Wataru Fukuokaya1, Takahiro Kimura2, Jun Miki2, Shoji Kimura2, Hisaki Watanabe3, Fan Bo4, Daigo Okada4, Koichi Aikawa3, Atsuhiko Ochi4, Koichiro Suzuki4, Naoki Shiga4, Hirokazu Abe4, Shin Egawa2. 1. Department of Urology, Kameda Medical Center, Kamogawa City, Chiba, Japan; Department of Urology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan. Electronic address: wfukuokaya@gmail.com. 2. Department of Urology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan. 3. Department of Urology, Kameda Medical Center, Kamogawa City, Chiba, Japan; Department of Urology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan. 4. Department of Urology, Kameda Medical Center, Kamogawa City, Chiba, Japan.
Abstract
PURPOSE: To investigate the clinical prognostic value of red cell distribution width (RDW) in patients with non-muscle-invasive bladder cancer (NMIBC). MATERIALS AND METHODS: We retrospectively evaluated 582 consecutive patients with primary NMIBC. The efficacy of preoperative RDW at predicting treatment outcome was assessed. A cut-off point for predicting recurrence was also identified. Uni- and multivariable analyses of time to recurrence (TTR) and progression were conducted. Harrell's concordance index (c-index) was used to evaluate the additive value of RDW to the European Organization of Research and Treatment of Cancer (EORTC) risk scoring model for recurrence. RESULTS: According to the receiver operating characteristic curve of RDW for recurrence, a RDW ≥ 14.5% was classified as high. In the multivariable analysis, a high RDW could independently predict shorter TTR (subdistribution hazard ratio [SHR]: 2.65, 95% confidence interval [CI]: 1.83-3.84, P < 0.001), irrespective of tumor characteristics. No significant relationship was observed between RDW and time to progression (SHR: 1.75, 95% CI: 0.76-4.08, P = 0.19). Adding binary-coded RDW to the EORTC risk scoring model significantly improved its discriminatory performance in assessing recurrence risk (c-index: 0.62, improvement: 0.052, P < 0.001). High RDW was associated with shorter TTR in patients treated with bacillus Calmette-Guerin in the multivariable analysis (SHR: 2.0, 95% CI: 1.01-3.98, P = 0.047). CONCLUSIONS: RDW was an independent, significant prognostic factor of TTR in patients with primary NMIBC. Adding RDW to the EORTC risk model significantly improved the model's predictability for tumor recurrence.
PURPOSE: To investigate the clinical prognostic value of red cell distribution width (RDW) in patients with non-muscle-invasive bladder cancer (NMIBC). MATERIALS AND METHODS: We retrospectively evaluated 582 consecutive patients with primary NMIBC. The efficacy of preoperative RDW at predicting treatment outcome was assessed. A cut-off point for predicting recurrence was also identified. Uni- and multivariable analyses of time to recurrence (TTR) and progression were conducted. Harrell's concordance index (c-index) was used to evaluate the additive value of RDW to the European Organization of Research and Treatment of Cancer (EORTC) risk scoring model for recurrence. RESULTS: According to the receiver operating characteristic curve of RDW for recurrence, a RDW ≥ 14.5% was classified as high. In the multivariable analysis, a high RDW could independently predict shorter TTR (subdistribution hazard ratio [SHR]: 2.65, 95% confidence interval [CI]: 1.83-3.84, P < 0.001), irrespective of tumor characteristics. No significant relationship was observed between RDW and time to progression (SHR: 1.75, 95% CI: 0.76-4.08, P = 0.19). Adding binary-coded RDW to the EORTC risk scoring model significantly improved its discriminatory performance in assessing recurrence risk (c-index: 0.62, improvement: 0.052, P < 0.001). High RDW was associated with shorter TTR in patients treated with bacillus Calmette-Guerin in the multivariable analysis (SHR: 2.0, 95% CI: 1.01-3.98, P = 0.047). CONCLUSIONS: RDW was an independent, significant prognostic factor of TTR in patients with primary NMIBC. Adding RDW to the EORTC risk model significantly improved the model's predictability for tumor recurrence.