Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA.
Department of Artificial Intelligence, Chung-Ang University, Seoul, South Korea.
Spine (Phila Pa 1976). 2023 Apr 1;48(7):460-467. doi: 10.1097/BRS.0000000000004531. Epub 2022 Nov 3.
A retrospective, case-control study.
We aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF).
The average age and proportion of patients with at least one comorbidity undergoing ACDF have increased in recent years. Given the increased morbidity and cost associated with perioperative complications and unplanned readmission, accurate risk stratification of patients undergoing ACDF is of great clinical utility.
This is a retrospective cohort study of adults who underwent anterior cervical fusion at any nonfederal California hospital between 2015 and 2017. The primary outcome was major perioperative complication or 30-day readmission. We built standard and ensemble machine learning models for risk prediction, assessing discrimination, and calibration. The best-performing model was validated on an external cohort comprised of consecutive adult patients who underwent ACDF at our institution between 2013 and 2020.
A total of 23,184 patients were included in this study; there were 1886 cases of major complication or readmissions. The ensemble model was well calibrated and demonstrated an area under the receiver operating characteristic curve of 0.728. The variables most important for the ensemble model include male sex, medical comorbidities, history of complications, and teaching hospital status. The ensemble model was evaluated on the validation cohort (n=260) with an area under the receiver operating characteristic curve of 0.802. The ensemble algorithm was used to build a web-based risk calculator.
We report derivation and external validation of an ensemble algorithm for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion. This model has excellent discrimination and is well calibrated when tested on a contemporaneous external cohort of ACDF cases.
回顾性病例对照研究。
我们旨在建立一个预测前路颈椎融合术后主要围手术期并发症的风险计算器。此外,我们旨在通过接受前路颈椎间盘切除融合术(ACDF)的机构队列患者对该计算器进行外部验证。
近年来,接受 ACDF 的患者的平均年龄和至少有一种合并症的患者比例有所增加。鉴于围手术期并发症和计划外再入院相关的发病率和成本增加,对接受 ACDF 的患者进行准确的风险分层具有重要的临床意义。
这是一项对 2015 年至 2017 年期间在加利福尼亚州任何非联邦医院接受前路颈椎融合术的成年人进行的回顾性队列研究。主要结局是主要围手术期并发症或 30 天再入院。我们为风险预测构建了标准和集成机器学习模型,评估了区分度和校准度。在我们机构 2013 年至 2020 年间接受 ACDF 的连续成年患者的外部队列上验证了表现最佳的模型。
共有 23184 例患者纳入本研究;有 1886 例发生主要并发症或再入院。集成模型校准良好,ROC 曲线下面积为 0.728。对于集成模型最重要的变量包括男性、合并症、并发症史和教学医院地位。在验证队列(n=260)上评估了集成模型,ROC 曲线下面积为 0.802。该集成算法被用于构建一个基于网络的风险计算器。
我们报告了一种用于预测前路颈椎融合术后主要围手术期并发症和 30 天再入院的集成算法的推导和外部验证。当在接受 ACDF 的同期外部队列上进行测试时,该模型具有出色的区分度和良好的校准度。