摘要
目的 研究尿液检验中尿常规和尿沉渣的关系.方法 选取本院2014年5月～2016年5月收治的接受尿液检验的患者80例,依据检测方法将这些患者分为尿常规组(n=40)和尿沉渣组(n=40),对2组患者的取样时间、留取标本时间、获取结果时间、尿蛋白、白细胞、红细胞、结晶、管型检测结果进行统计分析.结果 2组患者的取样时间、留取标本时间、获取结果时间之间的差异无统计学意义;尿常规组患者的尿蛋白、白细胞、红细胞、结晶、管型检测的阳性率分别为15.0%、20.0%、22.5%、27.5%、25.0%,尿沉渣组分别为15.0%、20.0%、25.0%、30.0%、27.5%.2组患者的尿蛋白、白细胞、红细胞、结晶、管型检测阳性率、阴性率之间的差异无统计学意义;2组患者的尿蛋白、白细胞、红细胞、结晶、管型检测的阳性符合率分别为15.0%、17.5%、22.5%、25.0%、22.5%,阴性符合率分别为82.5%、77.5%、72.5%、72.5%、70.0%,检测的总符合率分别为97.5%、95.0%、95.0%、97.5%、92.5%.结论 尿液检验中尿常规和尿沉渣联合检测能够显著提升检测准确率.
Abstract
Objective To study the relationship between urine routine and urinary sediment in urine test.Methods 80 cases of patients accepted urine test in our hospital from May 2014 to May 2016 were selected, these patients were divided into urine routine group (n=40) and urinary sediment group (n=40) two groups according to the detection methods, the sampling collected time, obtained time, Results time, urine protein, white blood cells, red blood cells, crystallization, tube type test results of the two groups were statistically analyzed.ResultsThe differences of sampling collected time, obtained time, results time between the two groups were not significant;the detection positive rates of urinary protein, white blood cells, red blood cells, crystallization, tube type of the urine routine group were 15%, 20%, 22.5%, 27.5%, 25%, respectively, the urine sediment group were 15%, 20%, 25%, 30%, 27.5%, respectively.The differences of detection positive rates of urinary protein, white blood cells, red blood cells, crystallization, tube type between the two groups were not significant;the detection positive coincidence rates of urinary protein, white blood cells, red blood cells, crystallization, tube type of the two groups were 15%, 17.5%, 22.5%, 25% 22.5%, respectively, the negative coincidence rates were 82.5%, 77.5%, 72.5%, 72.5%, 70%, respectively, the total detection rates were 97.5%, 95%, 95%, 97.5%, 92.5%, respectively.Conclusion Combined detection of urine routine and urinary sediment in urine test can significantly improve detection accuracy.
NETL
NSTL
维普
万方数据