Administration of Bevacizumab and the Risk of Chronic Kidney Disease Development in Taiwan Residents: A Population-Based Retrospective Cohort Study.
Lon-Fye LyeRuey-Hwang ChouTsai-Kun WuWu-Lung ChuangStella Chin-Shaw TsaiHeng-Jun LinFuu-Jen TsaiKuang-Hsi ChangPublished in: International journal of molecular sciences (2023)
Vascular endothelial growth factor (VEGF) plays a significant role as a pro-angiogenic and pro-permeability factor within the kidney. Bevacizumab is a pharmaceutical monoclonal anti-VEGF antibody that inhibits the growth of new blood vessels, which blocks blood supply and thereby restricts tumor growth. Thus, we conducted a nationwide study to explore the risk of chronic kidney disease (CKD) development in Taiwan residents after bevacizumab therapy. We drew data from the extensive National Health Insurance Research Database (NHIRD), which encompasses data from >99% of Taiwan's population from 1995 onwards. Individuals who received bevacizumab between 2012-2018 were identified as the bevacizumab cohort, with the index date set at the first usage. We randomly selected dates within the study period for the control group to serve as index dates. We excluded patients with a history of CKD prior to the index date or those <20 years old. In both cohorts, patients' propensity scores matched in a 1:1 ratio based on sex, age, index year, income, urbanization level, comorbidities, and medications. We found patients treated with bevacizumab had a significantly higher risk of contracting CKD than patients without bevacizumab (adjusted hazard ratio = 1.35, 95% confidence interval = 1.35-1.73). The risk of CKD was 1.35-fold higher in participants with bevacizumab treatment than those in the control group. These findings suggest that close monitoring of CKD development after bevacizumab administration is needed.
Keyphrases
- chronic kidney disease
- end stage renal disease
- metastatic colorectal cancer
- vascular endothelial growth factor
- health insurance
- peritoneal dialysis
- ejection fraction
- endothelial cells
- newly diagnosed
- physical activity
- mental health
- big data
- machine learning
- prognostic factors
- quality improvement
- artificial intelligence
- cell therapy