Medcontrol AG, Liestal, Switzerland.
Spine Center Division, Department of Teaching, Research and Development, Schulthess Klinik, Lengghalde 2, 8008, Zurich, Switzerland.
Eur Spine J. 2022 Aug;31(8):2125-2136. doi: 10.1007/s00586-022-07306-8. Epub 2022 Jul 14.
It is clear that individual outcomes of spine surgery can be quite heterogeneous. When consenting a patient for surgery, it is important to be able to offer an individualized prediction regarding the likely outcome. This study used a comprehensive set of data collected over 12 years in an in-house registry to develop a parsimonious model to predict the multidimensional outcome of patients undergoing surgery for degenerative pathologies of the thoracic, lumbar or cervical spine.
Data from 8374 patients (mean age 63.9 (14.9-96.3) y, 53.4% female) were used to develop a model to predict the 12-month scores for the Core Outcome Measures Index (COMI) and its subdomain scores. The data were split 80:20 into a training and test set. The top predictors were selected by applying recursive feature elimination based on LASSO cross validation models. Based on the 111 top predictors (contained within 20 variables), Ridge cross validation models were trained, validated, and tested for each of 9 outcome domains, for patients with either "Back" (thoracic/lumbar spine) or "Neck" (cervical spine) problems (total 18 models).
Among the strongest outcome predictors in most models were: preoperative scores for almost all COMI items (especially axial pain (back or neck) and peripheral pain (leg/buttock or arm/shoulder)), catastrophizing, fear avoidance beliefs, comorbidity, age, BMI, nationality, previous spine surgery, type and spinal level of intervention, number of affected levels, and surgeon seniority. The R of the models on the validation/test sets averaged 0.16/0.13. A preliminary online tool was programmed to present the predicted outcomes for individual patients, based on their presenting characteristics. https://linkup.kws.ch/prognostictool .
The models provided estimates to enable a bespoke prediction of the outcome of surgery for individual patients with varying degenerative pathologies and baseline characteristics. The models form the basis of a simple, freely-available online prognostic tool developed to improve access to and usability of prognostic information in clinical practice. It is hoped that, following confirmation of its validity and practical utility, the tool will ultimately serve to facilitate decision-making and the management of patients' expectations.
很明显，脊柱手术的个体结果可能存在很大差异。在为患者进行手术前，能够对可能的结果进行个体化预测非常重要。本研究使用了在内部注册中心收集的超过 12 年的综合数据集，开发了一个简洁的模型，以预测接受手术治疗的退行性胸、腰或颈椎病变患者的多维结局。
该研究共纳入 8374 例患者（平均年龄 63.9（14.9-96.3）岁，53.4%为女性）的数据，以建立一个模型来预测 12 个月时核心结局测量指标（COMI）及其子域评分。数据以 80:20 的比例分为训练集和测试集。通过基于 LASSO 交叉验证模型的递归特征消除选择最佳预测因子。基于包含在 20 个变量内的 111 个最佳预测因子，为患有“背部”（胸/腰椎）或“颈部”（颈椎）问题的患者（共 18 个模型）训练、验证和测试了 9 个结局领域的 Ridge 交叉验证模型。
在大多数模型中，最强的结局预测因子包括：几乎所有 COMI 项目的术前评分（尤其是轴向疼痛（背部或颈部）和外周疼痛（腿部/臀部或手臂/肩部））、灾难化、回避恐惧信念、合并症、年龄、BMI、国籍、既往脊柱手术、干预类型和脊柱水平、受累节段数以及手术医生级别。模型在验证/测试集上的 R 值平均为 0.16/0.13。基于患者的表现特征，编写了一个初步的在线工具，以提供对个别患者手术结局的预测。https://linkup.kws.ch/prognostictool。
这些模型提供了估计值，可以对患有不同退行性病变和基线特征的患者进行手术结局的个体化预测。该模型为一种简单的、免费的在线预后工具奠定了基础，旨在改善临床实践中预后信息的获取和可用性。我们希望，在确认其有效性和实际效用后，该工具最终能够促进决策制定和患者期望的管理。
