Mitsui Yozo, Yamabe Fumito, Hori Shunsuke, Uetani Masato, Aoki Hiroshi, Sakurabayashi Kei, Okawa Mizuho, Kobayashi Hideyuki, Nagao Koichi, Nakajima Koichi
Department of Urology, Toho University Faculty of Medicine, Tokyo, Japan.
Front Oncol. 2023 Jun 2;13:1162820. doi: 10.3389/fonc.2023.1162820. eCollection 2023.
This study aimed to identify the prediction accuracy of the combination of C-reactive protein (CRP) albumin ratio (CAR) and time to castration resistance (TTCR) for overall survival (OS) following development of metastatic castration-resistant prostate cancer (mCRPC).
Clinical data from 98 mCRPC patients treated at our institution from 2009 to 2021 were retrospectively evaluated. Optimal cutoff values for CAR and TTCR to predict lethality were generated by use of a receiver operating curve and Youden's index. The Kaplan-Meier method and Cox proportional hazard regression models for OS were used to analyze the prognostic capabilities of CAR and TTCR. Multiple multivariate Cox models were then constructed based on univariate analysis and their accuracy was validated using the concordance index.
The optimal cutoff values for CAR at the time of mCRPC diagnosis and TTCR were 0.48 and 12 months, respectively. Kaplan-Meier curves indicated that patients with CAR >0.48 or TTCR <12 months had a significantly worse OS (both < 0.005). Univariate analysis also identified age, hemoglobin, CRP, and performance status as candidate prognostic factors. Furthermore, a multivariate analysis model incorporating those factors and excluding CRP showed CAR and TTCR to be independent prognostic factors. This model had better prognostic accuracy as compared with that containing CRP instead of CAR. The results showed effective stratification of mCRPC patients in terms of OS based on CAR and TTCR ( < 0.0001).
Although further investigation is required, CAR and TTCR used in combination may more accurately predict mCRPC patient prognosis.
本研究旨在确定C反应蛋白（CRP）与白蛋白比值（CAR）和去势抵抗时间（TTCR）相结合对转移性去势抵抗性前列腺癌（mCRPC）发生后的总生存期（OS）的预测准确性。
回顾性评估了2009年至2021年在本机构接受治疗的98例mCRPC患者的临床数据。通过使用受试者工作特征曲线和尤登指数生成预测致死率的CAR和TTCR的最佳临界值。采用Kaplan-Meier法和OS的Cox比例风险回归模型分析CAR和TTCR的预后能力。然后基于单变量分析构建多个多变量Cox模型，并使用一致性指数验证其准确性。
mCRPC诊断时CAR的最佳临界值和TTCR分别为0.48和12个月。Kaplan-Meier曲线表明，CAR>0.48或TTCR<12个月的患者OS明显更差（均<0.005）。单变量分析还确定年龄、血红蛋白、CRP和体能状态为候选预后因素。此外，纳入这些因素并排除CRP的多变量分析模型显示CAR和TTCR是独立的预后因素。与包含CRP而非CAR的模型相比，该模型具有更好的预后准确性。结果表明，基于CAR和TTCR可对mCRPC患者的OS进行有效的分层（<0.0001）。
尽管需要进一步研究，但联合使用CAR和TTCR可能更准确地预测mCRPC患者的预后。
