Haematology specimen acceptability: a national survey in Chinese laboratories

Ye, Yuanyuan; Wang, Wei; Zhao, Haijian; He, Falin; Zhong, Kangping; Yuan, Shuai; Du, Yuxuan; Chen, Bingquan; Wang, Zhiguo

doi:10.11613/bm.2018.030704

Cited by 6 publications

(10 citation statements)

References 8 publications

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“…The sigma metrics value quality management workflow chart for the endocrine analytes in daily QC work was finally formulated and summarized (Figure 5). In clinical laboratories, sigma metrics had been widely used to evaluate and improve the quality of preanalytical, analytical, and postanalytical processes 7,25,26 …”

Section: Discussionmentioning

confidence: 99%

“…The use of sigma (σ) metrics is a great success in the areas of customer satisfaction and global profitability, 4 It was introduced into clinical laboratories by David Nevalainen1 in 2000. 5 Currently, sigma metrics had been widely used in many aspects of laboratory quality management including pre-analytic, 6,7 analytic, [8][9][10] and post-analytic 11 phases of testing. The analytical performance of analytes is quantitatively estimated as a sigma value.…”

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confidence: 99%

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Evaluation of the analytical performance of endocrine analytes using sigma metrics

Yanming

Cao

Liu

et al. 2020

Clinical Laboratory Analysis

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Background (a) To evaluate the clinical performance of endocrine analytes using the sigma metrics (σ) model. (b) To redesign quality control strategies for performance improvement. Methods The sigma values of the analytes were initially evaluated based on the allowable total error (TEa), bias, and coefficient of variation (CV) at QC materials level 1 and 2 in March 2018. And then, the normalized QC performance decision charts, personalized QC rules, quality goal index (QGI) analysis, and root causes analysis (RCA) were performed based on the sigma values of the analytes. Finally, the sigma values were re‐evaluated in September 2018 after a series of targeted corrective actions. Results Based on the initial sigma values, two analytes (FT3 and TSH) with σ > 6, only needed one QC rule (13S) with N2 and R500 for QC management. On the other hand, seven analytes (FT4, TT4, CROT, E2, PRL, TESTO, and INS) with σ < 4 at one QC material level or both needed multiple rules (13S/22S/R4S/41S/10X) with N6 and R10‐500 depending on different sigma values for QC management. Subsequently, detailed and comprehensive RCA and timely corrective actions were performed on all the analytes base on the QGI analysis. Compared with the initial sigma values, the re‐evaluated sigma metrics of all the analytes increased significantly. Conclusions It was demonstrated that the combination of sigma metrics, QGI analysis, and RCA provided a useful evaluation system for the analytical performance of endocrine analytes.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Evaluation of the analytical performance of endocrine analytes using sigma metrics

Yanming

Cao

Liu

et al. 2020

Clinical Laboratory Analysis

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“…However, further studies are needed to confirm the validity of this eventuality. A cross-sectional study carried out in 2018 in China by Ye et al's team [5], including 1586 laboratories, during a period of one month, showed a rate of 0.001% of time-lapse between the blood collection and the receiving the tubes at the treatment room. This very low rate has been explained by the attention given to samples and their reception over the last 5 years, which has generally minimized the rejections due to pre-analytical errors.…”

Section: Discussionmentioning

confidence: 99%

“…The rate of under-filling tubes from services (28%) was higher than that of tubes received from the blood drawing room (19%). Our rates are high by comparing them to the rates of similar studies [5][6][7][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Discussionmentioning

confidence: 99%

Pre-analytical Phase in Hemostasis: The Main Anomalies and Means to Correct Them

Siham¹,

Ikhlas²,

Raihane³

et al. 2019

AJLM

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Pre-analytical errors still represent nearly 70% of all errors occurring in the laboratory, constituting a danger, a waste of time and an additional cost to the patient. The control of the different components of the pre-analytical step is important for the validity of the hemostasis exploration tests. The purpose of our work is to identify the main anomalies of the pre-analytical phase in hemostasis and to propose the means to correct them. We conducted a prospective and descriptive study on the pre-analytical phase of hemostasis. It was in the form of a survey, identifying the main errors related to this phase. It was performed at the Hematology laboratory of the Avicenna Military Hospital of Marrakech and spread over a period of 4 weeks. Our investigation took place at the hemostasis room, which received the collection tubes from the various hospital departments and the blood drawing room (for non-hospitalized patients). The hemostasis room received 400 prescription cards and their corresponding tubes. The parameters related to the prescription file: full name and gender of the patients, were mentioned on all the cards received and they were in conformity with those marked on the corresponding tube. The age of the patients and their clinical and therapeutic informations were mentioned in 73% and 13% of the exam requests, respectively. For the pre-analytical hemostasis parameters related to the blood collection: 63% of samples were taken at the laboratory's blood drawing room, while 37% came from the various hospital departments. Time of the realization of the samples was not mentioned on the cards or on the labels of the tubes. The anticoagulant used for all samples was sodium citrate at a concentration of 3.8%. The filling of the tubes was noncompliant in 22.25%. Registration and triage of the tubes systems were manual. Centrifugation was carried out at a rotation speed of 5000 G for 5 minutes and at a temperature set at 22°C. Hemolyzed samples accounted for 3% of the tubes.

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“…Clotted specimens are the most common reason for rejection for automated counting and coagulation. In a large study in China of a total of approximately 10 million haematology samples collected, 57% of the 11 000 rejections were due to specimen clotting, and similar proportions (43%‐51%) of specimen rejections because of clotting have been reported in a number of other studies. This high rate of clotted samples is mainly the result of poor phlebotomy and inadequate specimen mixing postcollection.…”

Section: The Preanalytical Phase: From the Patient To The Laboratory mentioning

confidence: 99%

Pre‐ and postanalytical errors in haematology

Salle

2019

Int J Lab Hematology

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The majority of errors in laboratory medicine occur in the pre‐ and postanalytical phases of the testing process. Although the causes of these errors are largely common to all laboratory medicine specialties, it is important for the haematology laboratory to understand the particular impact of some on automated counting. The preanalytical phase is the stage of greatest risk but preanalytical errors may go undetected until postanalytical validation and interpretation. The challenges in the postanalytical phase include the standardisation of reference intervals against which results can be interpreted and the impact of just a small difference in reference interval for a key analyte such as haemoglobin concentration. Quality indicators against which pre‐ and postanalytical error incidence are measured are a source of information that can be used to improve services but laboratories struggle to collect good quality data.

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Haematology specimen acceptability: a national survey in Chinese laboratories

Cited by 6 publications

References 8 publications

Evaluation of the analytical performance of endocrine analytes using sigma metrics

Evaluation of the analytical performance of endocrine analytes using sigma metrics

Pre-analytical Phase in Hemostasis: The Main Anomalies and Means to Correct Them

Pre‐ and postanalytical errors in haematology

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