Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China.
China for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Front Cell Infect Microbiol. 2023 Aug 3;13:1220456. doi: 10.3389/fcimb.2023.1220456. eCollection 2023.
To investigate the differences in postoperative deep venous thrombosis (DVT) between patients with spinal infection and those with non-infected spinal disease; to construct a clinical prediction model using patients' preoperative clinical information and routine laboratory indicators to predict the likelihood of DVT after surgery.
According to the inclusion criteria, 314 cases of spinal infection (SINF) and 314 cases of non-infected spinal disease (NSINF) were collected from January 1, 2016 to December 31, 2021 at Xiangya Hospital, Central South University, and the differences between the two groups in terms of postoperative DVT were analyzed by chi-square test. The spinal infection cases were divided into a thrombotic group (DVT) and a non-thrombotic group (NDVT) according to whether they developed DVT after surgery. Pre-operative clinical information and routine laboratory indicators of patients in the DVT and NDVT groups were used to compare the differences between groups for each variable, and variables with predictive significance were screened out by least absolute shrinkage and operator selection (LASSO) regression analysis, and a predictive model and nomogram of postoperative DVT was established using multi-factor logistic regression, with a Hosmer- Lemeshow goodness-of-fit test was used to plot the calibration curve of the model, and the predictive effect of the model was evaluated by the area under the ROC curve (AUC).
The incidence of postoperative DVT in patients with spinal infection was 28%, significantly higher than 16% in the NSINF group, and statistically different from the NSINF group (P < 0.000). Five predictor variables for postoperative DVT in patients with spinal infection were screened by LASSO regression, and plotted as a nomogram. Calibration curves showed that the model was a good fit. The AUC of the predicted model was 0.8457 in the training cohort and 0.7917 in the validation cohort.
In this study, a nomogram prediction model was developed for predicting postoperative DVT in patients with spinal infection. The nomogram included five preoperative predictor variables, which would effectively predict the likelihood of DVT after spinal infection and may have greater clinical value for the treatment and prevention of postoperative DVT.
探讨脊柱感染与非感染性脊柱疾病患者术后深静脉血栓形成（DVT）的差异；利用患者术前临床信息和常规实验室指标构建预测术后 DVT 发生概率的临床预测模型。
根据纳入标准，收集 2016 年 1 月 1 日至 2021 年 12 月 31 日期间在中南大学湘雅医院就诊的 314 例脊柱感染（SINF）和 314 例非感染性脊柱疾病（NSINF）患者，采用卡方检验比较两组患者术后 DVT 的差异。根据术后是否发生 DVT 将脊柱感染病例分为血栓组（DVT）和非血栓组（NDVT）。比较 DVT 组和 NDVT 组患者术前临床信息和常规实验室指标的组间差异，采用最小绝对收缩和选择算子（LASSO）回归分析筛选出有预测意义的变量，采用多因素逻辑回归建立术后 DVT 的预测模型和列线图，采用 Hosmer-Lemeshow 拟合优度检验绘制模型的校准曲线，采用 ROC 曲线下面积（AUC）评估模型的预测效果。
脊柱感染患者术后 DVT 的发生率为 28%，显著高于 NSINF 组的 16%，与 NSINF 组比较差异有统计学意义（P<0.000）。LASSO 回归筛选出脊柱感染患者术后 DVT 的 5 个预测变量，并绘制为列线图。校准曲线显示模型拟合良好。预测模型在训练队列中的 AUC 为 0.8457，在验证队列中的 AUC 为 0.7917。
本研究建立了一种预测脊柱感染患者术后 DVT 的列线图预测模型。该列线图包含 5 个术前预测变量，可有效预测脊柱感染后 DVT 的发生概率，可能对术后 DVT 的治疗和预防具有更大的临床价值。
