Renal adverse reactions of tyrosine kinase inhibitors in the treatment of tumours: A Bayesian network meta-analysis

Objectives: Tumours remain a serious threat to human life. Following rapid progress in oncology research, tyrosine kinase inhibitors have been used to treat multiple tumour types. Given the great influence of kidneys on pharmacokinetics, renal toxicities associated with TKIs have attracted attention. However, the TKIs with the lowest risks of renal impairment are unclear. In this study, we conducted a Bayesian network meta-analysis to compare the incidence of renal impairment among different TKIs in patients with tumours. Methods and analysis: Six databases (PubMed, EMBASE, The Cochrane Library, Chinese National Knowledge Infrastructure, Wanfang Data, and China Biomedical Literature Database) were electronically searched from inception to 1 November 2021 to identify randomized controlled trials on the incidence of renal impairment for different TKIs in patients with tumours. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Then, a pairwise meta-analysis was conducted using Stata version 13, and network meta-analysis within the Bayesian framework was conducted using R software version 3.5.3 with the package “gemtc 0.8–2” recalling JAGS (version 4.3.0). Results: Overall, 34 randomized controlled trials were included in this study. Although renal toxicity was common among patients receiving TKIs, the incidence and severity greatly differed among the drugs and studies. Elevated creatinine and protein levels were the most common nephrotoxic events, whereas haematuria was relatively rare. Among TKIs, nintedanib and ripretinib carried the lowest risks of renal impairment. Conclusion: TKIs displayed different profiles of renal toxicity because of their different targets and underlying mechanisms. Clinicians should be aware of the risks of renal impairment to select the optimal treatment and improve patient adherence to treatment. Systematic Review Registration: [www.crd.york.ac.uk/prospero/], identifier [CRD42022295853].