域提供一种预测EP病例在CSC处理中成功的手段 改善临床实践并提高有效利用可用的治疗资源。