Background & aims: Deep vein thrombosis, a common complication after laparoscopic surgery, can negatively affect patients' limb motor function and even seriously threaten their lives. Therefore, it is crucial to accurately identify patients at high risk of lower extremity deep vein thrombosis. Thus, the aim of this study was to develop a model to predict the occurrence of deep vein thrombosis in patients after laparoscopy.

Methods: We retrospectively analyzed the clinical data of patients who underwent laparoscopic surgery at Wenzhou Central Hospital's Hepatobiliary Surgery Department. Patients with postoperative deep vein thrombosis composed the observation group, while others composed the control group. Eleven key features were identified through group comparisons and used for model development. Twenty machine learning algorithms were evaluated, and the top five algorithms were used to build the final model by stacking.

Results: A total of 335 patients underwent laparoscopic abdominal surgery. Patients with deep vein thrombosis (9.9%) differed significantly in age, history of tumor, hemoglobin, red blood cell counts, preoperative blood pressure, duration of the surgery, activated partial thromboplastin time, D-dimer, total protein, albumin, and calcium. According to our model, the most important features influencing the predictions were tumor history, age, time to surgery, and D-dimer level. We employed two interpretability methods: decomposition interpretation and Shapley additive explanation. Decomposition analysis revealed that the three study characteristics with the strongest predictive effect for deep vein thrombosis occurrence after laparoscopy were, in descending order, the time of surgery, patient age, and tumor history. Conversely, for ruling out deep vein thrombosis, the most important features were tumor history, hemoglobin level, and age. Shapley additive explanation revealed that tumor history, age, and time of surgery were the most important factors for predicting and ruling out deep vein thrombosis following laparoscopy. We additionally selected 114 patients for external validation, and the results showed that the ROC of validation set for the LASDVT model was 0.9293 and the AUPRC was 0.6497. The effect of the LASDVT model was statistically different (delong test, p = 0.0047) and superior to the Caprini score.

Conclusion: We present a model for predicting deep vein thrombosis in laparoscopic surgery patients. This model outperformed the Caprini score in predicting the incidence of deep vein thrombosis.

Keywords: artificial intelligence; clinical supervision; deep vein thrombosis; laparoscopic surgery; nursing diagnosis; postoperative care.

© 2025 Yang, Peng, Lin, Guan, Zhang and Yu.

背景与目的: 腹腔镜手术后的深静脉血栓形成是一种常见并发症，会对患者的肢体运动功能产生负面影响，甚至严重威胁生命安全。因此，准确识别下肢深静脉血栓形成的高风险患者至关重要。本研究旨在开发一种模型，用于预测腹腔镜术后患者的深静脉血栓发生情况。

方法: 我们回顾性分析了温州市中心医院肝胆外科接受腹腔镜手术的患者的临床数据。将术后出现深静脉血栓形成的患者列为观察组，其他患者为对照组。通过组间比较确定了11个关键特征，并用于模型开发。评估了20种机器学习算法，选取前五名算法通过堆叠方式构建最终模型。

结果: 共有335例患者接受了腹腔镜腹部手术。深静脉血栓形成患者的年龄、肿瘤史、血红蛋白水平、红细胞计数、术前血压、手术时长、活化部分凝血酶原时间、D-二聚体、总蛋白、白蛋白和钙含量均显著不同。根据我们的模型，影响预测结果最重要的特征为肿瘤史、年龄、手术等待时间和D-二聚体水平。我们采用两种可解释性方法：分解解释和Shapley加法解释。分解分析显示，在腹腔镜术后深静脉血栓形成的三项研究特征中，手术时间、患者年龄和肿瘤史的预测效果最强。相反，排除深静脉血栓形成时最重要的特征是肿瘤史、血红蛋白水平和年龄。Shapley加法解释表明，在预测和排除腹腔镜术后的深静脉血栓方面，肿瘤史、年龄和手术时间是最重要因素。我们另外选择了114名患者进行外部验证，结果显示LASDVT模型的ROC值为0.9293，AUPRC值为0.6497。通过delong检验（p=0.0047），该效果统计学上有显著差异，并优于Caprini评分。

结论: 我们提出了一种用于预测腹腔镜手术患者深静脉血栓的模型，该模型在预测深静脉血栓发生率方面优于Caprini评分。

关键词: 人工智能；临床监督；深静脉血栓形成；腹腔镜手术；护理诊断；术后护理。