Exploration of IMDC model in patients with metastatic renal cell carcinoma using targeted agents: a meta-analysis

• Autor(a) principal: Jiang,Guiya
• Data de Publicação: 2020
• Outros Autores: Chen,Shuqiu, Chen,Ming
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: International Braz J Urol (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1677-55382020000300328
• topic: Carcinoma, Renal Cell; Meta-Analysis [Publication Type]; Prognosis

ABSTRACT Purpose: To explore the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model application for predicting outcome of patients with metastatic renal cell carcinoma using targeted agents. Materials and Methods: We performed a literature review of 989 articles. The selecting process used preferred reporting items for systematic reviews and meta-analyses (PRISMA). All included studies were assessed by Newcastle-Ottawa scale. Results of individual studies were pooled using Stata 14.0 software. Results: A total of 17 articles were included. Most articles provided univariate and multivariate analysis of IMDC model prognosis. Combined HRs were 1.58 (95% CI 1.34-1.82) and 3.74 (95% CI 2.67-4.81) for univariate PFS of intermediate to favorable and poor to favorable respectively. In the category of multivariate PFS, combined HRs were 1.27 (95% CI 0.99-1.56) and 2.29 (95% CI 1.65-2.93) with intermediate to favorable and poor to favorable respectively. Regarding univariate OS, combined HRs were 1.93 (95% CI 1.62-2.24) and 6.25 (95% CI 4.18-8.31) with intermediate to favorable and poor to favorable respectively. With multivariate OS, combined HRs were 1.32 (95%CI 1.04-1.59) and 2.35 (95%CI 1.69-3.01) with intermediate to favorable and poor to favorable respectively. Conclusion: In summary, analysis of currently available clinical evidence indicated that IMDC model could be applied to classify patients with metastatic renal cell carcinoma using targeted agents. However, different types of targeted agents and various areas could affect the accuracy of the model. There was also a difference in predicting patients' PFS and OS.