Pediatric Chronic Headache and COVID-19: Use of Machine Learning and Biobehavioral Analysis to Classify Headache Mechanism and Optimize Treatment Course.

小儿慢性头痛和 COVID-19：利用机器学习和生物行为分析对头痛机制进行分类并优化治疗方案。

• 批准号: 10521562
• 负责人: Scott Holmes
• 金额: $ 53.74万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-18 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10521562
• 关键词: Absence of pain sensation; Adolescent; Adult; Age; Behavioral; Brain; Brain Concussion; Brain region; COVID-19; Characteristics; Child; Childhood; Chronic; Chronic Daily Headaches; Chronic Headaches; Classification; Clinical; Complex; Crystallization; Data; Data Set; Dependence; Development; Diagnosis; Disease; Drug Addiction; Elements; Etiology; Female; Frequencies; Functional Magnetic Resonance Imaging; Future; Genetic; Growth; Headache; Headache Disorders; Health system; Healthcare; Intervention; Investigation; Learning; Literature; Machine Learning; Malingering; Measures; Mental Health; Migraine; Muslim religion; Neuraxis; Pain; Pain Disorder; Pain management; Participant; Patients; Phenotype; Physiological; Physiology; Post-Traumatic Headaches; Predisposing Factor; Prevalence; Problem behavior; PubMed; Refractory; Rehabilitation Centers; Reporting; Research; Resistance; Resolution; Resort; Resources; Severities; Source; Specificity; Symptoms; System; Techniques; Testing; Time; United States National Library of Medicine; Work; Youth; base; biobehavior; biopsychosocial; central pain; chronic pain; chronic painful condition; clinical care; clinically relevant; cohort; deep learning; experience; individualized medicine; machine learning classifier; male; medical specialties; midbrain central gray substance; morphogens; neuroimaging; pain processing; pain rehabilitation; pain symptom; programs; response; sex; suicidal behavior; therapy resistant; treatment optimization; treatment services

Chronic pain experienced by children has the potential to persist into adulthood and drive drug addiction, mental health problems and suicidal behavior. The level of pain reported by children who experience headache is immense and likely poorly estimated based on lowered ability of children to articulate symptoms, potential malingering or underreporting of pain symptoms, and extensive variability in physical growth and brain development. Headache is a frequently reported and poorly understood primary and secondary disorder in pediatric subjects. The diverse presentation of headache underscores the potential for distinct mechanisms of headache presentation, thus placing emphasis on tailored treatment options. There is clear need to better define childhood headache in terms of the clinical presentation and underlying pain physiology. Our hypothesis is that each clinical headache disorder can be defined by unique biobehavioral characteristics. In the proposed research program, we (Aim 1) evaluate the clinical and behavioral elements of each headache disorder and pain modulation, (Aim 2) the biobehavioral signature of treatment refractory headache, and (Aim 3) develop machine learning classifiers to understand the features that differentiate headache subtype and treatment resistance. This study is likely to yield highly relevant information that will contribute towards identifying and treating headache disorders in children, identifying unique characteristics of headache and pain processing, and (3) outline biobehavioral targets for different headache disorders. Data from this investigation is likely to contribute greatly towards the treatment of pediatric pain disorders.

儿童经历的慢性疼痛有可能持续到成年， 成瘾、心理健康问题和自杀行为。儿童报告的疼痛程度 经历头痛的人是巨大的，可能是基于降低的能力而估计不足。 儿童阐明症状，潜在的装病或少报疼痛症状，和 身体发育和大脑发育的广泛差异。头痛是一种常见的 对儿科受试者的原发性和继发性疾病知之甚少。多样化 头痛的表现强调了头痛不同机制的可能性 介绍，从而强调量身定制的治疗方案。显然需要更好地 根据临床表现和潜在的疼痛生理学来定义儿童头痛。 我们的假设是，每一种临床头痛障碍都可以通过独特的生物行为来定义。 特色在拟议的研究计划中，我们（目的1）评估临床和行为 每种头痛疾病和疼痛调节的要素，（目标2） 治疗难治性头痛，以及（目标3）开发机器学习分类器，以了解 区分头痛亚型和治疗抵抗的特征。这项研究可能会 产生高度相关的信息，有助于识别和治疗头痛 儿童疾病，确定头痛和疼痛处理的独特特征，以及（3） 概述了不同头痛疾病的生物行为目标。从这次调查的数据来看 为儿科疼痛疾病的治疗做出巨大贡献。