University of Zürich, Zürich, Switzerland.
The Walton Centre NHS Foundation Trust, Liverpool, UK.
Eur Spine J. 2021 May;30(5):1337-1354. doi: 10.1007/s00586-021-06777-5. Epub 2021 Mar 9.
Predictive models in spine surgery are of use in shared decision-making. This study sought to develop multivariable models to predict the probability of general and surgical perioperative complications of spinal surgery for lumbar degenerative diseases.
Data came from EUROSPINE's Spine Tango Registry (1.2012-12.2017). Separate prediction models were built for surgical and general complications. Potential predictors included age, gender, previous spine surgery, additional pathology, BMI, smoking status, morbidity, prophylaxis, technology used, and the modified Mirza invasiveness index score. Complete case multiple logistic regression was used. Discrimination was assessed using area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CI). Plots were used to assess the calibration of the models.
Overall, 23'714/68'111 patients (54.6%) were available for complete case analysis: 763 (3.2%) had a general complication, with ASA score being strongly predictive (ASA-2 OR 1.6, 95% CI 1.20-2.12; ASA-3 OR 2.98, 95% CI 2.19-4.07; ASA-4 OR 5.62, 95% CI 3.04-10.41), while 2534 (10.7%) had a surgical complication, with previous surgery at the same level being an important predictor (OR 1.9, 95%CI 1.71-2.12). Respectively, model AUCs were 0.74 (95% CI, 0.72-0.76) and 0.64 (95% CI, 0.62-0.65), and calibration was good up to predicted probabilities of 0.30 and 0.25, respectively.
We developed two models to predict complications associated with spinal surgery. Surgical complications were predicted with less discriminative ability than general complications. Reoperation at the same level was strongly predictive of surgical complications and a higher ASA score, of general complications. A web-based prediction tool was developed at https://sst.webauthor.com/go/fx/run.cfm?fx=SSTCalculator .
脊柱外科中的预测模型有助于共同决策。本研究旨在建立多变量模型，以预测腰椎退行性疾病脊柱手术的一般和手术围手术期并发症的概率。
数据来自 EUROSPINE 的 Spine Tango 注册中心（2012 年 12 月至 2017 年 12 月）。分别为手术和一般并发症建立了预测模型。潜在的预测因素包括年龄、性别、既往脊柱手术、附加病变、BMI、吸烟状况、发病率、预防措施、使用的技术以及改良 Mirza 侵袭性指数评分。采用完整病例多变量逻辑回归。使用接受者操作特征曲线（ROC）下面积（AUC）及其 95%置信区间（CI）评估区分度。绘制图表以评估模型的校准。
共有 23714/68111 例患者（54.6%）可进行完整病例分析：763 例（3.2%）发生一般并发症，ASA 评分具有很强的预测性（ASA-2 OR 1.6，95%CI 1.20-2.12；ASA-3 OR 2.98，95%CI 2.19-4.07；ASA-4 OR 5.62，95%CI 3.04-10.41），而 2534 例（10.7%）发生手术并发症，同一水平的既往手术是一个重要的预测因素（OR 1.9，95%CI 1.71-2.12）。相应的模型 AUC 分别为 0.74（95%CI，0.72-0.76）和 0.64（95%CI，0.62-0.65），校准良好，预测概率分别高达 0.30 和 0.25。
我们开发了两种模型来预测与脊柱手术相关的并发症。手术并发症的预测能力低于一般并发症。同一水平的再次手术对手术并发症有强烈的预测作用，而较高的 ASA 评分对一般并发症有预测作用。我们在 https://sst.webauthor.com/go/fx/run.cfm?fx=SSTCalculator 开发了一个基于网络的预测工具。
