Department of Neurosurgery, University of California San Francisco, 400 Parnassus Ave, San Francisco, CA, 94143, USA.
Department of Neurosurgery, University of Virginia Medical Center, PO Box 800212, Charlottesville, VA, 22908, USA.
Eur Spine J. 2019 Sep;28(9):1998-2011. doi: 10.1007/s00586-019-06079-x. Epub 2019 Jul 19.
Health-related quality of life (HRQL) instruments are essential in value-driven health care, but patients often have more specific, personal priorities when seeking surgical care. The Scoliosis Research Society-22R (SRS-22R), an HRQL instrument for spinal deformity, provides summary scores spanning several health domains, but these may be difficult for patients to utilize in planning their specific care goals. Our objective was to create preoperative predictive models for responses to individual SRS-22R questions at 1 and 2 years after adult spinal deformity (ASD) surgery to facilitate precision surgical care.
Two prospective observational cohorts were queried for ASD patients with SRS-22R data at baseline and 1 and 2 years after surgery. In total, 150 covariates were used in training machine learning models, including demographics, surgical data and perioperative complications. Validation was accomplished via an 80%/20% data split for training and testing, respectively. Goodness of fit was measured using area under receiver operating characteristic (AUROC) curves.
In total, 561 patients met inclusion criteria. The AUROC ranged from 56.5 to 86.9%, reflecting successful fits for most questions. SRS-22R questions regarding pain, disability and social and labor function were the most accurately predicted. Models were less sensitive to questions regarding general satisfaction, depression/anxiety and appearance.
To the best of our knowledge, this is the first study to explicitly model the prediction of individual answers to the SRS-22R questionnaire at 1 and 2 years after deformity surgery. The ability to predict individual question responses may prove useful in preoperative counseling in the age of individualized medicine. These slides can be retrieved under Electronic Supplementary Material.
健康相关生活质量（HRQL）量表在以价值为导向的医疗保健中至关重要，但患者在寻求手术治疗时通常有更具体、更个人的优先事项。Scoliosis Research Society-22R（SRS-22R）是一种脊柱畸形 HRQL 量表，提供了跨越多个健康领域的综合评分，但这些评分可能难以让患者在规划自己特定的护理目标时使用。我们的目标是创建术前预测模型，以预测成人脊柱畸形（ASD）手术后 1 年和 2 年时对 SRS-22R 个别问题的反应，以促进精准手术护理。
对 SRS-22R 基线数据和手术后 1 年和 2 年有数据的 ASD 患者进行了两项前瞻性观察队列研究。在训练机器学习模型时共使用了 150 个协变量，包括人口统计学数据、手术数据和围手术期并发症。分别通过 80%/20%的数据分割进行训练和测试来实现验证。使用接收者操作特征（AUROC）曲线下面积来衡量拟合优度。
共有 561 名患者符合纳入标准。AUROC 范围为 56.5%至 86.9%，反映了大多数问题的拟合良好。与疼痛、残疾以及社会和劳动功能相关的 SRS-22R 问题预测得最为准确。模型对一般满意度、抑郁/焦虑和外观等问题的预测敏感性较低。
据我们所知，这是第一项明确对畸形手术后 1 年和 2 年时 SRS-22R 问卷个别答案进行预测的研究。预测个体问题答案的能力可能在个体化医学时代的术前咨询中证明有用。这些幻灯片可在电子补充材料中检索。
