Department of Rehabilitation Medicine, University of Washington, Box 356490, 1959 NE Pacific St, Seattle, WA, 98195-6490, USA.
Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA.
BMC Musculoskelet Disord. 2022 Jul 21;23(1):692. doi: 10.1186/s12891-022-05598-x.
Lumbar spinal stenosis (LSS) is a common degenerative condition that contributes to back and back-related leg pain in older adults. Most patients with symptomatic LSS initially receive non-operative care before surgical consultation. However, there is a scarcity of data regarding prognosis for patients seeking non-surgical care. The overall goal of this project is to develop and evaluate a clinically useful model to predict long-term physical function of patients initiating non-surgical care for symptomatic LSS.
This is a protocol for an inception cohort study of adults 50 years and older who are initiating non-surgical care for symptomatic LSS in a secondary care setting. We plan to recruit up to 625 patients at two study sites. We exclude patients with prior lumbar spine surgeries or those who are planning on lumbar spine surgery. We also exclude patients with serious medical conditions that have back pain as a symptom or limit walking. We are using weekly, automated data pulls from the electronic health records to identify potential participants. We then contact patients by email and telephone within 21 days of a new visit to determine eligibility, obtain consent, and enroll participants. We collect data using telephone interviews, web-based surveys, and queries of electronic health records. Participants are followed for 12 months, with surveys completed at baseline, 3, 6, and 12 months. The primary outcome measure is the 8-item PROMIS Physical Function (PF) Short Form. We will identify distinct phenotypes using PROMIS PF scores at baseline and 3, 6, and 12 months using group-based trajectory modeling. We will develop and evaluate the performance of a multivariable prognostic model to predict 12-month physical function using the least absolute shrinkage and selection operator and will compare performance to other machine learning methods. Internal validation will be conducted using k-folds cross-validation.
This study will be one of the largest cohorts of individuals with symptomatic LSS initiating new episodes of non-surgical care. The successful completion of this project will produce a cross-validated prognostic model for LSS that can be used to tailor treatment approaches for patient care and clinical trials.
腰椎管狭窄症（LSS）是一种常见的退行性疾病，会导致老年人腰痛和与腰痛相关的腿痛。大多数有症状的 LSS 患者在接受手术咨询之前，最初会接受非手术治疗。然而，关于接受非手术治疗的患者的预后数据却很少。本项目的总体目标是开发和评估一种临床有用的模型，以预测开始接受非手术治疗的有症状 LSS 患者的长期身体功能。
这是一项在二级医疗机构中对 50 岁及以上开始接受非手术治疗的有症状 LSS 患者的初始队列研究的方案。我们计划在两个研究地点招募多达 625 名患者。我们排除有腰椎手术史或计划进行腰椎手术的患者。我们还排除有腰痛作为症状或限制行走的严重医疗状况的患者。我们正在使用每周从电子病历中自动提取数据，以识别潜在的参与者。然后，我们在新就诊后 21 天内通过电子邮件和电话联系患者，以确定是否符合条件、获得同意并招募参与者。我们通过电话访谈、网络调查和电子病历查询收集数据。参与者随访 12 个月，在基线、3、6 和 12 个月时完成调查。主要结局测量指标是 PROMIS 物理功能（PF）简短形式的 8 项。我们将使用基于群组的轨迹建模，根据基线和 3、6 和 12 个月时的 PROMIS PF 评分来确定不同的表型。我们将使用最小绝对收缩和选择算子（LASSO）开发和评估预测 12 个月身体功能的多变量预后模型，并将性能与其他机器学习方法进行比较。内部验证将使用 k 折交叉验证进行。
这项研究将是接受新非手术治疗的有症状 LSS 患者中最大的队列之一。该项目的成功完成将产生一种经过交叉验证的 LSS 预后模型，可用于为患者护理和临床试验定制治疗方法。
