Literature DB >> 27815968

A systematic review of biomarkers for the prediction of thromboembolism in lung cancer - Results, practical issues and proposed strategies for future risk prediction models.

Marliese Alexander1, Kate Burbury2.   

Abstract

INTRODUCTION: This review aimed to identify candidate biomarkers for the prediction of thromboembolism (TE) in lung cancer.
MATERIALS AND METHODS: Systematic review of publications indexed in PubMed or EMBASE databases in the past 5years (01/05/2011-01/05/2016) which evaluated baseline and/or longitudinal biomarker measurements as a predictor of subsequent TE (venous and arterial) in lung cancer patients.
RESULTS: Of 1105 studies identified, 18 fulfilled predefined inclusion criteria: 6 prospective and 12 retrospective. The 18 studies included 11,262 patients and 36 unique biomarkers. The combined TE rate was 7% (741/10,854), increasing to 11% (294/2612) within prospective studies. All biomarker measurements were baseline only, with no longitudinal assessment reported. The most frequently investigated biomarkers were tumour-related driver mutations, D-dimer, haemoglobin, white cell, and platelet count; as well as biomarker combinations previously used in risk prediction models, such as Khorana risk score. Biomarker thresholds rather than continuous variable analyses were generally applied, however thresholds were not consistent across studies. D-dimer and epidermal growth factor receptor mutation were the strongest and most reproducible predictors of TE.
CONCLUSION: An important limitation is the lack of prospective data across specific subpopulations of cancer, with correlative, and preferably longitudinal, biomarker assessments. This would provide insight into the pathophysiology, allow patient profiling, and the development of personalised decision-making tools that can be used real-time and throughout the course of the patients' journey, for targeted, risk-adaptive preventative strategies.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Biomarker; Lung cancer; Risk prediction; Thromboembolism; Thrombosis

Mesh:

Substances:

Year:  2016        PMID: 27815968     DOI: 10.1016/j.thromres.2016.10.020

Source DB:  PubMed          Journal:  Thromb Res        ISSN: 0049-3848            Impact factor:   3.944


  8 in total

1.  Prospective Assessment of Clinical Risk Factors and Biomarkers of Hypercoagulability for the Identification of Patients with Lung Adenocarcinoma at Risk for Cancer-Associated Thrombosis: The Observational ROADMAP-CAT Study.

Authors:  Konstantinos Syrigos; Dimitra Grapsa; Rabiatou Sangare; Ilias Evmorfiadis; Annette K Larsen; Patrick Van Dreden; Paraskevi Boura; Andriani Charpidou; Elias Kotteas; Theodoros N Sergentanis; Ismail Elalamy; Anna Falanga; Grigoris T Gerotziafas
Journal:  Oncologist       Date:  2018-08-13

2.  Incidence and risk of thromboembolism associated with bevacizumab in patients with non-small cell lung carcinoma.

Authors:  Li-Juan Li; Di-Fei Chen; Guo-Feng Wu; Wei-Jie Guan; Zheng Zhu; Yi-Qian Liu; Guo-Ying Gao; Yin-Yin Qin; Nan-Shan Zhong
Journal:  J Thorac Dis       Date:  2018-08       Impact factor: 2.895

Review 3.  Cancer-associated pathways and biomarkers of venous thrombosis.

Authors:  Yohei Hisada; Nigel Mackman
Journal:  Blood       Date:  2017-08-14       Impact factor: 22.113

Review 4.  Evaluation of unmet clinical needs in prophylaxis and treatment of venous thromboembolism in high-risk patient groups: cancer and critically ill.

Authors:  Benjamin Brenner; Russell Hull; Roopen Arya; Jan Beyer-Westendorf; James Douketis; Ismail Elalamy; Davide Imberti; Zhenguo Zhai
Journal:  Thromb J       Date:  2019-04-15

5.  RNA expression and risk of venous thromboembolism in lung cancer.

Authors:  Tamara A Sussman; Mohamed E Abazeed; Keith R McCrae; Alok A Khorana
Journal:  Res Pract Thromb Haemost       Date:  2019-12-27

Review 6.  Cancer-Associated Thrombosis: A New Light on an Old Story.

Authors:  Sidrah Shah; Afroditi Karathanasi; Antonios Revythis; Evangelia Ioannidou; Stergios Boussios
Journal:  Diseases       Date:  2021-05-04

7.  Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology.

Authors:  Patrizia Ferroni; Fabio M Zanzotto; Noemi Scarpato; Silvia Riondino; Fiorella Guadagni; Mario Roselli
Journal:  Dis Markers       Date:  2017-09-17       Impact factor: 3.434

8.  Effect of intravitreal injection of aflibercept on blood coagulation parameters in patients with age-related macular degeneration.

Authors:  Constantinos D Georgakopoulos; Olga E Makri; Athina Pallikari; Konstantinos Kagkelaris; Panagiotis Plotas; Vasiliki Grammenou; Andreas Emmanuil
Journal:  Ther Adv Ophthalmol       Date:  2020-02-11
  8 in total

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