Background: Based on the literature and data on its clinical trials, the incidence of venous thromboembolism (VTE) in patients undergoing neurosurgery has been 3.0%~26%. We used advanced machine learning techniques and statistical methods to provide a clinical prediction model for VTE after neurosurgery. Methods: All patients (n = 5867) who underwent neurosurgery from the development and retrospective internal validation cohorts were obtained from May 2017 to April 2022 at the Department of Neurosurgery at the Sanbo Brain Hospital. The clinical and biomarker variables were divided into pre-, intra-, and postoperative. A univariate logistic regression (LR) was applied to explore the 67 candidate predictors with VTE. We used a multivariable logistic regression (MLR) to select all significant MLR variables of MLR to build the clinical risk prediction model. We used a random forest to calculate the importance of significant variables of MLR. In addition, we conducted prospective internal (n = 490) and external validation (n = 2301) for the model. Results: Eight variables were selected for inclusion in the final clinical prediction model: D-dimer before surgery, activated partial thromboplastin time before neurosurgery, age, craniopharyngioma, duration of operation, disturbance of consciousness on the second day after surgery and high dose of mannitol, and highest D-dimer within 72 h after surgery. The area under the curve (AUC) values for the development, retrospective internal validation, and prospective internal validation cohorts were 0.78, 0.77, and 0.79, respectively. The external validation set had the highest AUC value of 0.85. Conclusions: This validated clinical prediction model, including eight clinical factors and biomarkers, predicted the risk of VTE following neurosurgery. Looking forward to further research exploring the standardization of clinical decision-making for primary VTE prevention based on this model.
背景：根据文献及其临床试验数据，神经外科手术患者静脉血栓栓塞症（VTE）的发生率为3.0%~26%。本研究采用先进的机器学习技术与统计方法，构建神经外科术后VTE的临床预测模型。方法：研究纳入2017年5月至2022年4月期间于三博脑科医院神经外科接受手术的全部患者（n=5867），分为模型开发队列与回顾性内部验证队列。临床及生物标志物变量按术前、术中、术后三个阶段划分。首先通过单因素逻辑回归分析67个候选预测因子与VTE的关联，继而采用多因素逻辑回归筛选显著变量构建临床风险预测模型，并运用随机森林算法评估各显著变量的重要性。此外，研究还进行了前瞻性内部验证（n=490）与外部验证（n=2301）。结果：最终模型纳入八个预测变量：术前D-二聚体水平、神经外科术前活化部分凝血活酶时间、年龄、颅咽管瘤、手术时长、术后第二天意识障碍、大剂量甘露醇使用及术后72小时内最高D-二聚体值。模型在开发队列、回顾性内部验证队列和前瞻性内部验证队列中的曲线下面积（AUC）值分别为0.78、0.77和0.79，外部验证集的AUC值最高，达到0.85。结论：本研究构建的临床预测模型包含八个临床因素与生物标志物，可有效预测神经外科术后VTE风险。期待未来基于该模型开展进一步研究，探索原发性VTE预防临床决策的标准化路径。
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