Identifying genetic predictors of outcomes for Veterans with chronic low back pain and lumbosacral spinal disorders

确定患有慢性腰痛和腰骶脊柱疾病的退伍军人结果的遗传预测因素

• 批准号: 10641238
• 负责人: Pradeep Suri
• 金额: --
• 依托单位: VA PUGET SOUND HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10641238
• 关键词: Area; Biological; Caring; Characteristics; Chronic; Chronic low back pain; Clinical; Clinical Data; Compensation; Coronary Arteriosclerosis; Data; Development; Diagnosis; Discrimination; Electronic Health Record; Electronic Medical Records and Genomics Network; Failure; Future; Genetic Risk; Genomics; Health; Healthcare; Individual; Link; Low Back Pain; Meta-Analysis; Modeling; Neuropathy; Operative Surgical Procedures; Outcome; Pain management; Participant; Patient-Focused Outcomes; Patients; Pharmacy facility; Phase; Physical therapy; Prognosis; ROC Curve; Recurrence; Rehabilitation therapy; Research; Resources; Risk; Role; Sampling; Signs and Symptoms; Spinal Diseases; Spinal Manipulation; Spinal Stenosis; Subgroup; Surgical Decompression; Syndrome; Treatment outcome; Validation; Variant; Vertebral column; Veterans; Visit; biobank; care outcomes; disability; electronic health information; genetic predictors; genome wide association study; genome-wide; genomic data; high risk; improved; individualized medicine; military veteran; model development; outcome prediction; patient subsets; personalized approach; polygenic risk score; predictive modeling; prognostic model; prognostic value; programs; progression risk; rare mendelian disorder; risk stratification; years lived with disability

Low back pain (LBP) is the #1 contributor to disability globally and the 4th most prevalent reason for new VA disability compensation. The societal burden of LBP is largely attributed to 2 distinct subgroups of patients: (1) those who use healthcare resources for chronic (persistent or recurrent) LBP; and (2) those undergoing surgical treatments for specific spine-related conditions associated with LBP and/or neuropathic symptoms/signs, such as lumbosacral radicular syndrome (LSRS) and symptomatic lumbar spinal stenosis (SLSS). Personalized approaches to improve the efficiency of care and treatment outcomes for these subgroups of Veterans have the potential to reduce the burden of LBP for the Veteran population. Stratified care for LBP based on prognosis showed early promise when linked to clinical decisions regarding physical therapy. More robust effects from stratified care may come through improving the feasibility and prognostic ability of risk stratification or linking risk stratification to clinical decisions regarding treatments with large magnitude effects in subgroups of patients with LBP (e.g., decompression surgery for LSRS). The proposed research will apply these two approaches to improving stratified care for LBP, which will develop and validate powerful prediction models using clinical electronic health record (EHR) and genomic data. This research will two parts to achieve each of the two study aims. Part I will involve genome-wide association study (GWAS) meta-analyses to predict outcomes for LBP-associated conditions, including participants from the Million Veteran Program (MVP), the Electronic Medical Records and Genomics Network phase 3 (eMERGE3) network, and the UK Biobank, as well as summary data from other genomic biobanks. Part II will involve the development and validation of multivariable prognostic models for LBP-related outcomes. First, multivariable prognostic models will be developed using a cross-validation approach in 80% of the MVP sample, using only clinical data (visits, diagnoses, pharmacy, vital signs, etc.) from the VA EHR; only genomic data (genome-wide PRSs); and both clinical and genomic data. Next, the best-performing multivariable models developed in each aim will be validated in an independent 20% sample of MVP participants, the eMERGE network phase 3, and UK Biobank. Aim 1. Develop and validate prognostic models for the risk of chronic LBP with healthcare use (CLBP-HU) in Veterans. These models will identify Veterans with LBP of substantial impact sufficient to warrant healthcare use, who should be prioritized for rehabilitative pain treatments. GWAS of CLBP-HU will be conducted. Validated variants will be characterized and their potential biological roles examined. Multivariable models for predicting CLBP-HU will then be developed and compared with each other. These models will be informed by (a) EHR-defined clinical data, (b) genomic data (genome-wide PRSs), and (c) both clinical and genomic data. Hypothesis: prognostic models for predicting CLBP-HU will have acceptable discrimination (area under the receiver operating characteristic curve [AUC] ≥ 0.75). The best-performing models will then be validated in other samples. Aim 2. Develop and validate prognostic models for the risk of failure of non-operative treatment (surgical decompression) in two LBP subgroups: (1) Veterans with LSRS and (2) Veterans with SLSS. The same approach will be followed as used for GWAS and model development in Aim 1. Models developed in Aim 2 will identify Veterans at high risk for progression to decompression surgery, for whom prolonged rehabilitation (e.g., physical therapy) is unlikely to be successful. Hypothesis: prognostic models for predicting decompression surgery using genomic data only will have acceptable discrimination (AUC ≥ 0.75).

腰痛 (LBP) 是全球导致残疾的第一大原因，也是新 VA 的第四大常见原因 LBP 的社会负担主要归因于 2 个不同的患者亚组：(1) 使用医疗资源治疗慢性（持续性或复发性）腰痛的患者；以及 (2) 正在接受治疗的患者； 针对腰痛和/或神经病相关的特定脊柱相关疾病的手术治疗 症状/体征，例如腰骶神经根综合征 (LSRS) 和症状性腰椎管狭窄症 （SLSS）。提高这些人的护理效率和治疗结果的个性化方法。 退伍军人亚群体有可能减轻分层退伍军人群体的 LBP 负担。 当与身体相关的临床决策联系起来时，基于预后的 LBP 护理显示出了早期的希望。 分层护理的更强大效果可能会通过改善可行性和预后来实现。 风险分层或将风险分层与有关大剂量治疗的临床决策联系起来的能力 对 LBP 患者亚组的影响程度（例如 LSRS 减压手术）。 研究将应用这两种方法来改善腰痛的分层护理，这将发展和 使用临床电子健康记录 (EHR) 和基因组数据验证强大的预测模型。 这项研究将分为两个部分来实现这两个研究目标，第一部分将涉及全基因组关联。 研究（GWAS）荟萃分析来预测 LBP 相关疾病的结果，包括来自 百万退伍军人计划 (MVP)、电子病历和基因组网络第 3 阶段 (eMERGE3) 网络和英国生物库，以及其他基因组生物库的汇总数据第二部分将。 涉及 LBP 相关结果的多变量预后模型的开发和验证。 80% 的 MVP 将使用交叉验证方法开发多变量预后模型 样本，仅使用来自 VA EHR 的临床数据（就诊、诊断、药学、生命体征等）； 基因组数据（全基因组 PRS）；以及临床和基因组数据。 每个目标中开发的多变量模型将在 MVP 的独立 20% 样本中进行验证 参与者、eMERGE 网络第 3 阶段和英国生物银行 目标 1. 开发和验证预后。 退伍军人因医疗保健而患慢性腰痛的风险模型（CLBP-HU）这些模型将确定退伍军人的慢性腰痛风险。 患有 LBP 的退伍军人具有足以保证医疗保健使用的重大影响，应优先考虑他们 将进行 CLBP-HU 的康复疼痛治疗。 然后将检查用于预测 CLBP-HU 的多变量模型。 这些模型将根据 (a) EHR 定义的临床数据，(b) 进行开发和比较。 基因组数据（全基因组 PRS），以及 (c) 临床和基因组数据假设：预后模型。 预测 CLBP-HU 将具有可接受的辨别力（接受者操作特征曲线下的面积 [AUC] ≥ 0.75）。然后将在其他样本中验证性能最佳的模型。目标 2. 开发和验证。 验证非手术治疗（手术减压）失败风险的预后模型 分为两个 LBP 亚组：(1) 患有 LSRS 的退伍军人和 (2) 患有 SLSS 的退伍军人。 随后用于目标 1 中的 GWAS 和模型开发。目标 2 中开发的模型将识别退伍军人 进展为减压手术的风险很高，需要长期康复（例如身体康复） 疗法）不太可能成功。假设：预测减压手术的预后模型。 仅使用基因组数据将具有可接受的区分度（AUC ≥ 0.75）。