2/2 B-SNIP: Algorithmic Diagnostics for Efficient Prescription of Treatments (ADEPT) - Resubmission - 1
2/2 B-SNIP:高效治疗处方的算法诊断 (ADEPT) - 重新提交 - 1
基本信息
- 批准号:10299189
- 负责人:
- 金额:$ 32.8万
- 依托单位:
- 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:
- 关键词:AgeAlgorithmsBacterial Artificial ChromosomesBiologicalBiological MarkersBipolar DisorderChicagoClassificationClinicalClinical DataClinical TreatmentClozapineCognitionCognitiveCollaborationsComplexDataDatabasesDiagnosisDiagnosticDimensionsDiscriminationDiseaseElectrophysiology (science)EnvironmentEtiologyEvaluationHylobates GenusIndividualInterventionInterviewInvestigationLaboratoriesMeasuresMedicalMedicineModelingModificationNatureNeurobiologyOutcomePatientsPhenotypeProbabilityProceduresPsychosesResourcesSaccadesSamplingSchizoaffective DisordersSchizophreniaSensorySignal TransductionStandardizationStimulusSyndromeTestingTimeTranslationsUniversitiesWorkanalytical toolbasecase-basedclinical Diagnosisclinical applicationcognitive testingimprovedindividualized medicinelaboratory equipmentnovelpatient subsetsphenomenological modelspreservationrelating to nervous systemresearch clinical testingresponsetherapy developmentvalidation 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)允许
基于神经生物学的个体化治疗处方。B-SNIP联盟显示并复制了
精神分裂症、分裂情感性精神病和双相情感障碍伴精神病缺乏神经生物学特征。B-
SNIP过渡到基于生物标志物同源性的精神病病例亚组。我们生产并复制了
生物同源性精神病生物型(BT 1,BT 2,BT3)可能有助于治疗精神病。
这个为期12个月的项目将开发一种时间和资源有效的算法,用于获得B-SNIP生物型,
甚至可以在资源不足的环境中实施。与实验室医学一样,该程序(ADEPT)
将逐步(临床评价,然后认知,然后电生理学)产生生物型,
治疗可以实施(已建立的干预措施)或评估(新的治疗开发)。
目标1:B-SNIP生物型目前需要专门的设备进行实验室测试,
多个分数的统计整合。相反,我们将确定最佳的个别措施,
最有效和最高概率的生物型成员资格。ADEPT将在(临床,
认知,电生理)和跨域(临床特征告知认知测试的选择,
通知电生理测试选择)。在每个阶段,ADEPT将产生生物型分类,
信心这将允许在一定比例的情况下进行生物型确定,即使在实验室测试时,
资源是有限的。目的2:医学评价中的首次接触涉及临床表征。临床
单独的特征将产生足以在小但重要的子集中进行治疗靶向的生物型区分
的患者(约15%,主要为BT3)。目标3:认知测试是技术要求最低的实验室
评估,是生物型的强有力的鉴别器。B-SNIP使用BACS、停止信号(SST)和
以评估认知能力。将认知添加到临床特征中将产生约80%的识别准确率
BT3和BT 2占所有病例的40%(大多数是BT 2,尽管BT 1和BT 2难以区分,
电生理学)。患者将接受基于自适应算法的不同认知测试(例如,SST可能
在某些情况下对于生物型测定是上级的)。自适应方法保持分类精度
同时减轻临床医生和患者的负担。目的4:最重要的生物型区分电生理学
特征是对显著刺激的低神经反应(BT 1)和旺盛的非特异性神经活动(BT 2)。我们
使用了多种复杂的电生理学措施,但我们将确定产生最多的测试和措施
高效的生物型分化。在临床和认知信息中加入电生理学将产生90-
95%的准确率为所有情况下确定生物型。同样,对于给定的患者,我们将自适应地选择特定的
电生理测量以最大化该患者的分类准确度(例如,P300可能更优上级
在某些情况下用于生物型测定)。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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ROBERT D GIBBONS其他文献
ROBERT D GIBBONS的其他文献
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{{ truncateString('ROBERT D GIBBONS', 18)}}的其他基金
Adaptive Testing of Cognitive Function based on multi-dimensionalItem Response Theory
基于多维项目反应理论的认知功能自适应测试
- 批准号:
10900990 - 财政年份:2023
- 资助金额:
$ 32.8万 - 项目类别:
A New Statistical Paradigm for Measuring Psychopathology Dimensions in Youth
测量青少年精神病理学维度的新统计范式
- 批准号:
8666668 - 财政年份:2013
- 资助金额:
$ 32.8万 - 项目类别:
A New Statistical Paradigm for Measuring Psychopathology Dimensions in Youth
测量青少年精神病理学维度的新统计范式
- 批准号:
8733940 - 财政年份:2013
- 资助金额:
$ 32.8万 - 项目类别:
A New Statistical Paradigm for Measuring Psychopathology Dimensions in Youth
测量青少年精神病理学维度的新统计范式
- 批准号:
9254603 - 财政年份:2013
- 资助金额:
$ 32.8万 - 项目类别:
A New Statistical Paradigm for Measuring Psychopathology Dimensions in Youth
测量青少年精神病理学维度的新统计范式
- 批准号:
8482531 - 财政年份:2013
- 资助金额:
$ 32.8万 - 项目类别:
Antidepressant Treatment and Suicidality: Biostatistical/Methodological Solutions
抗抑郁治疗和自杀:生物统计/方法学解决方案
- 批准号:
8089395 - 财政年份:2008
- 资助金额:
$ 32.8万 - 项目类别:
Antidepressant Treatment and Suicidality: Biostatistical/Methodological Solutions
抗抑郁治疗和自杀:生物统计/方法学解决方案
- 批准号:
8246644 - 财政年份:2008
- 资助金额:
$ 32.8万 - 项目类别:
Antidepressant Treatment and Suicidality: Biostatistical/Methodological Solutions
抗抑郁治疗和自杀:生物统计/方法学解决方案
- 批准号:
7691764 - 财政年份:2008
- 资助金额:
$ 32.8万 - 项目类别:
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