Identifying opioid response phenotypes in low back pain electronic health data

疼痛是成人门诊和急诊就诊的主要原因，影响100多个 每年花费超过6000亿美元。下背痛（LBP）占28% 在美国和世界范围内，这是医疗保健问题，也是残疾的主要原因。阿片 是美国最常见的处方药类别，这些处方中的大多数 对于LBP。尽管阿片类药物治疗广泛应用于LBP，但 阿片类药物治疗的疼痛缓解经验尚未确定，使提供者没有明确的 指导安全有效的治疗。鉴于疾病和保健利用的巨大负担， 关于LBP的临床信息广泛地填充了电子健康记录（EHR），提供了有价值的 数据源然而，这些信息目前除了个别患者的经验外几乎没有什么意义 因为来自EHR的大多数疼痛相关数据都嵌入在自由文本中。使用EHR数据可以 为更好地了解LBP提供了重要的桥梁。因此，这一提议的核心假设是 将临床经验转化为离散和可分析的数据，特别是模拟阿片类药物反应， LBP患者的表型，将确定临床相关的表型治疗反应。为了验证这一 假设，这个指导职业发展项目将适应和应用自然语言处理 (NLP)，数据标准化，挖掘和分析工具，以专门模拟阿片类药物反应表型， LBP患者，以表征疼痛强度，功能状态和疼痛干扰活动。 通过整合目标，该提案将：1）支持LBP和阿片类药物笔记语料库的注释， 与疼痛强度、功能状态和疼痛干扰相关的临床概念的映射， 活动; 2）使用NLP识别LBP患者的相关阿片类反应表型并将其与之关联 在EHR中;和3）表征与阿片剂量递增相关的LBP表型。临床NLP使用 统计建模，以提取和转换高维临床数据，当与 PI的领域知识，创造了一个独特的机会，了解LBP管理，结果， 疗效最终，这一基础可用于预测临床结果和对 治疗干预。我们的长期目标是超越确定疾病表型特征， 创建一个系统来识别治疗反应表型。根据疼痛强度对患者进行分层， 功能状态，疼痛干扰和其他因素，我们计划确定潜在的队列， 从遗传学的角度进一步研究。这种指导的职业发展补助金（K 08）将支持临床 专家以系统的方法调整工具和培训，使其朝着计划路线发展 研究是令人难以置信的响应NIH疼痛研究议程，并可以过渡到独立的 R 01级融资。

Pain is the leading reason for adult outpatient and emergency department medical visits, impacting over 100 million Americans at a cost of over $600 billion dollars annually. Low back pain (LBP) represents 28% of this health-care problem and is the leading cause of disability, both in the United States and worldwide. Opioids are the most commonly prescribed drug class in the United States, and the majority of these prescriptions are for LBP. Despite the broad application of opioid therapy in LBP, the phenotypes of individuals who experience pain relief from opioid treatment have not been identified, leaving providers without clear guidance for safe and effective therapy. Given this staggering burden of disease and health-care utilization, clinical information regarding LBP widely populates the electronic health record (EHR), providing a valuable data source. However, this information presently has little meaning beyond the individual patient experience because the majority of pain-related data from the EHR is embedded in free text. Using EHR data may provide the crucial bridge to a better understanding of LBP. Thus, the central hypothesis of this proposal is that translating clinical experiences into discrete and analyzable data, specifically modeling opioid response phenotypes for patients with LBP, will identify clinically relevant phenotypic treatment responses. To test this hypothesis, this mentored career development project will adapt and apply natural language processing (NLP), data standardization, mining, and analysis tools to specifically model opioid response phenotypes for patients with LBP to characterize pain intensity, functional status, and pain interference with activity. Through integrated aims, this proposal will, 1) support the annotation of LBP and opioid note corpus, and the mapping of clinical concepts related to pain intensity, functional status, and pain interference with activities; 2) use NLP to identify and relate relevant opioid response phenotypes in patients with LBP in the EHR; and 3) characterize LBP phenotypes associated with opioid dose escalation. Clinical NLP uses statistical modeling to extract and transform high dimensional clinical data, which, when developed with the PI’s domain knowledge, creates a unique opportunity to understand LBP management, outcomes, and therapeutic efficacy. Ultimately this foundation may be used to predict clinical outcomes and responses to therapeutic interventions. Our long-term goal is to move beyond identifying disease phenotype profiles to create a system to identify treatment response phenotypes. Stratifying patients based on pain intensity, functional status, pain interference and other factors, we plan to identify potential cohorts that warrant further study from a genetic focus. This mentored career development grant (K08) will support a clinical expert’s adaptation of tools and training in a systematic method to allow growth toward a programmatic line of research that is incredibly responsive to the NIH pain research agenda and can transition to independent R01-level funding.