【摘要】 目的探讨中性粒-淋巴细胞比值(NLR)预测良性前列腺增生(BPH)疾病进展的价值。方法回顾性分析初诊并经术后病理证实的BPH病人908例的临床资料,分析NLR与前列腺特异性抗原(PSA)、前列腺体积和国际前列腺症状评分(IPSS)间的相关性;根据PSA、前列腺体积及IPSS分为高危组和低危组,比较两组NLR的差异;并用ROC曲线分析NLR对高危组的诊断能力。结果 NLR与PSA、前列腺体积和IPSS均呈正相关(r=0.106 9~0.269 5,P<0.01)。根据前列腺体积及IPSS分组,高危组NLR较低危组高,差异有显著性(t=2.947、2.875,P<0.05);而根据PSA分组,高危组和低危组NLR比较差异无显著性(P>0.05)。ROC曲线分析表明,NLR作为BPH进展的预测因子预测IPSS高危的最佳临界点为1.46,其灵敏度为77%,特异度为38%;预测前列腺体积高危的最佳临界点为1.84,灵敏度为62%,特异度为59%。结论 NLR与BPH进展的高危因素有关,可作为病人随访的指标之一。更多还原

【Abstract】 Objective To investigate the value of neutrophil-lymphocyte ratio(NLR)in predicting the progression of benign prostatic hyperplasia(BPH). Methods A retrospective analysis was performed for the clinical data of 908 patients with an initial diagnosis of BPH which was confirmed by postoperative pathology.The correlation of NLR with prostate-specific antigen(PSA),prostate volume,and International Prostate Symptom Score(IPSS)was analyzed.The patients were divided into high-risk groups and low-risk groups according to PSA,prostate volume,and IPSS,and NLR was compared between these groups.The receiver operating characteristic(ROC)curve was used to analyze the capability of NLR in identifying patients at a high risk of progression. Results NLR was positively correlated with PSA(r=0.106 9,P<0.01),prostate volume(r=0.1677,P<0.01),and IPSS(r=0.269 5,P<0.01).When the patients were divided into groups based on prostate volume and IPSS,the high-risk groups had a significantly higher NLR than the low-risk groups(t=2.947,2.875;P<0.05);when the patients were divided based on PSA,there was no significant difference in NLR between the high-risk group and the low-risk group(P>0.05).The ROC curve analysis showed that NLR had a cut-off value of 1.46,a sensitivity of 77%,and a specificity of 38% in predicting high-risk IPSS,while it had a cut-off value of 1.84,a sensitivity of 62%,and a specificity of 59%in predicting high-risk prostate volume. Conclusion NLR is associated with the high-risk factors for the progression of BPH and can be used as one of the indices for follow-up.更多还原