Risk Factors and Prediction Models for Venous Thromboembolism in Ambulatory Patients with Lung Cancer

Primary thromboprophylaxis in cancer outpatients - real-world evidence

INTRODUCTION

Cancer-associated thrombosis (CAT) is a significant concern among patients with malignant diseases, leading to increased mortality. While current guidelines recommend primary thromboprophylaxis for venous thromboembolism (VTE) in medium-to-high-risk outpatients, this practice remains controversial. A better understanding of primary thromboprophylaxis is crucial, yet there is a lack of Real-World Evidence (RWE) in Portugal.

AIMS

This RWE study aimed to elucidate primary thromboprophylaxis practices among cancer outpatients in Portugal.

METHODS

A five-year observational multicentric study in eight Portuguese health institutions enrolled 124 adult cancer outpatients under primary thromboprophylaxis for VTE. The endpoints were CAT, bleeding, cancer progression and death.

RESULTS

High thrombotic risk tumours were prevalent, with 57% (71) of the patients presenting with pancreatic and gastric cancers. Regarding primary thromboprophylaxis, 55% (68) received Low-Molecular-Weight Heparin (LMWH). VTE was presented in 11% (14) of the patients and major bleeding in 2% (2). Vascular compression, elevated D-dimer and previous VTE were significantly associated with VTE occurrence under primary thromboprophylaxis. The Onkotev model was shown to be the best risk assessment model (RAM) in this population (p = 0.007). CAT patients exhibited a lower progression-free survival than non-CAT patients (p = 0.021), while thrombosis did not influence overall survival (p = 0.542).

CONCLUSION

Primary thromboprophylaxis in medium-to-high-risk cancer outpatients is a safe and effective practice in real-world settings. This study is the first Portuguese RWE on primary thromboprophylaxis, highlighting evidence for improving prophylactic strategies in this population.

A systematic review of risk prediction model of venous thromboembolism for patients with lung cancer

BACKGROUND

Venous thromboembolism (VTE) increases the risk of death or adverse outcomes in patients with lung cancer. Therefore, early identification and treatment of high-risk groups of VTE have been the research focus. In this systematic review, the risk assessment tools of VTE in patients with lung cancer were systematically analyzed and evaluated to provide a reference for VTE management.

METHODS

Relevant studies were retrieved from major English databases (The Cochrane Library, Embase, Web of Science, PubMed, Scopus, Medline) and Chinese databases (China National Knowledge Infrastructure [CNKI] and WanFang Data) until July 2023 and extracted by two researchers. This systematic review was registered at PROSPERO (no. CRD42023409748).

RESULTS

Finally, two prospective cohort studies and four retrospective cohort studies were included from 2019. There was a high risk of bias in all included studies according to the Prediction Model Risk of Bias Assessment tool (PROBAST). In the included studies, Cox and logistic regression were used to construct models. The area under the receiver operating characteristic curve (AUC) of the model ranged from 0.670 to 0.904, and the number of predictors ranged from 4 to 11. The D-dimer index was included in five studies, but significant differences existed in optimal cutoff values from 0.0005 mg/L to 2.06 mg/L. Then, three studies validated the model externally, two studies only validated the model internally, and only one study validated the model using a combination of internal and external validation.

CONCLUSION

VTE risk prediction models for patients with lung cancer have received attention for no more than 5 years. The included model shows a good predictive effect and may help identify the risk population of VTE at an early stage. In the future, it is necessary to improve data modeling and statistical analysis methods, develop predictive models with good performance and low risk of bias, and focus on external validation and recalibration of models.

Arteriovenous thrombotic events in a patient with advanced lung cancer following bevacizumab plus chemotherapy: A case report

BACKGROUND

In driver gene-negative non-small cell lung cancer patients who relapse following radical resection, combination chemotherapy using bevacizumab and platinum-based dual drugs is known to increase both progression-free and overall survival. Treatment initially includes bevacizumab, and if patients are able to tolerate it, bevacizumab can continue to be utilized until disease progression. Bevacizumab is a recombinant humanized monoclonal neutralizing antibody that acts against vascular endothelial growth factor (VEGF). Various anti-VEGF monoclonal antibodies, such as bevacizumab, can increase the risk of arterial thromboembolism. Current data indicate that VEGF-targeted treatment does not significantly increase the risk of venous thromboembolism events, except for bevacizumab.

CASE SUMMARY

A 55-year-old man underwent radical resection for cancer of the right lung. Six months following surgery, multiple metastases were observed in his left lung. Subsequently, six cycles of bevacizumab combined with pemetrexed/carboplatin chemotherapy was given. Efficacy evaluation continued to be partial relief according to RECIST 1.1 standards, and no noticeable adverse reactions were noted. After three cycles of maintenance therapy using a combination of bevacizumab and pemetrexed, the patient developed dizziness and dyspnea. The patient was diagnosed with acute cerebral infarction and pulmonary embolism following head magnetic resonance imaging, computed tomography (CT) angiography, and chest enhanced CT. Although the patient received low-molecular-weight heparin anticoagulation and other treatment, the patient eventually died of respiratory failure 1 mo later. This case report may offer some insight into fatal arteriovenous embolism, which has not been previously reported.

CONCLUSION

Bevacizumab combined with chemotherapy may also increase the risk of arteriovenous thromboembolism. Accordingly, patients who receive angiogenesis inhibitor therapy should be carefully selected. Furthermore, close monitoring and timely intervention are necessary in order to reduce the risk of such toxicities.

Development and Validation of a Risk Prediction Model for Venous Thromboembolism in Lung Cancer Patients Using Machine Learning

Background

There is currently a lack of model for predicting the occurrence of venous thromboembolism (VTE) in patients with lung cancer. Machine learning (ML) techniques are being increasingly adapted for use in the medical field because of their capabilities of intelligent analysis and scalability. This study aimed to develop and validate ML models to predict the incidence of VTE among lung cancer patients.

Methods

Data of lung cancer patients from a Grade 3A cancer hospital in China with and without VTE were included. Patient characteristics and clinical predictors related to VTE were collected. The primary endpoint was the diagnosis of VTE during index hospitalization. We calculated and compared the area under the receiver operating characteristic curve (AUROC) using the selected best-performed model (Random Forest model) through multiple model comparison, as well as investigated feature contributions during the training process with both permutation importance scores and the impurity-based feature importance scores in random forest model.

Results

In total, 3,398 patients were included in our study, 125 of whom experienced VTE during their hospital stay. The ROC curve and precision–recall curve (PRC) for Random Forest Model showed an AUROC of 0.91 (95% CI: 0.893–0.926) and an AUPRC of 0.43 (95% CI: 0.363–0.500). For the simplified model, five most relevant features were selected: Karnofsky Performance Status (KPS), a history of VTE, recombinant human endostatin, EGFR-TKI, and platelet count. We re-trained a random forest classifier with results of the AUROC of 0.87 (95% CI: 0.802–0.917) and AUPRC of 0.30 (95% CI: 0.265–0.358), respectively.

Conclusion

According to the study results, there was no conspicuous decrease in the model’s performance when use fewer features to predict, we concluded that our simplified model would be more applicable in real-life clinical settings. The developed model using ML algorithms in our study has the potential to improve the early detection and prediction of the incidence of VTE in patients with lung cancer.

Models for predicting venous thromboembolism in ambulatory patients with lung cancer: a systematic review protocol

INTRODUCTION

Venous thromboembolism (VTE) is a common complication in patients with cancer and has a determining role in the disease prognosis. The risk is significantly increased with certain types of cancer, such as lung cancer. Partly due to difficulties in managing haemorrhage in outpatient settings, anticoagulant prophylaxis is only recommended for ambulatory patients at high risk of VTE. This requires a precise VTE risk assessment in individual patients. Although VTE risk assessment models have been developed and updated in recent years, there are conflicting reports on the effectiveness of such risk prediction models in patient management. The aim of this systematic review is to gain a better understanding of the available VTE risk assessment tools for ambulatory patients with lung cancer and compare their predictive performance.

METHODS AND ANALYSIS

A systematic review will be conducted using MEDLINE, Cochrane Library, CINAHL, Scopus and Web of Science databases from inception to 30 September 2021, to identify all reports published in English describing VTE risk prediction models which have included adult ambulatory patients with primary lung cancer for model development and/or validation. Two independent reviewers will conduct article screening, study selection, data extraction and quality assessment of the primary studies. Any disagreements will be referred to a third researcher to resolve. The included studies will be assessed for risk of bias and applicability. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies will be used for data extraction and appraisal. Data from similar studies will be used for meta-analysis to determine the incidence of VTE and the performance of the risk models.

ETHICS AND DISSEMINATION

Ethics approval is not required. We will disseminate the results in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42021245907.