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: 10685407
• 负责人: Scott Holmes
• 金额: $ 53.96万
• 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/10685407
• 关键词: Absence of pain sensation; Adolescent; Adult; Age; Articulation; Behavioral; Brain; Brain Concussion; Brain region; COVID-19; Central Nervous System; Characteristics; Child; Childhood; Chronic; Chronic Daily Headaches; Chronic Headaches; Classification; Clinical; Complex; Data; Data Set; Dependence; Development; Diagnosis; Disease; Drug Addiction; Elements; Etiology; Female; Frequencies; Future; Genetic; Growth; Headache; Headache Disorders; Health system; Healthcare; Intervention; Investigation; Learning; Long COVID; Machine Learning; Malingering; Measures; Mental Health; Migraine; Muslim religion; 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; Resources; Severities; Source; Specificity; Symptoms; System; Techniques; Testing; Time; Work; Youth; 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; text searching; 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成年并驱动毒品 成瘾，心理健康问题和自杀行为。儿童报告的疼痛程度 谁经历头痛是巨大的，并且基于降低的能力估计很差 儿童表达症状，潜在的恶意或疼痛症状不足，以及 身体生长和大脑发育的广泛差异。头痛是经常报道的 并且在小儿受试者中对原发性和继发性疾病的理解不足。多元化 头痛的呈现强调了明显的头痛机制的潜力 演讲，因此重点放在量身定制的治疗方案上。显然需要更好 根据临床表现和潜在的疼痛生理学来定义童年的头痛。 我们的假设是，每个临床头痛障碍都可以通过独特的生物行为来定义 特征。在拟议的研究计划中，我们（目标1）评估临床和行为 每种头痛障碍和疼痛调节的元素，（目标2） 治疗难治性头痛，（目标3）开发机器学习分类器以了解 区分头痛亚型和治疗耐药性的特征。这项研究可能会 产生高度相关的信息，这些信息将有助于识别和治疗头痛 儿童的疾病，确定头痛和疼痛处理的独特特征，以及（3） 概述不同头痛障碍的生物行为目标。这项调查的数据可能是 为小儿疼痛障碍的治疗做出很大的贡献。