Development and validation of a machine learning model for predicting venous thromboembolism complications following colorectal cancer surgery.

IF 6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Zongsheng Sun, Di Hao, Mingyu Yang, Wenzhi Wu, Hanhui Jing, Zhensong Yang, Anbang Sun, Wentao Xie, Longbo Zheng, Xixun Wang, Dongsheng Wang, Yun Lu, Guangye Tian, Shanglong Liu
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Abstract

Postoperative venous thromboembolism (VTE) in colorectal cancer (CRC) patients undergoing surgery results in poor prognosis. However, there are no effective tools for early screening and predicting VTE. In this study, we developed a machine learning (ML)-based model for predicting the risk of VTE following CRC surgery and tested its performance using an external dataset. A total of 3227 CRC surgery patients were enrolled from the Affiliated Hospital of Qingdao University and Yantai Yuhuangding Hospital (from January 2016 to December 2023). Subsequently, 1596 patients from the Affiliated Hospital of Qingdao University were assigned to the training set, and 716 patients from Yantai Yuhuangding Hospital were assigned to the external validation set. A model was developed and trained using six ML algorithms using the stacking ensemble technique. Moreover, all models were developed using the tenfold cross-validation on the training set, and their performance was tested using an independent external validation set. In the training set, 173 (10.8%) patients developed VTE, 163 (10.2%) patients experienced deep venous thrombosis, and 29 (1.82%) cases had pulmonary embolism (PE). In the external validation set, 85 (11.9%) cases of VTE, 83 (11.6%) cases of deep vein thrombosis, and 14 (1.96%) cases of PE were recorded. The analysis revealed that the stacking model outperformed all other models in the external validation set, achieving significantly better performance in all metrics: the area under the receiver operating characteristic curve = 0.840 (0.790-0.887), accuracy = 0.810 (0.783-0.836), specificity = 0.819 (0.790-0.848), sensitivity = 0.741 (0.652-0.825), and recall = 0.959 (0.942-0.975). The stacking model for surgical CRC patients shows promise in enabling timely clinical detection of high-risk cases. This method facilitates the prioritized implementation of prophylactic anticoagulation in confirmed high-risk individuals, thereby mitigating unnecessary pharmacological intervention in low-risk populations.

预测结直肠癌手术后静脉血栓栓塞并发症的机器学习模型的开发和验证。
结直肠癌(CRC)手术患者术后静脉血栓栓塞(VTE)导致预后不良。然而,没有有效的工具来早期筛查和预测静脉血栓栓塞。在这项研究中,我们开发了一个基于机器学习(ML)的模型来预测CRC手术后VTE的风险,并使用外部数据集测试了其性能。2016年1月至2023年12月,青岛大学附属医院和烟台玉皇顶医院共纳入3227例结直肠癌手术患者。随后,青岛大学附属医院的1596例患者被分配到训练集,烟台玉皇顶医院的716例患者被分配到外部验证集。使用六种ML算法,使用堆叠集成技术开发和训练了一个模型。此外,所有模型都使用训练集上的十倍交叉验证来开发,并使用独立的外部验证集来测试它们的性能。在训练集中,173例(10.8%)患者发生VTE, 163例(10.2%)患者发生深静脉血栓形成,29例(1.82%)患者发生肺栓塞(PE)。外部验证集中,VTE 85例(11.9%),深静脉血栓83例(11.6%),PE 14例(1.96%)。分析结果表明,叠加模型在外部验证集中表现优于其他模型,在受试者工作特征曲线下面积= 0.840(0.790-0.887),准确度= 0.810(0.783-0.836),特异性= 0.819(0.790-0.848),灵敏度= 0.741(0.652-0.825),召回率= 0.959(0.942-0.975)。手术结直肠癌患者的堆叠模型显示出能够及时临床发现高危病例的希望。该方法有利于在确诊的高危人群中优先实施预防性抗凝,从而减少了低危人群中不必要的药物干预。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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