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The significance of this study in addressing the risk of lower extremity DVT in postoperative GC patients is underscored by the substantial morbidity and potential mortality associated with VTE in this patient population13. Notably, GC surgery is linked to a heightened risk of postoperative VTE, including DVT and PE14,15. Compared with air wave pressure therapy instrument, rivaroxaban has better preventive effect on lower extremity DVT after GC operations16. A systematic review and meta-analysis involving 111,936 patients indicated that the 1-month incidence of VTE post GC surgery was 1.8%, and specifically for DVT, it was 1.2%11. Among the 666 Korean patients after gastrectomy, the overall incidence of VTE was 2.1%17. These figures highlight the critical importance of focusing on DVT in GC patients postsurgery. Moreover, this study aims to fill a significant gap in the current research. While the incidence of VTE in GC patients is known, there is less focus on predicting lower extremity DVT, specifically in the postoperative phase of GC. A retrospective cohort study revealed that age, preoperative blood glucose level, postoperative anemia, and tumor malignancy were independent risk factors for postgastrectomy VTE in GC patients18. However, compared with previous studies, our study focused on predictive modeling using a comprehensive set of clinical indicators, including age and calcium ion levels, and provided a more detailed risk assessment tool; this underscores the need for predictive models that can accurately identify patients at higher risk for DVT following GC surgery, enabling targeted prophylactic strategies.

The predictive model developed in this study demonstrated high accuracy, as reflected by the area under the curve (AUC) values in both the training and validation sets. This finding indicates the strong predictive capability of the NRS-2002, which is essential in clinical settings for risk stratification and management of DVT in postoperative GC patients. The importance of such predictive models is highlighted by the varying risk factors identified across different studies, including age and tumor-related factors. Age has been consistently identified as a significant risk factor for postoperative VTE18, and the role of calcium in coagulation processes further substantiates its relevance as a predictive marker in the developed model. These factors provide critical insights into patient-specific risk profiles and can guide clinicians in the prophylaxis and management of DVT after GC surgery.

According to our univariate analysis, age emerged as a significant independent variable influencing DVT occurrence following gastrectomy in GC patients. Furthermore, multivariate analysis highlighted age as a contributing factor to the development of postoperative DVT in these patients. Age is also a risk factor for VTE in patients with GC19. Here, we found that calcium ions were a significant clinical factor in our model. The role of calcium ions in the coagulation process and thrombosis is complex and multifaceted; one key aspect is their involvement in platelet activation. Platelets play a critical role in maintaining hemostasis and vessel integrity under normal conditions and in thrombosis under pathological conditions. The activation of platelets strongly depends on an increase in the intracellular calcium (Ca2+) concentration. This increase results from the release of Ca2+ by the dense tubular system and the entry of Ca2+ from the extracellular space20. In the context of fibrinogen clotting, calcium ions are also known to be necessary for the normal polymerization of fibrin monomers21. In the activation of coagulation factor XIII, an important player in the final stages of the coagulation cascade, calcium also plays a crucial role22. Therefore, calcium ions are integral to the coagulation process and influence various stages, from platelet activation to stabilization of the fibrin clot.

LDL plays a significant role in the pathogenesis of atherothrombotic processes. It can modify the antithrombotic properties of the vascular endothelium and influence vessel contractility, partly by reducing the availability of endothelial nitric oxide and activating proinflammatory signaling pathways. These modified intravascular LDLs promote the formation of foam cells from smooth muscle cells and macrophages, increasing the vulnerability of atherosclerotic plaques and enhancing the thrombogenicity of both plaques and blood23.

Several research findings indicate that a reduction in hemoglobin levels may serve as an indicator of increased VTE risk and poorer prognosis in cancer patients5. Another study demonstrated that low hemoglobin levels at baseline correlated with an increased likelihood of symptomatic VTE, symptomatic DVT, and nonfatal PE24. Another study investigated the influence of anemia on the risk of bleeding in patients receiving anticoagulant therapy for VTE25. These findings underscore the importance of considering anemia as a factor in the management of VTE, particularly in populations at high risk, such as acutely ill patients and those with cancer.

Different from previous research studies, here, we collected plentiful and comprehensive clinical indicators including a total of 47 baseline, preoperative, surgical and pathological clinical data. So far, we have included the largest number of clinical variables in our study. Most importantly, in our research, we use a variety of comprehensive machine learning algorithms. Machine learning methods have been successfully applied in various fields of medicine and have shown great potential in predictive data analytics26. Compared to conventional prediction models (logistic regression), machine learning models perform as well as logistic regression models; however, some machine learning methods exhibit exceptional performance27. One study developed machine learning models (LightGBMs) to predict VTE diagnosis and 1-year risk using electronic health record data from diverse populations. These tools outperformed existing risk assessment tools, showing robust performance across various VTE types and patient demographics28. In our study, we used various machine learning algorithms, including logistic regression, decision trees, random forests, SVM, XGBoost, and LightGBM. By applying these insights to our study, we can anticipate a more robust and precise model for predicting lower extremity DVT risk in postoperative GC patients, potentially leading to better patient outcomes.

In a real-world setting, the model could be integrated into clinical decision-making processes, perhaps through electronic health records systems. By inputting patient-specific data, health care providers could receive immediate risk assessments, guiding them in choosing the most appropriate prophylactic measures. This approach aligns with the growing trend of personalized medicine, where treatment and preventive strategies are tailored to individual patient characteristics and risk profiles.

Despite its contributions, one potential limitation of this study is its retrospective nature, which may introduce biases such as selection bias or information bias. The data used in the study might also have limitations in terms of their scope or the accuracy of the recorded information. Another limitation could be the generalizability of the findings. The studys results are based on a specific patient population and may not be directly applicable to other populations or settings. Additionally, this study developed a population-specific predictive model. However, the selected predictors were not unique to any specific population, as they appear applicable to patients undergoing gastrointestinal, liver, and pancreatic surgeries. Therefore, it raises the question of whether it is necessary to develop a postoperative lower limb thrombosis prediction model specifically for patients undergoing radical gastrectomy.

Future research should focus on validating the predictive model in diverse patient populations and clinical settings to enhance its generalizability. Future studies could also explore the integration of the model into clinical workflows and its impact on patient outcomes in a real-world setting. However, further research is needed to understand the biological mechanisms underlying the identified risk factors for DVT in GC patients; this could lead to more targeted therapeutic interventions. Additionally, incorporating new types of data, such as genetic or molecular marker data, could improve the models predictive accuracy.

In summary, the development of a predictive model for lower extremity DVT in postoperative GC patients addresses a vital clinical need. The models accuracy and ability to identify significant predictive factors make it a valuable tool for enhancing postoperative care and patient outcomes in patients with GC.

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Predictive modeling of lower extreme deep vein thrombosis following radical gastrectomy for gastric cancer: based on ... - Nature.com