COX regression analysis influence factors of the intra-laboratory turnaround time
10.3760/cma.j.issn.1009-9158.2015.08.017
- VernacularTitle: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
- Author:
Zhihui YIN
;
Li LIU
;
Jianhong ZHAO
- Publication Type:Journal Article
- Keywords:
Clinical laboratory techniques;
Specimen handling;
Clinical laboratory information systems;
Time;
Proportional hazards models
- From:
Chinese Journal of Laboratory Medicine
2015;(8):573-576
- CountryChina
- Language:Chinese
-
Abstract:
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 right 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 Operator 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 processing for improvement.Thus, significant decrease on ILTAT would be achieved.
