Predictors of thrombosis in patients treated with bevacizumab
Introduction: Bevacizumab is an anti-VEGF monoclonal antibody used widely in oncology. It causes an increased risk of both thrombotic events and proteinuria. Thrombotic events are also a known association of nephrotic syndrome, however, drug-induced proteinuria contributing to thrombosis in this pat...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2022-03-01
|
Series: | Thrombosis Update |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S266657272100064X |
_version_ | 1818487402115629056 |
---|---|
author | Jessica Sparks Xiaoyong Wu Mika Kessans Knable Shesh N. Rai Vivek Sharma |
author_facet | Jessica Sparks Xiaoyong Wu Mika Kessans Knable Shesh N. Rai Vivek Sharma |
author_sort | Jessica Sparks |
collection | DOAJ |
description | Introduction: Bevacizumab is an anti-VEGF monoclonal antibody used widely in oncology. It causes an increased risk of both thrombotic events and proteinuria. Thrombotic events are also a known association of nephrotic syndrome, however, drug-induced proteinuria contributing to thrombosis in this patient population has not been reported in the literature. Methods: Patients treated with bevacizumab from April 2016 to April 2020 at our institution were identified. The primary objective was to investigate the risk of thrombosis in patients who had proteinuria compared to those without proteinuria. Secondary objectives included evaluating other predictors of thrombosis including hypertension, hyperlipidemia, Khorana score, diabetes, atrial fibrillation, tobacco use, and BMI. Results: Of the 203 patients treated with bevacizumab, 160 had some degree of proteinuria. A thrombotic event occurred in 8/58 (13.8%) of the trace proteinuria cohort, 19/102 (18.6%) of the proteinuria greater than 30 mg/dL cohort and 5/43 (11.6%) of the no proteinuria cohort (p = 0.508). Additionally, thrombotic events occurred in 24/116 (20.7%) of the hypertension cohort compared to 8/87 (9.2%) of the normotensive patients (p = 0.026) and in 15/52 (28.8%) of hyperlipidemic patients vs 17/151 (11.3%) of those with normal lipids (p = 0.003). The Khorana score was not a significant predictor in this population. In further analyzing our data, we found increasing thrombotic events with each addition of the most telling predictors of thromboses in our population: hypertension, hyperlipidemia, and greater than trace proteinuria, such that patients with all three risk factors present vs none had an odds ratio of 6.786 (p = 0.004). Conclusion: In patients on bevacizumab, hypertension and hyperlipidemia may better predict thrombotic risk than the Khorana score. While overall proteinuria did not reach statistical significance, there was a numerical trend toward higher rates of thrombosis as the degree of proteinuria increased. Finally, incorporating these three risk factors into a clinical risk score may help stratify patients into lower and higher risk categories which may assist clinicians in making decisions about the use of prophylactic anticoagulation in this population. |
first_indexed | 2024-12-10T16:37:19Z |
format | Article |
id | doaj.art-9beda000e9eb4620a9da739b7ab03b0d |
institution | Directory Open Access Journal |
issn | 2666-5727 |
language | English |
last_indexed | 2024-12-10T16:37:19Z |
publishDate | 2022-03-01 |
publisher | Elsevier |
record_format | Article |
series | Thrombosis Update |
spelling | doaj.art-9beda000e9eb4620a9da739b7ab03b0d2022-12-22T01:41:21ZengElsevierThrombosis Update2666-57272022-03-016100095Predictors of thrombosis in patients treated with bevacizumabJessica Sparks0Xiaoyong Wu1Mika Kessans Knable2Shesh N. Rai3Vivek Sharma4James Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40202, USABiostatistics and Bioinformatics Facility, Brown Cancer Center, University of Louisville, Louisville, KY, 40202, USAJames Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40202, USAJames Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40202, USA; Biostatistics and Bioinformatics Facility, Brown Cancer Center, University of Louisville, Louisville, KY, 40202, USA; Biostatistics and Informatics Facility, Center for Integrative Environmental Research Sciences, University of Louisville, Louisville, KY, 40202, USA; Christina Lee Brown Envirome Institute, University of Louisville, Louisville, KY, 40202, USA; Hepatobiology and Toxicology Center, University of Louisville, Louisville, KY, 40202, USA; School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY, 40292, USA; Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, 40202, USAJames Graham Brown Cancer Center, University of Louisville, Louisville, KY, 40202, USA; Corresponding author.Introduction: Bevacizumab is an anti-VEGF monoclonal antibody used widely in oncology. It causes an increased risk of both thrombotic events and proteinuria. Thrombotic events are also a known association of nephrotic syndrome, however, drug-induced proteinuria contributing to thrombosis in this patient population has not been reported in the literature. Methods: Patients treated with bevacizumab from April 2016 to April 2020 at our institution were identified. The primary objective was to investigate the risk of thrombosis in patients who had proteinuria compared to those without proteinuria. Secondary objectives included evaluating other predictors of thrombosis including hypertension, hyperlipidemia, Khorana score, diabetes, atrial fibrillation, tobacco use, and BMI. Results: Of the 203 patients treated with bevacizumab, 160 had some degree of proteinuria. A thrombotic event occurred in 8/58 (13.8%) of the trace proteinuria cohort, 19/102 (18.6%) of the proteinuria greater than 30 mg/dL cohort and 5/43 (11.6%) of the no proteinuria cohort (p = 0.508). Additionally, thrombotic events occurred in 24/116 (20.7%) of the hypertension cohort compared to 8/87 (9.2%) of the normotensive patients (p = 0.026) and in 15/52 (28.8%) of hyperlipidemic patients vs 17/151 (11.3%) of those with normal lipids (p = 0.003). The Khorana score was not a significant predictor in this population. In further analyzing our data, we found increasing thrombotic events with each addition of the most telling predictors of thromboses in our population: hypertension, hyperlipidemia, and greater than trace proteinuria, such that patients with all three risk factors present vs none had an odds ratio of 6.786 (p = 0.004). Conclusion: In patients on bevacizumab, hypertension and hyperlipidemia may better predict thrombotic risk than the Khorana score. While overall proteinuria did not reach statistical significance, there was a numerical trend toward higher rates of thrombosis as the degree of proteinuria increased. Finally, incorporating these three risk factors into a clinical risk score may help stratify patients into lower and higher risk categories which may assist clinicians in making decisions about the use of prophylactic anticoagulation in this population.http://www.sciencedirect.com/science/article/pii/S266657272100064XBevacizumabThrombosisProteinuriaHypertensionHyperlipidemia |
spellingShingle | Jessica Sparks Xiaoyong Wu Mika Kessans Knable Shesh N. Rai Vivek Sharma Predictors of thrombosis in patients treated with bevacizumab Thrombosis Update Bevacizumab Thrombosis Proteinuria Hypertension Hyperlipidemia |
title | Predictors of thrombosis in patients treated with bevacizumab |
title_full | Predictors of thrombosis in patients treated with bevacizumab |
title_fullStr | Predictors of thrombosis in patients treated with bevacizumab |
title_full_unstemmed | Predictors of thrombosis in patients treated with bevacizumab |
title_short | Predictors of thrombosis in patients treated with bevacizumab |
title_sort | predictors of thrombosis in patients treated with bevacizumab |
topic | Bevacizumab Thrombosis Proteinuria Hypertension Hyperlipidemia |
url | http://www.sciencedirect.com/science/article/pii/S266657272100064X |
work_keys_str_mv | AT jessicasparks predictorsofthrombosisinpatientstreatedwithbevacizumab AT xiaoyongwu predictorsofthrombosisinpatientstreatedwithbevacizumab AT mikakessansknable predictorsofthrombosisinpatientstreatedwithbevacizumab AT sheshnrai predictorsofthrombosisinpatientstreatedwithbevacizumab AT viveksharma predictorsofthrombosisinpatientstreatedwithbevacizumab |