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

下背痛（LBP）是全球残疾的第一大原因，也是新发VA的第四大最常见原因 残疾赔偿金。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）LBP的患者;以及（2）接受 与LBP和/或神经性疾病相关的特定脊柱相关病症的外科治疗 症状/体征，如腰骶神经根综合征（LSRS）和症状性腰椎管狭窄症 （SLSS）。个性化的方法，以提高护理和治疗结果的效率， 退伍军人亚组有可能减少退伍军人群体的LBP负担。分层 基于预后的LBP护理显示出早期的希望，当与有关身体的临床决定联系起来时， 疗法分层护理的更强大的效果可能来自于提高可行性和预后 风险分层的能力或将风险分层与关于治疗大 LBP患者亚组中的幅度效应（例如，LSRS的减压手术）。拟议 研究将应用这两种方法来改善LBP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。 本研究将分两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，以预测LBP相关疾病的结局，包括来自 百万退伍军人计划（MVP），电子病历和基因组学网络第3阶段 （eMERGE 3）网络和英国生物库，以及来自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。第一、 将在80%的MVP中使用交叉验证方法开发多变量预后模型 样本，仅使用临床数据（访视、诊断、药房、生命体征等）从VA EHR;仅 基因组数据（全基因组PRS）;以及临床和基因组数据。接下来是表现最好的 每个目标中开发的多变量模型将在独立的20% MVP样本中进行验证 参与者，eMERGE网络第三阶段，和英国生物银行。目标1.制定并验证预后 模型的风险慢性LBP与医疗保健使用（CLBP-HU）的退伍军人。这些模型将识别 患有LBP的退伍军人的实质性影响足以保证医疗保健的使用，他们应该优先考虑 疼痛康复治疗。将进行CLBP-HU的GWAS。将对经验证的变体进行表征 以及它们潜在的生物学作用。预测CLBP-HU的多变量模型将是 发展并相互比较。这些模型将通过（a）EHR定义的临床数据，（B） 基因组数据（全基因组PRS），以及（c）临床和基因组数据。假设： 预测CLBP-HU将具有可接受的辨别力（受试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 验证非手术治疗（手术减压）失败风险的预后模型 两个LBP亚组：（1）LSRS退伍军人和（2）SLSS退伍军人。同样的方法将是 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高，对于那些长期康复的人（例如，物理 治疗）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）。

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).