Pre-treatment red blood cell distribution width as a predictor of clinically significant prostate cancer.
Int Urol Nephrol 2021;
53:1765-1771. [PMID:
34047898 DOI:
10.1007/s11255-021-02900-z]
[Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 05/22/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE
The present study aims to assess the relationship between red blood cell distribution width (RDW) and clinically significant prostate cancer (csPCa).
METHODS
A total of 458 patients with prostate-specific antigen (PSA) ≤ 10 ng/ml, who subsequently underwent 11-core transperineal template-guided prostate biopsy from June 15, 2015 to November 24, 2020, were included in the present study. Receiver-operating characteristic (ROC)-derived area under the curve analysis was performed to evaluate the predictive accuracy. In addition, univariate and multivariate logistic regression analysis was carried out to determine the association between RDW and csPCa detection.
RESULTS
A total of 89 patients were diagnosed with csPCa, and these patients presented with higher median RDW. The optimal RDW cut-off was set at 12.35%, which gained the maximal Yuden's index. In addition to csPCa, RDW demonstrated a positive correlation with age (r = 0.210, P < 0.001). It was observed that RDW was independent of prostate-specific antigen density for csPCa detection. Compared with the low-RDW group, patients in the high-RDW group had a 1.586-fold increased risk of being diagnosed with csPCa (OR = 2.586, P = 0.007). In the ROC analysis, the accuracy level increased by 3.1% for the prediction of csPCa, when RDW was added to the multivariate logistic model.
CONCLUSION
A high-RDW value is an independent risk factor for csPCa detection. However, more large-scale studies are needed to confirm these findings. If validated, RDW can become an inexpensive, non-invasive, and convenient indicator for csPCa prediction.
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