基于术后Caprini评分的脊柱手术患者住院期间静脉血栓栓塞增强预测模型构建及内部验证
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中南大学湘雅医院 普通外科血管外科,湖南 长沙 410008

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王康韬,中南大学湘雅医院助理研究员,主要从事血管外科疾病方面的研究。

基金项目:

湖南省自然科学基金资助项目 2025JJ70053湖南省自然科学基金资助项目(2025JJ70053)。


Development and internal validation of an enhanced model for predicting in-hospital venous thromboembolism after spine surgery based on the postoperative Caprini score
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Division of Vascular Surgery, Department of General Surgery, Xiangya Hospital, Central South University, Changsha 410008, China

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    摘要:

    背景与目的 静脉血栓栓塞(VTE)是脊柱手术后重要并发症。Caprini评分可用于围手术期VTE风险分层,但对术后早期凝血纤溶状态、营养状态及手术创伤负荷等信息的反映有限。本研究以术后Caprini评分为基础,评价早期术后临床信息的增量预测价值,构建并内部验证脊柱手术患者住院期间VTE预测模型。方法 采用单中心回顾性队列研究,纳入2024年在中南大学湘雅医院接受脊柱手术的成年患者1 508例,以术后住院期间发生总体VTE为主要结局。术后实验室指标取术后24~48 h内首次检测值,发生VTE者仅采用确诊前检测结果。构建3个Logistic回归模型:模型A为术后Caprini评分单变量模型;模型B在此基础上纳入年龄、白蛋白、纤维蛋白降解产物(FDP)、手术时间、术中出血量及输注红细胞量;模型C保留术后Caprini评分、年龄、白蛋白和FDP。采用1 000次Bootstrap重抽样和分层10折交叉验证进行内部验证,并通过受试者工作特征(ROC)曲线下面积(AUC)、校准及决策曲线分析评价模型性能;同时进行术前Caprini评分对照、剔除FDP敏感性分析及LASSO辅助分析。基于验证后的模型B和模型C开发在线Web风险评估工具,实现VTE风险的个体化计算与可视化评估。结果 1 508例患者中156例发生VTE,发生率为10.34%。Bootstrap校正后,模型A、B和C的AUC分别为0.706、0.824和0.821;10折交叉验证折外AUC分别为0.699(95% CI=0.656~0.742)、0.824(95% CI=0.792~0.855)和0.820(95% CI=0.790~0.851)。模型B和C的区分度均优于模型A(均P<0.001),而模型B与C差异无统计学意义(P=0.494)。模型B和C的校准斜率分别为0.954和0.975,决策曲线分析显示,在约0.02~0.30的阈值概率范围内,两种增强模型总体净获益高于模型A。剔除FDP后,增强模型的折外预测AUC降至0.795,与模型B比较,配对DeLong检验的AUC差值为0.028(P=0.006),提示FDP具有一定增量预测价值。基于验证后的模型B和C开发了在线Web风险评估工具,可实现患者VTE风险的个体化计算与可视化评估。结论 术后Caprini评分可提供脊柱手术患者住院期间VTE的基础风险信息,但单独使用时区分度有限。在此基础上整合年龄、白蛋白、FDP及手术等相关信息,可进一步提高风险识别能力。简化模型在减少预测变量的同时保持了与完整增强模型相近的预测性能,可用于术后VTE风险分层及在线辅助评估,但仍需在独立、多中心队列中进行外部验证。

    Abstract:

    Background and Aims Venous thromboembolism (VTE) is an important complication after spine surgery. Although the Caprini score is widely used for perioperative VTE risk stratification, it has limited ability to capture early postoperative information related to coagulation and fibrinolysis, nutritional status, and surgical burden. This study aimed to evaluate the incremental predictive value of early postoperative clinical information beyond the postoperative Caprini score and to develop and internally validate models for predicting in-hospital VTE after spine surgery.Methods This single-center retrospective cohort study included 1 508 adult patients who underwent spine surgery at Xiangya Hospital, Central South University, in 2024. The primary outcome was overall VTE occurring during postoperative hospitalization. Postoperative laboratory variables were defined as the first measurements obtained within 24-48 h after surgery; for patients who developed VTE, only measurements obtained before VTE diagnosis were used. Three Logistic regression models were developed. Model A included the postoperative Caprini score alone. Model B additionally included age, albumin, fibrin degradation products (FDP), operation duration, intraoperative blood loss, and transfused red blood cell units. Model C included the postoperative Caprini score, age, albumin, and FDP. Internal validation was performed using 1 000 bootstrap resamples and stratified 10-fold cross-validation. Model discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration analysis, and decision curve analysis. A preoperative Caprini score model, a sensitivity analysis excluding FDP, and an auxiliary LASSO analysis were also performed. Based on the validated models B and C, online web-based risk assessment tools were developed to enable individualized VTE risk calculation and visual assessment.Results Among the 1 508 patients, 156 developed VTE, corresponding to an incidence of 10.34%. After bootstrap optimism correction, the AUC of models A, B, and C were 0.706, 0.824, and 0.821, respectively. The corresponding out-of-fold AUC from 10-fold cross-validation were 0.699 (95% CI=0.656-0.742), 0.824 (95% CI=0.792-0.855), and 0.820 (95% CI=0.790-0.851). Models B and C demonstrated significantly better discrimination than Model A (both P<0.001), whereas no significant difference was observed between models B and C (P=0.494). The calibration slopes of models B and C were 0.954 and 0.975, respectively. Decision curve analysis showed that models B and C generally provided greater net benefit than model A across threshold probabilities of approximately 0.02-0.30. After excluding FDP, the out-of-fold AUC of the enhanced model was 0.795; paired DeLong comparison with model B yielded an AUC difference of 0.028 (P=0.006), indicating an incremental predictive contribution of FDP. Online web-based risk assessment tools were developed based on the validated models B and C, enabling individualized VTE risk calculation and visual assessment.Conclusion The postoperative Caprini score provides baseline risk information for in-hospital VTE after spine surgery but has limited discrimination when used alone. Incorporating age, albumin, FDP, and surgery-related information further improves risk discrimination. The simplified model maintained predictive performance comparable to that of the full enhanced model while using fewer predictors, supporting its potential use for postoperative VTE risk stratification and online assessment. External validation in independent multicenter cohorts is required before broader clinical application.

    图1 术后VTE预测模型的预测性能及辅助分析 A:模型B和C中各预测变量的OR及95% CI;B:三种模型的10折交叉验证OOF ROC曲线;C:三种模型的OOF校准曲线;D:决策曲线;E:模型B与剔除FDP模型的OOF ROC曲线;F:LASSO 10折交叉验证曲线Fig.1 Predictive performance and auxiliary analyses of the postoperative VTE prediction models A: ORs and 95% CIs for the predictors in models B and C; B: OOF ROC curves of the three models based on 10-fold cross-validation; C: OOF calibration curves of the three models; D: decision curve analysis; E: OOF ROC curves of model B and the model excluding FDP; F: 10-fold cross-validation curve for LASSO analysis
    图2 术后VTE风险在线评估工具界面及访问二维码Fig.2 Interface and QR code for the online postoperative VTE risk assessment tool
    表 2 VTE组与非VTE组患者围手术期及手术相关指标比较Table 2 Comparison of perioperative and surgery-related characteristics between the VTE and non-VTE groups
    表 3 术后VTE预测模型的Logistic回归结果Table 3 Logistic regression results of the postoperative VTE prediction models
    表 4 不同VTE预测模型的内部验证、模型比较及敏感性分析结果Table 4 Internal validation, model comparison, and sensitivity analyses of the VTE prediction models
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王康韬,ALI Muhammad Kashif,潘柏宏,欧阳洋.基于术后Caprini评分的脊柱手术患者住院期间静脉血栓栓塞增强预测模型构建及内部验证[J].中国普通外科杂志,2026,35(8):1632-1642.
DOI:10.7659/j. issn.1005-6947.260360

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  • 收稿日期:2026-06-24
  • 最后修改日期:2026-08-13
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  • 在线发布日期: 2026-09-29
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