scholarly journals Biosafety Risk Assessment of a Clinical Biochemistry Laboratory for SARS-CoV-2 Infection

2021 ◽  
Author(s):  
nergiz zorbozan
Keyword(s):  
Risk Assessment ◽  
Clinical Biochemistry ◽  
Clinical Biochemistry Laboratory

scholarly journals High preanalytical non-compliance and samples rejection rate in clinical biochemistry laboratory are decreased by nurse staff training in phlebotomy and sample handling

2020 ◽  
Vol 13 (3) ◽  
pp. 113-118
Author(s):  
Modibo Coulibaly ◽  
Abdelaye Keita ◽  
Moussa Diawara ◽  
Valentin Sagara ◽  
Brehima Traoré ◽  
...  
Keyword(s):  
Staff Training ◽  
Clinical Biochemistry ◽  
Statistical Significance ◽  
Venous Blood ◽  
Diagnostic Errors ◽  
Hospital Staff ◽  
Clinical Samples ◽  
Sample Handling ◽  
Significant Difference ◽  
Clinical Biochemistry Laboratory

Background: Preanalytical phase of biomedical analysis remains an important source of diagnostic errors that deserves special attention. This study aims to evaluate the training in phlebotomy and sample handling impact on the preanalytical non-compliances. Material and Methods: we performed a prospective study before and after staff training in phlebotomy and sample handling by systematically recording all clinical samples non-compliances. First, we assessed and describe the non-compliance baseline rate from January to December 2017 in the clinical biochemistry laboratory of Hôpital Sominé DOLO de Mopti. After two sessions of one week staff training in January 2018, we performed the same study from January to December 2018. We compared the proportions of non-compliances between the two assessments. Data were collected on the case report forms, captured in Excel and analyzed by R software for (Mac) OS X version 4.0.3. Pearson Ch2 or Fisher exact tests were used for the comparison of proportions. The statistical significance was set at p < 5%. Results: a total of 27,810 venous blood samples were received during the study period; 48% was for biochemistry, 41% for immuno-serology, 9% for blood cell count and 2% for coagulation tests. There were 3,826 instances of preanalytical non-compliances (13.76%) identified that led to sample rejection. Out of the 11 types of non-compliances investigated, 5 (45.4%) accounted for nearly 91% of the problems: insufficient sample volume (28.9%), hemolyzed samples (20.5%), inappropriate collection time (17.8%), sample clot (12.9%), and inappropriate sample collection tube (10.8%). We observed a significant difference in rates of non-compliance between inpatients and outpatients samples (44.4% vs 7.3%; p < 0.001). The proportion of non-compliance have significatively decreased after the two training sessions of hospital staff in phlebotomy and sample handling 3,826/27,810 (13.8%) vs 3,009/32,476 (9.3%); p < 0.001. Conclusion: we report a significantly higher rate of non-compliance in inpatients. Hospital staff training in phlebotomy and sample handling reduce the proportion of preanalytical non-compliance and thereby improve patient management and safety.

scholarly journals Application of Sigma Metrics for Evaluating the Analytical Performance of Thyroid Profile and Cortisol in Clinical Biochemistry Laboratory

2020 ◽  
Author(s):  
Smita Natvarbhai Vasava ◽  
Roshni Gokaldas Sadaria
Keyword(s):  
Quality Control ◽  
Six Sigma ◽  
Clinical Laboratory ◽  
Clinical Biochemistry ◽  
Poor Performance ◽  
Analytical Performance ◽  
Internal Quality ◽  
Six Sigma Methodology ◽  
Clinical Biochemistry Laboratory ◽  
Thyroid Profile

Introduction: Now-a-days quality is the key aspect of clinical laboratory services. The six sigma metrics is an important quality measurement method for evaluating the performance of the clinical laboratory. Aim: To assess the analytical performance of clinical biochemistry laboratory by utilising thyroid profile and cortisol parameters from Internal Quality Control (IQC) data and to calculate sigma values. Materials and Methods: Study was conducted at Clinical Biochemistry Laboratory, Dhiraj General Hospital, Piparia, Gujarat, India. Retrospectively, IQC data of thyroid profile and cortisol were utilised for six subsequent months (July to December 2019). Coefficient of Variation (CV%) and bias were calculated from IQC data, from that the sigma values were calculated. The sigma values <3, >3 and >6 were indicated by poor performance procedure, good performance and world class performance, respectively. Results: The sigma values were estimated by calculating mean of six months. The mean sigma value of Thyroid Stimulating Hormone (TSH) and Cortisol were >3 for six months which indicated the good performance. However, sigma value of Triiodothyronine (T3), Tetraiodothyronine (T4) were found to be <3 which indicated poor performance. Conclusion: Six sigma methodology applications for thyroid profile and cortisol was evaluated, it was generally found as good. While T3 and T4 parameters showed low sigma values which requires detailed root cause analysis of analytical process. With the help of six sigma methodology, in clinical biochemistry laboratories, an appropriate Quality Control (QC) programming should be done for each parameter. To maintain six sigma levels is challenging to quality management personnel of laboratory, but it will be helpful to improve quality level in the clinical laboratories.

scholarly journals The frequency of testing for glycated haemoglobin, HbA1c, is linked to the probability of achieving target levels in patients with suboptimally controlled diabetes mellitus

2018 ◽  
Vol 57 (2) ◽  
pp. 296-304 ◽  
Author(s):  
Christopher J. Duff ◽  
Ivonne Solis-Trapala ◽  
Owen J. Driskell ◽  
David Holland ◽  
Helen Wright ◽  
...  
Keyword(s):  
Diabetes Management ◽  
Clinical Biochemistry ◽  
Healthcare Services ◽  
Glycated Haemoglobin ◽  
Suboptimal Control ◽  
Linear Mixed Effects ◽  
Monitoring Frequency ◽  
Patients With Diabetes ◽  
Clinical Biochemistry Laboratory ◽  
Changes Over Time

Abstract Background We previously showed, in patients with diabetes, that >50% of monitoring tests for glycated haemoglobin (HbA1c) are outside recommended intervals and that this is linked to diabetes control. Here, we examined the effect of tests/year on achievement of commonly utilised HbA1c targets and on HbA1c changes over time. Methods Data on 20,690 adults with diabetes with a baseline HbA1c of >53 mmol/mol (7%) were extracted from Clinical Biochemistry Laboratory records at three UK hospitals. We examined the effect of HbA1c tests/year on (i) the probability of achieving targets of ≤53 mmol/mol (7%) and ≤48 mmol/mol (6.5%) in a year using multi-state modelling and (ii) the changes in mean HbA1c using a linear mixed-effects model. Results The probabilities of achieving ≤53 mmol/mol (7%) and ≤48 mmol/mol (6.5%) targets within 1 year were 0.20 (95% confidence interval: 0.19–0.21) and 0.10 (0.09–0.10), respectively. Compared with four tests/year, having one test or more than four tests/year were associated with lower likelihoods of achieving either target; two to three tests/year gave similar likelihoods to four tests/year. Mean HbA1c levels were higher in patients who had one test/year compared to those with four tests/year (mean difference: 2.64 mmol/mol [0.24%], p<0.001). Conclusions We showed that ≥80% of patients with suboptimal control are not achieving commonly recommended HbA1c targets within 1 year, highlighting the major challenge facing healthcare services. We also demonstrated that, although appropriate monitoring frequency is important, testing every 6 months is as effective as quarterly testing, supporting international recommendations. We suggest that the importance HbA1c monitoring frequency is being insufficiently recognised in diabetes management.

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