Prediction Models for Venous Thromboembolism in Ambulatory Adults with Pancreatic and Gastro-oesophageal Cancer: Protocol for Systematic Review and Meta-analysis

Overview

Journal BMJ Open

Specialty General Medicine

Date 2022 Mar 5

PMID 35246422

Authors

Asma Zaheer

Nenad Naumovski

Kellie Toohey

Theophile Niyonsenga

Desmond Yip

Nicholas Brown

Reza Mortazavi

Affiliations

Soon will be listed here.

Abstract

Introduction: Venous thromboembolism (VTE) is a common complication of cancer. Pancreatic and gastro-oesophageal cancers are among malignancies that have the highest rates of VTE occurrence. VTE can increase cancer-related morbidity and mortality and disrupt cancer treatment. The risk of VTE can be managed with measures such as using anticoagulant drugs, although the risk of bleeding may be an impeding factor. Therefore, a VTE risk assessment should be performed before the start of anticoagulation in individual patients. Several prediction models have been published, but most of them have low sensitivity and unknown clinical applicability in pancreatic or gastro-oesphageal cancers. We intend to do this systematic review to identify all applicable published predictive models and compare their performance in those types of cancer.

Methods And Analysis: All studies in which a prediction model for VTE have been developed, validated or compared using adult ambulatory patients with pancreatic or gastro-oesphageal cancers will be identified and the reported predictive performance indicators will be extracted. Full text peer-reviewed journal articles of observational or experimental studies published in English will be included. Five databases (Medline, EMBASE, Web of Science, CINAHL and Cochrane) will be searched. Two reviewers will independently undertake each of the phases of screening, data extraction and risk of bias assessment. The quality of the selected studies will be assessed using Prediction model Risk Of Bias Assessment Tool. The results from the review will be used for a narrative information synthesis, and if the same models have been validated in multiple studies, meta-analyses will be done to pool the predictive performance measures.

Ethics And Dissemination: There is no need for ethics approval because the review will use previously peer-reviewed articles. The results will be published.

Prospero Registration Number: CRD42021253887.

Citing Articles

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients.

Xu Q, Lei H, Li X, Li F, Shi H, Wang G Heliyon. 2023; 9(1):e12681.

PMID: 36632097 PMC: 9826862. DOI: 10.1016/j.heliyon.2022.e12681.

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