Incidence of Arterial and Venous Thromboembolism in Cancer Patients
Deng, H.; Li, J.; Mei, W.; Lin, X.-X.; Xu, Q.; Zhai, Y.; Zheng, Q.; Chen, J.; Huang, Z.; Cheng, Y.
Show abstract
Background and AimsThe reported incidence of arterial thromboembolism (ATE) and venous thromboembolism (VTE) after cancer varies. A meta-analysis was performed to define the incidence of thromboembolism (TE) in cancer patients. MethodsArticles were searched in PubMed and Embase from inception to November 1, 2022. Studies reporting the incidence data or data from which incidence could be estimated among patients with cancer and the explicit follow-up duration were included. ResultsSeventy-four studies involving 5059134 cancer patients were identified. The incidence rate per 1000 person-years was 11.60 (95% CI 7.62-15.58) for ATE, 6.11 (95% CI 3.70-8.53) for myocardial infarction, 9.07 (95% CI 7.48-10.66) for ischemic stroke, 2.11 (95% CI 0.89-3.31) for another ATE, 26.32 (95% CI 24.46-28.18) for VTE, 12.69 (95% CI 11.51-13.87) for deep vein thrombosis, 5.94 (95% CI 5.29-6.59) for pulmonary embolism, and 13.18 (95% CI 9.93-16.42) for another VTE. In addition, the highest incidence of ATE was observed in patients with gastrointestinal cancer, while patients with pancreatic cancer had the highest incidence of VTE. The risk of ATE and VTE increased at the initial stage of cancer, and then declined and became non-significant. ConclusionThis meta-analysis provided overall estimates of ATE and VTE incidence in cancer patients, adding an important insight into the trajectory of the development of TE in cancer patients, which could help reduce the risk of TE in cancer patients in the future.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- International Multicenter Study Comparing Cancer to Non-Cancer Patients with COVID-19: Impact of Risk Factors and Treatment Modalities on Survivorship 92%
- Clinical Characteristics, Racial Inequities, and Outcomes in Patients with Breast Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Cohort Study 91%
- The impact of lag time to cancer diagnosis and treatment on clinical outcomes prior to the COVID-19 pandemic: a scoping review of systematic reviews and meta-analyses 91%
Similar papers in this journal
- Predictors of survival in patients with ischemic stroke and active cancer: A prospective, multicenter, observational study 94%
- Association between prostate cancer and myocardial infarction management and post-infarction outcomes: A Norwegian registry study 93%
- Heart disease mortality in cancer survivors: A population-based study in Japan 93%
Similar papers in this journal
- COVID-19 is associated with higher risk of venous thrombosis, but not arterial thrombosis, compared with influenza: Insights from a large US cohort 94%
- Incidence and Risk of Post-COVID-19 Thromboembolic Disease and the Impact of Aspirin Prescription; Nationwide Observational Cohort at the US Department of Veteran Affairs 92%
- Epidemiological Risk Factors Associated with Death and Severe Disease in Patients Suffering From COVID-19: A Comprehensive Systematic Review and Meta-analysis 92%
Similar papers in this journal
- A Novel Risk Assessment Model Predicts Major Bleeding Risk at Admission in Medical Inpatients 93%
- Genetic risk and incident venous thromboembolism in middle-aged and older adults following Covid-19 vaccination 90%
- Coagulation factor XII, XI, and VIII activity levels and secondary events after first ischemic stroke 90%
Similar papers in this journal
- Cardiovascular events and venous thromboembolism after primary malignant and non-malignant brain tumour diagnosis: a population matched cohort study in Wales (United Kingdom) 93%
- Association of heavy menstrual bleeding with cardiovascular diseases in US female hospitalizations 89%
- Long-term cardiac symptoms following COVID-19: a systematic review and meta-analysis 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.