实验室内标本周转时间影响因素的COX比例风险回归模型建立与分析

COX regression analysis influence factors of the intra-laboratory turnaround time

目的探讨影响实验室内标本周转时间的危险因素并建立比例风险模型。方法回顾性研究，邢台市第三医院检验科2014年1至6月住院急诊血常规标本，随机选取5周数据，共904份，记录标本信息包括：测定日期、送到时间、审核时间、标本状态、消耗时间、测定时段、操作者、项目组合、延时因素、血小板计数、30min处理结果及测定星期。采用SPSSl7．0进行统计分析，对以上指标先行COX单因素分析，再行逐步COX多因素回归分析。结果规定时间内发送421份，占46．6％；10、20、30、40及50min发送率分别为10．4％、24．7％、46．6％、58．7％和82．1％；单因素COX分析显示：送到时间、标本状态、测定时段、操作者、项目组合、延时因素对标本周转时间延迟有统计学意义均（P均〈0．05）；COX多因素分析显示：正确的送到时间为缩短标本周转时间有利因素（Wald：40．446，P=0．000），非正常上班测定时段、项目组合、体检标本和交接班因素为缩短标本周转时间不利因素（IVald=7．904、38．029、42．874、18．617，P=0．005、0．000、0．000、0．000），5号操作者为缩短标本周转时间有利因素（Wald=11．039，P=0．001），3号和10号操作者为缩短标本周转时间不利因素（Wald=6．432、24．242，P=0．011、0．000），其他操作者则无明显差异（P均〉0．05）。结论送到时间、测定时段、操作者、项目组合、延时因素是导致实验室内标本周转时间延迟的独立危险因素，其他实验室可根据本医院标本运送、检测流程、主要影响因素等确定比例风险模型的变量数量，对标本处理过程各因素量化评价并加以改善，实现实验室内标本周转时间大幅缩短。

Objective To explore the factors influencing the intra-laboratory turnaround time （ILTAT） and establish a COX regression model. Methods Data of 5 weeks with a total of 904 cases from the samples of blood routine examinations from January 2014 to June 2014 in The Third Hospital of Xingtai were randomly collected. The records of the samples included test dates, times of arrival, times of test, sample statuses, time consumption, time duration, operators, project portfolios, delay, PLT counts, results of 30-minute treatment and test weeks. Based on SPSS 17.0, the above indicators were analyzed by COX single factor analysis and then COX mutiple-factor regression analysis. Results Within the prescribed time, 421 cases were sent taking up 46. 6% of the total samples. The ratios of sent cases in 10, 20, 30, 40 and 50 minutes are 10. 4%, 24. 7%, 46. 6%, 58.7% and 82. 1% respectively. The results of COX single factor analysis showed that times of arrival, sample statuses, times of examination, operators, project portfolios and delay had statistical significance for ILTAT （ P 〈 0. 05 ）. The results of COX multiple-factor analysis indicated that fight times of arrival had a positive impact in reducing the turnaround time of samples （Wald = 40. 446 ,P = 0. 000）; non-office hours, project portfolios, physical check samples, and handovers were unfavorable factors to shorten ILTAT （ Wald = 7. 904,38. 029,42. 874, 18. 617, P = 0. 005,0. 000, 0. 000,0. 000） ; Operator 5 was a favorable factor（ Wald = 11. 039. P =0. 001 ） and Operator 3 and Ooerator 10 were unfavorable factors （ Wald = 6.432,24. 242, P = 0. 011,0. 000 ） ; no obvious discrepancy was observed for other operators （ P 〉 0. 05 ）. Conclusions Times of arrival, times of test, operators, project portfolios and delay were the independent risk factors leading to the delay in ILTAT. Other laboratories could determine the variable number of proportional hazards models based on their sample transport, test procedures and principal influence factors, and carry out quantitative evaluation on the factors in sample nroeessing for imnrovement. Thus. significant decrease on ILTAT would be achieved.