Department of Orthopaedic Surgery, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China.
Eur Spine J. 2024 Nov;33(11):4082-4091. doi: 10.1007/s00586-024-08396-2. Epub 2024 Jul 29.
This study aimed to develop and validate a machine learning (ML) model to predict high-grade heterotopic ossification (HO) following Anterior cervical disc replacement (ACDR).
Retrospective review of prospectively collected data of patients undergoing ACDR or hybrid surgery (HS) at a quaternary referral medical center was performed. Patients diagnosed as C3-7 single- or multi-level cervical disc degeneration disease with > 2 years of follow-up and complete pre- and postoperative radiological imaging were included. An ML-based algorithm was developed to predict high grade HO based on perioperative demographic, clinical, and radiographic parameters. Furthermore, model performance was evaluated according to discrimination and overall performance.
In total, 339 ACDR segments were included (61.65% female, mean age 45.65 ± 8.03 years). Over 45.65 ± 8.03 months of follow-up, 48 (14.16%) segments developed high grade HO. The model demonstrated good discrimination and overall performance according to precision (High grade HO: 0.71 ± 0.01, none-low grade HO: 0.85 ± 0.02), recall (High grade HO: 0.68 ± 0.03, none-low grade HO: 0.87 ± 0.01), F1-score (High grade HO: 0.69 ± 0.02, none-low grade HO: 0.86 ± 0.01), and AUC (0.78 ± 0.08), with lower prosthesis‑endplate depth ratio, higher height change, male, and lower postoperative-shell ROM identified as the most important predictive features.
Through an ML approach, the model identified risk factors and predicted development of high grade HO following ACDR with good discrimination and overall performance. By addressing the shortcomings of traditional statistics and adopting a new logical approach, ML techniques can support discovery, clinical decision-making, and intraoperative techniques better.
本研究旨在开发和验证一种机器学习（ML）模型，以预测颈椎前路椎间盘置换（ACDR）后高级异位骨化（HO）的发生。
对一家四级转诊医疗中心前瞻性收集的接受 ACDR 或混合手术（HS）的患者数据进行回顾性分析。纳入标准为 C3-7 单或多节段颈椎间盘退行性疾病患者，随访时间>2 年，且术前和术后影像学检查完整。基于围手术期的人口统计学、临床和影像学参数，建立一种基于 ML 的算法来预测高级 HO。此外，还根据区分度和整体性能来评估模型性能。
共纳入 339 个 ACDR 节段（61.65%为女性，平均年龄 45.65±8.03 岁）。在 45.65±8.03 个月的随访中，48 个（14.16%）节段发生了高级 HO。该模型在区分度和整体性能方面表现良好，其精度（高级 HO：0.71±0.01，无低级别 HO：0.85±0.02）、召回率（高级 HO：0.68±0.03，无低级别 HO：0.87±0.01）、F1 评分（高级 HO：0.69±0.02，无低级别 HO：0.86±0.01）和 AUC（0.78±0.08）较高，较低的假体-终板深度比、较高的高度变化、男性和较低的术后壳 ROM 被确定为最重要的预测特征。
通过 ML 方法，该模型确定了 ACDR 后高级 HO 发生的危险因素，并具有良好的区分度和整体性能。通过克服传统统计学的局限性并采用新的逻辑方法，ML 技术可以更好地支持发现、临床决策和术中技术。
