Safety and efficacy of primary thromboprophylaxis in cancer patients

Inferior Vena Cava Filter in Cancer Patients: On Whom Should We Be Placing Them?

BACKGROUND

Standard treatment for venous thromboembolism is anticoagulation; vena cava filter placement is an alternative in special situations. We aimed to evaluate the outcomes in patients with cancer undergoing filter placement in a cancer center during a 10-year period and assess which preoperatory variables were associated with poorer survival.

METHODS

Retrospective unicenter analysis during a 10-year period was carried out in patients with cancer who had undergone placement of vena cava filter. Early deaths were those that occurred less than 30 days after the filter placement or that occurred during the same hospital stay of the placement.

RESULTS

About 250 patients were analyzed. About 51.6% were females; 77.2% had proximal lower limb deep vein thrombosis; 34.8% had contraindications to anticoagulation; 32.8% presented bleeding after the onset of anticoagulation; and 18.4% had the filter implanted because they were going to undergo surgery and could not be anticoagulated immediately after. About 51.2% of the filters were removable. However, only 2 had the filter removed. About 59.2% had metastatic disease at the time of filter placement. About 31.2% fulfilled criteria for early death. Of those, 34 patients were put in palliative care after filter insertion (median, 13.5 days). Body mass index >18 kg/m², the absence of metastatic disease, and filter placement during the same anesthesia of another surgery (especially if elective and curative) were associated with a higher chance of survival.

CONCLUSIONS

Multidisciplinary evaluation (and possibly consideration for palliation) should take place before the decision to insert a vena cava filter in severe oncologic cases depending on overall status. Patients with a greater chance of survival at a 3 or 5 years interval seem to be those whose filters were placed in the perioperative context of other surgeries (specially elective and curative), who were not undernourished, and whose disease was not metastatic at that time. For patients who survived, an active investigation protocol for filter removal should be implemented.

Thromboprophylaxis for inpatients with advanced cancer in palliative care settings: A systematic review and narrative synthesis

BACKGROUND

Patients with advanced cancer have an elevated risk of venous thromboembolism. Increasingly, patients are admitted to palliative care settings for brief admissions, with greater numbers of discharges (vs deaths) reported internationally. There is limited guidance around the use of thromboprophylaxis or incidence of venous thromboembolism for these patients.

AIM

The aim of this study was to review the use of thromboprophylaxis as well as incidence of venous thromboembolism and bleeding in palliative care units or residential hospices for patients with advanced cancer.

DESIGN

A systematic review using Cochrane methods.

DATA SOURCES

Medline, Embase and the Cochrane Library were searched up to 28 September 2018 along with a grey literature search; the reference lists of selected papers were hand-searched. Inclusion criteria were original papers assessing thromboprophylaxis use in palliative care units or residential hospices for adult inpatients with cancer. Two reviewers independently selected and appraised papers using a tool designed for disparate data. Heterogeneity in study design made a meta-analysis not possible.

RESULTS

A total of 11 full-text papers (9 quantitative and 2 qualitative) and 11 abstracts were included. Thromboprophylaxis use ranged between 4% and 53%; venous thromboembolism rates between 0.5% and 20%; and bleeding incidence was between 0.01% and 9.8%. Risk assessment tools were used infrequently and adherence to international thromboprophylaxis guidelines ranged between 5% and 71%. Physician opinions differed around the use of thromboprophylaxis; patients were largely accepting of thromboprophylaxis if it was offered.

CONCLUSION

There is limited evidence around the optimal use of thromboprophylaxis for patients with advanced cancer admitted to palliative care settings. Although some patients may derive benefit, further research in this area is warranted.

A reliable method for colorectal cancer prediction based on feature selection and support vector machine

Colorectal cancer (CRC) is a common cancer responsible for approximately 600,000 deaths per year worldwide. Thus, it is very important to find the related factors and detect the cancer accurately. However, timely and accurate prediction of the disease is challenging. In this study, we build an integrated model based on logistic regression (LR) and support vector machine (SVM) to classify the CRC into cancer and normal samples. From various factors, human location, age, gender, BMI, and cancer tumor type, tumor grade, and DNA, of the cancer, we select the most significant factors (p < 0.05) using logistic regression as main features, and with these features, a grid-search SVM model is designed using different kernel types (Linear, radial basis function (RBF), Sigmoid, and Polynomial). The result of the logistic regression indicates that the Firmicutes (AUC 0.918), Bacteroidetes (AUC 0.856), body mass index (BMI) (AUC 0.777), and age (AUC 0.710) and their combined factors (AUC 0.942) are effective for CRC detection. And the best kernel type is RBF, which achieves an accuracy of 90.1% when k = 5, and 91.2% when k = 10. This study provides a new method for colorectal cancer prediction based on independent risky factors. Graphical abstract Flow chart depicting the method adopted in the study. LR (logistic regression) and ROC curve are used to select independent features as input of SVM. SVM kernel selection aims to find the best kernel function for classification by comparing Linear, RBF, Sigmoid, and Polynomial kernel types of SVM, and the result shows the best kernel is RBF. Classification performance of LR + RF, LR + NB, LR + KNN, and LR + ANNs models are compared with LR + SVM. After these steps, the cancer and healthy individuals can be classified, and the best model is selected.