ABSTRACT

PURPOSE: This study aims to construct a machine learning model that can recognize preoperative, intraoperative, and postoperative high-risk indicators and predict the onset of venous thromboembolism (VTE) in patients.

PATIENTS AND METHODS: A total of 1239 patients diagnosed with gastric cancer were enrolled in this retrospective study, among whom 107 patients developed VTE after surgery. We collected 42 characteristic variables of gastric cancer patients from the database of Wuxi People's Hospital and Wuxi Second People's Hospital between 2010 and 2020, including patients' demographic characteristics, chronic medical history, laboratory test characteristics, surgical information, and patients' postoperative conditions. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN), were employed to develop predictive models. We also utilized Shapley additive explanation (SHAP) for model interpretation and evaluated the models using k-fold cross-validation, receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and external validation metrics.

RESULTS: The XGBoost algorithm demonstrated superior performance compared to the other three prediction models. The area under the curve (AUC) value for XGBoost was 0.989 in the training set and 0.912 in the validation set, indicating high prediction accuracy. Furthermore, the AUC value of the external validation set was 0.85, signifying good extrapolation of the XGBoost prediction model. The results of SHAP analysis revealed that several factors, including higher body mass index (BMI), history of adjuvant radiotherapy and chemotherapy, T-stage of the tumor, lymph node metastasis, central venous catheter use, high intraoperative bleeding, and long operative time, were significantly associated with postoperative VTE.

CONCLUSION: The machine learning algorithm XGBoost derived from this study enables the development of a predictive model for postoperative VTE in patients after radical gastrectomy, thereby assisting clinicians in making informed clinical decisions.

PMID:37228741 | PMC:PMC10202705 | DOI:10.2147/IJGM.S408770

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PubMed articles on: Cancer & VTE/PE

Preoperative Venous Thromboembolism Screening and Postoperative Selective Anticoagulant Therapy Effectively Prevents Postoperative Symptomatic Venous Thromboembolism in Gynecological Malignancies: A 15-Year, Single-Center Study

Clin Appl Thromb Hemost. 2023 Jan-Dec;29:10760296231178300. doi: 10.1177/10760296231178300.

ABSTRACT

The aim of this study was to determine which type of prophylaxis was effective for postoperative symptomatic venous thromboembolism (VTE) in patients with gynecological malignancies. A total of 1756 consecutive patients undergoing laparotomy as first-line treatment were included. In Period 1 (2004-2009), low-molecular weight heparin (LMWH) was not available for postoperative VTE prophylaxis, but available in after Period 2 (2009-2013). In Period 3 (2013-2020), patients with pretreatment VTE could switch from LMWH to direct oral anticoagulant (DOAC) as of 2015. Preoperative VTE was screened by measuring D-dimer, followed by venous ultrasound imaging, and computed tomography and/or perfusion lung scintigraphy. Postoperative symptomatic VTE occurred with an incidence of 2.8% by the measures without prophylactic LMWH administration in Period 1. The incidence of postoperative symptomatic VTE was 0.6% in Period 2 and 0.3% in Period 3, being significantly reduced compared with Period 1 (P < .01 and < .0001). The incidences were not significantly different between Periods 2 and 3, but no patient switching to DOAC in Period 3 (n = 79) developed symptomatic VTE. Our preoperative VTE screening and postoperative selective LMWH administration were significantly preventive against postoperative symptomatic VTE.

PMID:37231620 | PMC:PMC10226033 | DOI:10.1177/10760296231178300

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PubMed articles on: Cancer & VTE/PE

Ambulatory cancer patients: who should definitely receive antithrombotic prophylaxis and who should never receive

Intern Emerg Med. 2023 May 25. doi: 10.1007/s11739-023-03306-8. Online ahead of print.

ABSTRACT

Up to 15-20% of cancer patients experience one or more episodes of venous thromboembolism during cancer disease. Approximately 80% of all cancer-associated venous thromboembolic events occur in non-hospitalized patients. Routine thromboprophylaxis for outpatients with cancer who start new anticancer treatment is currently not recommended by the international guidelines due to the high heterogeneity of these patients in terms of VTE or bleeding risks, the difficulties in selecting patients at high risk, and the uncertainty of duration of prophylaxis. Although the international guidelines endorsed the Khorana score for estimating the thrombotic risk in ambulatory cancer patients, the discriminatory performance of this score is not completely convincing and varies according to the cancer type. Consequently, a minority of ambulatory patients with cancer receive an accurate screening for primary prophylaxis of VTE. The aim of this review is to provide support to physicians in identifying those ambulatory patients with cancer for whom thromboprophylaxis should be prescribed and those that should not be candidate to thromboprophylaxis. In absence of high bleeding risk, primary thromboprophylaxis should be recommended in patients with pancreatic cancer and, probably, in patients with lung cancer harboring ALK/ROS1 translocations. Patients with upper gastrointestinal cancers are at high risk of VTE, but a careful assessment of bleeding risk should be made before deciding on antithrombotic prophylaxis. Primary prevention of VTE is not recommended in cancer patients at increased risk of bleeding as patients with brain cancer, with moderate-to-severe thrombocytopenia or severe renal impairment.

PMID:37227679 | DOI:10.1007/s11739-023-03306-8

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PubMed articles on: Cardio-Oncology

Cardiotoxicities of Non-Chemotherapeutic Metastatic Breast Cancer Treatments

Curr Oncol Rep. 2023 May 30. doi: 10.1007/s11912-023-01427-z. Online ahead of print.