Department of Orthopedics, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, China.
National Clinical Research Center for Geriatric Diseases, Beijing, China.
J Orthop Surg Res. 2024 Jan 3;19(1):8. doi: 10.1186/s13018-023-04490-1.
The burden of lumbar degenerative diseases (LDD) has increased substantially with the unprecedented aging population. Identifying elderly patients with high risk of postoperative adverse events (AEs) and establishing individualized perioperative management is critical to mitigate added costs and optimize cost-effectiveness to the healthcare system. We aimed to develop a predictive tool for AEs in elderly patients with transforaminal lumbar interbody fusion (TLIF), utilizing multivariate logistic regression, single classification and regression tree (hereafter, "classification tree"), and random forest machine learning algorithms.
This study was a retrospective review of a prospective Geriatric Lumbar Disease Database (age ≥ 65). Our outcome measure was postoperative AEs, including prolonged hospital stays, postoperative complications, readmission, and reoperation within 90 days. Patients were grouped as either having at least one adverse event (AEs group) or not (No-AEs group). Three models for predicting postoperative AEs were developed using training dataset and internal validation using testing dataset. Finally, online tool was developed to assess its validity in the clinical setting (external validation).
The development set included 1025 patients (mean [SD] age, 72.8 [5.6] years; 632 [61.7%] female), and the external validation set included 175 patients (73.2 [5.9] years; 97 [55.4%] female). The predictive ability of our three models was comparable, with no significant differences in AUC (0.73 vs. 0.72 vs. 0.70, respectively). The logistic regression model had a higher net benefit for clinical intervention than the other models. A nomogram based on logistic regression was developed, and the C-index of external validation for AEs was 0.69 (95% CI 0.65-0.76).
The predictive ability of our three models was comparable. Logistic regression model had a higher net benefit for clinical intervention than the other models. Our nomogram and online tool ( https://xuanwumodel.shinyapps.io/Model_for_AEs/ ) could inform physicians about elderly patients with a high risk of AEs within the 90 days after TLIF surgery.
随着人口老龄化的空前增长，腰椎退行性疾病（LDD）的负担大大增加。识别术后不良事件（AE）风险较高的老年患者并建立个体化围手术期管理对于减轻额外成本和优化医疗保健系统的成本效益至关重要。我们旨在利用多变量逻辑回归、单分类和回归树（以下简称“分类树”）和随机森林机器学习算法，为接受经椎间孔腰椎体间融合术（TLIF）的老年患者开发一种预测 AE 的工具。
本研究为前瞻性老年腰椎疾病数据库（年龄≥65 岁）的回顾性研究。我们的结局指标是术后 AE，包括住院时间延长、术后并发症、90 天内再入院和再次手术。患者分为至少有一个不良事件（AE 组）或没有（无 AE 组）。使用训练数据集开发了三种预测术后 AE 的模型，并使用测试数据集进行内部验证。最后，开发了在线工具以评估其在临床环境中的有效性（外部验证）。
开发集包括 1025 例患者（平均[标准差]年龄 72.8[5.6]岁；632[61.7%]为女性），外部验证集包括 175 例患者（73.2[5.9]岁；97[55.4%]为女性）。我们的三种模型的预测能力相当，AUC 无显著差异（分别为 0.73、0.72 和 0.70）。逻辑回归模型对临床干预的净获益高于其他模型。基于逻辑回归的列线图已开发，AE 的外部验证 C 指数为 0.69（95%CI 0.65-0.76）。
我们的三种模型的预测能力相当。逻辑回归模型对临床干预的净获益高于其他模型。我们的列线图和在线工具（https://xuanwumodel.shinyapps.io/Model_for_AEs/）可以为 TLIF 手术后 90 天内 AE 风险较高的老年患者的医生提供信息。
