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).

单独的临床现象学既不能（i）捕获基于生物学的疾病实体，也不能（ii）考虑 B-SNIP 联盟展示并复制了基于神经生物学的个体化治疗处方。精神分裂症、情感分裂和伴有精神病的双相情感障碍缺乏神经生物学特征。我们制作并复制了 SNIP 并根据生物标志物同源性对精神病病例进行分组。生物学同源精神病生物型（BT1、BT2、BT3）可能有助于针对精神病的治疗。这个为期 12 个月的项目将开发一种时间和资源高效的算法，用于派生 B-SNIP 生物型，甚至可以在资源贫乏的环境中实施，就像实验室医学一样。（ADEPT）将逐步（临床评估，然后认知，然后电生理学）产生生物型哪些具体治疗可以实施（既定干预措施）或评估（新治疗目标 1：B-SNIP Biotypes 目前需要专门的设备进行实验室测试，并且相反，我们将通过多个分数的统计整合来确定最佳个体。产生最有效和最高概率的生物型成员资格的措施将是适应性的。无论是在领域内（临床、认知、电生理学）还是跨领域（临床特征告知选择）认知测试，为电生理测试的选择提供信息）在每个阶段，ADEPT 都会产生一个生物型分类和置信度甚至可以在一定比例的病例中进行生物型确定。当实验室检测资源有限时目标2：医学评估首先涉及临床。仅临床特征就足以产生足以用于治疗目标的生物型区分。少数但重要的患者子集（»15%，主要是 BT3）。目标 3：认知测试技术含量最低。严格的实验室评估，并且是 B-SNIP 使用 BACS、Stop 的强大鉴别器。信号 (SST) 和反扫视来评估认知，将认知添加到临床特征将产生 »80%。识别 BT3 的准确性和所有案例的 40%（主要是 BT2，尽管 BT1 和 BT2 很难识别）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 可能更适合生物型确定）。保持分类精度，同时减轻临床医生和患者的负担。目标 4：最重要的。生物型区分电生理学特征是对显着刺激（BT1）的低神经反应和旺盛的非特异性神经活动（BT2）。我们使用了多种复杂的电生理学测量，但我们会的。确定产生最有效的生物型分化的测试和措施。临床和认知信息将产生 90-95% 的准确度来识别所有病例的生物型。对于给定的患者，我们将自适应地选择特定的电生理措施以最大化分类该患者的准确性（例如，在某些情况下，P300 可能更适合生物型测定）。