1/2: B-SNIP: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT)

1/2：B-SNIP：高效治疗处方的算法诊断 (ADEPT)

基本信息

批准号：
10298707
负责人：
BRETT A CLEMENTZ
金额：
$ 30.2万
依托单位：
UNIVERSITY OF GEORGIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10298707
关键词：
Age Algorithms Bacterial Artificial Chromosomes Biological Biological Markers Bipolar Disorder Chicago Classification Clinical Clinical Data Clinical Treatment Clozapine Cognition Cognitive Collaborations Complex Data Databases Diagnosis Diagnostic Dimensions Discrimination Disease Electrophysiology (science)Environment Etiology Evaluation Hylobates Genus Individual Intervention Interview Investigation Laboratories Measures Medical Medicine Modeling Modification Nature Neurobiology Outcome Patients Phenotype Probability Procedures Psychoses Resources Saccades Sampling Schizoaffective Disorders Schizophrenia Sensory Signal Transduction Standardization Stimulus Syndrome Testing Time Translations Universities Work analytical tool base case-based clinical Diagnosis clinical application cognitive testing improved individualized medicine laboratory equipment novel patient subsets phenomenological models preservation relating to nervous system research clinical testing response therapy development validation studies

项目摘要

Clinical phenomenology alone neither (i) captures biologically based disease entities, nor (ii) allows for individualized treatment prescriptions based on neurobiology. The B-SNIP consortium showed and replicated that schizophrenia, schizoaffective, and bipolar disorder with psychosis lack neurobiological distinctiveness. B- SNIP transitioned to subgrouping psychosis cases based on biomarker homology. We produced and replicated biologically homologous psychosis Biotypes (BT1, BT2, BT3) that may assist treatment targeting for psychosis. This twelve-month project will develop a time and resource efficient algorithm for deriving B-SNIP Biotypes that can be implemented in even under-resourced environments. Like in laboratory medicine, the procedure (ADEPT) will be stepwise (clinical evaluation, then cognition, then electrophysiology) to yield Biotypes for which specific treatments can be either implemented (established interventions) or evaluated (novel treatment development). Aim 1: B-SNIP Biotypes currently require specialized equipment for laboratory testing, and multiple tests with statistical integration across multiple scores. Instead, we will determine the best individual measures that yield the most efficient and highest probability Biotype memberships. ADEPT will be adaptive both within (clinical, cognitive, electrophysiological) and across the domains (clinical features inform selection of cognitive tests which inform selection of electrophysiological tests). At each stage, ADEPT will produce a Biotype classification and confidence. This will allow for Biotype determination in a proportion of cases even when laboratory testing resources are limited. Aim 2: The first contact in medical evaluation involves clinical characterization. Clinical features alone will yield Biotype discriminations sufficient for treatment targeting in a small but significant subset of patients (»15%, mostly BT3). Aim 3: Cognition tests are the least technically demanding laboratory assessments, and are powerful discriminators of Biotypes. B-SNIP uses BACS, Stop Signal (SST), and antisaccades to assess cognition. Addition of cognition to clinical features will yield »80% accuracy for identifying BT3s and »40% of all cases (mostly BT2, although BT1 and BT2 are difficulty to differentiate without electrophysiology). Patients will receive different cognitive tests based on the adaptive algorithm (e.g., SST may be superior for Biotype determination in some cases). The adaptive approach preserves classification precision while reducing clinician and patient burden. Aim 4: The most important Biotype differentiating electrophysiology features are low neural response to salient stimuli (BT1) and exuberant nonspecific neural activity (BT2). We used multiple complex electrophysiology measures, but we will identify tests and measures that yield the most efficient Biotype differentiation. Addition of electrophysiology to clinical and cognition information will yield 90-95% accuracy for identifying Biotypes for all cases. Again, for a given patient, we will adaptively select the specific electrophysiological measures to maximize classification accuracy for that patient (e.g., P300 may be superior for Biotype determination in some cases).

单独的临床现象学（i）捕获基于生物学的疾病实体，而（ii）允许基于神经生物学的个性化治疗处方。显示并复制的B-SNIP联盟精神分裂症，精神分裂性和躁郁症患有精神病缺乏神经生物学的独特性。 b- 剪切过渡到基于生物标志物同源性的精神病病例。我们生产并复制了生物学上同源性精神病生物型（BT1，BT2，BT3）可能有助于针对精神病的治疗。这个十二个月的项目将开发一种时间和资源有效算法，用于推导B-SNIP生物型甚至可以在资源不足的环境中实现。像实验室医学一样（熟练）将逐步（临床评估，然后认知，然后是电生理学），以产生生物型可以实施哪种特定治疗方法（已建立的干预措施）或评估（新的治疗方法发展）。目标1：B-SNIP生物型当前需要专业设备进行实验室测试，并且多个测试的多个测试跨多个分数。相反，我们将确定最好的个人产生最有效和最高概率生物型成员资格的度量。熟练会是自适应的内部（临床，认知，电生理）和整个领域（临床特征信息选择）认知测试的选择，这些测试的选择）。在每个阶段，熟练都会产生一个生物型分类和信心。这将允许在一定比例的情况下确定生物型当实验室测试资源受到限制时。目标2：医学评估中的第一次接触涉及临床表征。仅临床特征就会产生足以用于治疗靶向靶向的生物型区分小但显着的患者子集（»15％，主要是BT3）。 AIM 3：认知测试在技术上是最少的苛刻的实验室评估，是生物型的强大歧视者。 B-SNIP使用BAC，停止信号（SST）和评估认知的反扫视。在临床特征中添加认知将产生»80％识别BT3和所有情况的40％的精度（尽管BT1和BT2很难在没有电生理学的情况下进行区分）。患者将根据适应性接受不同的认知测试算法（例如，在某些情况下，SST对于生物型的测定可能是优越的）。适应性方法保留分类精度，同时减少临床和患者伯恩。目标4：最重要的生物型区分电生理特征是对显着刺激（BT1）的神经元反应低，并且旺盛的非特异性神经活动（BT2）。我们使用了多个复杂的电生理测量指标，但是我们将确定产生最有效的生物型分化的测试和度量。将电生理学添加到临床和认知信息将产生90-95％的精度，以鉴定所有情况的生物型。再次，给定患者，我们将自适应选择特定的电生理测量以最大化分类该患者的准确性（例如，在某些情况下，P300对于生物型的测定可能优异）。