Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, USA.
Spine J. 2021 Oct;21(10):1679-1686. doi: 10.1016/j.spinee.2021.03.026. Epub 2021 Mar 31.
Surgical decompression and stabilization in the setting of spinal metastasis is performed to relieve pain and preserve functional status. These potential benefits must be weighed against the risks of perioperative morbidity and mortality. Accurate prediction of a patient's postoperative survival is a crucial component of patient counseling.
To externally validate the SORG machine learning algorithms for prediction of 90-day and 1-year mortality after surgery for spinal metastasis.
STUDY DESIGN/SETTING: Retrospective, cohort study PATIENT SAMPLE: Patients 18 years or older at a tertiary care medical center treated surgically for spinal metastasis OUTCOME MEASURES: Mortality within 90 days of surgery, mortality within 1 year of surgery METHODS: This is a retrospective cohort study of 298 adult patients at a tertiary care medical center treated surgically for spinal metastasis between 2004 and 2020. Baseline characteristics of the validation cohort were compared to the derivation cohort for the SORG algorithms. The following metrics were used to assess the performance of the algorithms: discrimination, calibration, overall model performance, and decision curve analysis.
Sixty-one patients died within 90 days of surgery and 133 died within 1 year of surgery. The validation cohort differed significantly from the derivation cohort. The SORG algorithms for 90-day mortality and 1-year mortality performed excellently with respect to discrimination; the algorithm for 1-year mortality was well-calibrated. At both postoperative time points, the SORG algorithms showed greater net benefit than the default strategies of changing management for no patients or for all patients.
With an independent, contemporary, and geographically distinct population, we report successful external validation of SORG algorithms for preoperative risk prediction of 90-day and 1-year mortality after surgery for spinal metastasis. By providing accurate prediction of intermediate and long-term mortality risk, these externally validated algorithms may inform shared decision-making with patients in determining management of spinal metastatic disease.
在脊柱转移的情况下，进行手术减压和稳定以缓解疼痛并保持功能状态。这些潜在的益处必须与围手术期发病率和死亡率的风险相权衡。准确预测患者的术后生存是患者咨询的重要组成部分。
验证 SORG 机器学习算法对脊柱转移手术后 90 天和 1 年死亡率的预测能力。
研究设计/设置：回顾性队列研究
在三级护理医疗中心接受脊柱转移手术治疗的 18 岁及以上患者
手术 90 天内的死亡率，手术 1 年内的死亡率
这是一项对三级护理医疗中心 298 例接受脊柱转移手术治疗的成年患者的回顾性队列研究，研究时间为 2004 年至 2020 年。对验证队列的基线特征与 SORG 算法的推导队列进行比较。使用以下指标评估算法的性能：区分度、校准度、整体模型性能和决策曲线分析。
61 例患者在手术后 90 天内死亡，133 例患者在手术后 1 年内死亡。验证队列与推导队列有显著差异。SORG 算法对 90 天死亡率和 1 年死亡率的预测性能非常出色，具有良好的校准度。在两个术后时间点，SORG 算法比默认策略（对无患者或所有患者改变管理策略）具有更大的净收益。
通过使用独立的、当代的和地理位置不同的人群，我们报告了 SORG 算法在脊柱转移手术后 90 天和 1 年死亡率的术前风险预测中的成功外部验证。通过准确预测中期和长期死亡率风险，这些经过外部验证的算法可以为与患者共同决策确定脊柱转移性疾病的管理提供信息。
