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

生物标志物/生物型、早期精神病课程和专业服务 (BICEPS)

• 批准号: 10683233
• 负责人: MATCHERI S. KESHAVAN
• 金额: $ 25.91万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 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/10683233
• 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 Biotypes identified by B-SNIP for symptomatic and functional outcome. We predict that the EP population will manifest distinct outcome clinical trajectories (good, intermediate and poor) and will have a 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. 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.

项目概要 越来越多的证据表明，在协调专科护理中对精神病进行早期干预 (CSC) 服务改善结果和生活。早期精神病（EP）的结果是 异质性，从早期完全恢复到治疗抵抗和功能衰退 从发病之日起。这种异质性限制了我们预测个体水平结果的能力 治疗计划和调整治疗类型、持续时间和强度所需的 干预措施。疾病早期的生物标志物以及临床和人口特征可以 预测结果，但单独来看，其预后价值有限。我们的双相- 精神分裂症中间表型网络 (BSNIP) 联盟最近开发了， 复制并验证了基于生物标记（脑电图、眼动测试和神经认知）的 特发性病例跨诊断样本中的分类（生物型 1、2 和 3） 精神病（精神分裂症、分裂情感性障碍或双相情感障碍伴精神病），范围 18-35岁。在本研究中，我们将利用这种分类以及临床和 生物标志物数据可预测 1、6 和 12 个月随访期间的疾病轨迹和结果 五个 B-SNIP 站点的 CSC 诊所的 320 名 EP 患者。首先，我们将描述结果 EP 中的轨迹和生物型结构。我们现有的数据表明生物型结构将是 EP 中的情况与我们的大样本中的情况相同。其次，我们将研究九个因素的预测价值。 生物因子和 B-SNIP 识别的三种生物型用于症状和功能结果。 我们预测 EP 人群将表现出不同的结果临床轨迹（良好、 中等和较差）并且将具有类似于慢性精神病中所见的生物型结构 受试者，即生物型 1、2 和 3）（假设 1）。 Biotype-3 和 Biotye-2 病例将具有 最佳结果（使用症状、认知和维度进行分类和维度定义） 功能措施）；在所有目标中，Biotype-1 的 CSC 治疗结果将是最差的 时间点（假设2）。值得注意的是，Biotype-1 和 Biotype-2 病例的感染水平相同 基线认知功能。最后，我们将研究临床的预测价值（例如 诊断、病程、药物滥用和治疗依从性）和生物标志物（包括 神经成像）以多变量模型为特征，并将开发可行的生物标记电池和 适用于全国社区 CSC 站点的预测算法。因此，我们将提供给 该领域是预测 CSC 治疗中 EP 病例成功的一种手段，以改善临床实践 并加强现有治疗资源的有效利用。