The rise in preanalytical errors during COVID-19 pandemic

Transfusion sample mislabelling and wrong blood in tube in the UK: Insights from the national comparative audits of blood transfusion in 2012 and 2022

BACKGROUND

Samples for transfusion rejected due to mislabelling can lead to harm when a patient has to be re-bled or has a transfusion or procedure delayed. Electronic labelling systems which scan the patient's identification band and generate a label at their side aim to reduce mislabelling and misidentification leading to wrong blood in tube (WBIT) errors. The 2022 National Comparative audit of sample collection aimed to compare national rates of sample mislabelling and WBIT to the 2012 audit and to examine the impact of electronic systems.

METHOD

All UK hospitals were invited to provide data on rejected transfusion samples and WBIT incidents in 1 month (October 2022) and were asked if they had electronic labelling.

RESULTS

Twenty-three thousand five hundred and eighty-four rejected samples were reported by 179 sites in 1 month. The rejection rate of 4.4% represents a 47% increase compared to 2012 (2.99%). There were 92 WBIT incidents, an incidence of 1 in 5882 samples-a 45% increase compared to 1 in 8547 in 2012. Twenty-three percent of sites can print a sample label at the patient's side, up by 224%. The six sites using only electronic sample labelling had a 46.9% lower rejection rate than sites using only hand-labelling but still reported WBIT.

CONCLUSIONS

The increase in sample rejection and WBIT may reflect pressures facing clinical staff, zero tolerance policies and the two-sample rule. A human factors approach to understanding and tackling underlying reasons locally is recommended. Electronic systems are associated with fewer labelling errors, but careful implementation and training is needed to maximise their safety benefits.

Frequency and types of pre-analytical errors in a clinical laboratory of a specialized healthcare hospital

Background & Objective

The pre-analytical phase, encompassing all preparatory steps leading to the analytical process, represents a critical stage prone to laboratory errors. An assessment of the occurrence and categories of laboratory errors, specifically within the pre-analytical phase of laboratory procedures, can guide in taking timely actions for rectifying errors responsible for damage and loss of samples. This study aimed to assess the frequency and types of pre-analytical errors within a clinical laboratory at the Multan Institute of Kidney Diseases over two years.

Methods

This research took place at the Multan Institute of Kidney Diseases. Data was extracted from the hospital laboratory records of the period from 1st January 2021 to 31st December 2022. After data compilation, a retrospective cross-sectional methodology was adopted to assess frequency and types of pre-analytical errors within a clinical laboratory. The records underwent a thorough examination to identify pre-analytical errors, which were classified according to their type and occurrence rate.

Results

Among the 254810 specimens received during the data collection period, a total of 1,722 specimens (0.67% of all collected samples) were found unsuitable for further processing. Amongst the rejected specimens, 718 (41.6%) displayed indications of hemolysis, 388 (22.5%) exhibited clotting, 217 (12.6%) had an insufficient volume and the remaining specimens fell into other miscellaneous categories such as insufficient quantity, unlabeled samples etc.

Conclusion

The overall percentage of sample rejections in the laboratory was 0.67%. This study provided valuable insights into various reasons, and causes that require improvements to enhance the efficiency and quality of laboratory processes.

Design of an algorithm for the detection of intravenous fluid contamination in clinical laboratory samples

OBJECTIVES

Contamination of blood samples from patients receiving intravenous fluids is a common error with potential risk to the patient. Algorithms based on the presence of aberrant results have been described but have the limitation that not all infusion fluids have the same composition. Our objective is to develop an algorithm based on the detection of the dilution observed on the analytes not usually included in infusion fluids.

METHODS

A group of 89 cases was selected from samples flagged as contaminated. Contamination was confirmed by reviewing the clinical history and comparing the results with previous and subsequent samples. A control group with similar characteristics was selected. Eleven common biochemical parameters not usually included in infusion fluids and with low intraindividual variability were selected. The dilution in relation to the immediate previous results was calculated for each analyte and a global indicator, defined as the percentage of analytes with significant dilution, was calculated. ROC curves were used to define the cut-off points.

RESULTS

A cut-off point of 20 % of dilutional effect requiring also a 60 % dilutional ratio achieved a high specificity (95 % CI 91-98 %) with an adequate sensitivity (64 % CI 54-74 %). The Area Under Curve obtained was 0.867 (95 % CI 0.819-0.915).

CONCLUSIONS

Our algorithm based on the global dilutional effect presents a similar sensitivity but greater specificity than the systems based on alarming results. The implementation of this algorithm in the laboratory information systems may facilitate the automated detection of contaminated samples.

Preanalytical Errors in Clinical Biochemistry Laboratory and Relationship With Hospital Departments and Staff: A Record-Based Study

OBJECTIVE

Despite many efforts to improve the overall quality of clinical laboratory service, errors that compromise patient safety and increase healthcare costs continue to occur even infrequently. We aimed to determine the causes of preanalytical errors and related factors by evaluating the laboratory records of a tertiary hospital.

METHODS

In this retrospective study, 7,762,981 requests that were registered in the biochemistry laboratory records of Ondokuz Mayıs University Health Practice and Research Center during 2019 were examined. All rejected samples were analyzed according to the department where they were collected and the reasons for rejection.

RESULTS

Of the total sample rejections, 99,561 (74.8%) were preanalytical and 33,474 (25.2%) belonged to the analytical phase. The preanalytical rejection rate was 1.28%, with the highest rejection rate in the inpatients (2.26%) and the lowest in the outpatients (0.02%). Insufficient sample (43.7%), clotted sample (35.1%), and inappropriate sample (11.1%) were the first 3 rows of rejection reasons. It was determined that sample rejection rates were low during routine working hours and high during nonworking hours.

CONCLUSIONS

Preanalytical errors were most common in inpatient wards and were most likely due to incorrect phlebotomy techniques. Education of health personnel on good laboratory practices, systematic monitoring of errors, and the development of quality indicators will play an important role in reducing the vulnerability of the preanalytical phase.

Impact of COVID-19 on the pre and post analytical clinical laboratory testing processes- A performance evaluation study using six sigma

BACKGROUND

The aim of this study was to determine the impact of COVID-19 pandemic on the total testing process using six sigma metrics based on a comparison of pre and during pandemic periods.

MATERIAL & METHODS

The study duration was over 12 months, 6 months before and 6 months after the COVID-19 onset in Pakistan in March 2020 after the recognition of the first case, using quality indicators (QIs). QIs were chosen from a model of QIs recommended by the CAP. Analysis was performed using Six Sigma calculators and QIs were expressed in percentage (%) and Sigma value were computed. Three levels of performance quality i.e. 25th, 50th, and 75th percentile were derived, being best, common and worst performance respectively. Between-group differences were tested using the Mann-Whitney's U test.

RESULTS

The median defect percentages of these QIs ranged from 0% to 0.27% for the pre-pandemic period and 0% to 0.13% for the during pandemic period. Meanwhile, sigma values of the majority of the QIs were all above 4.0σ during the pre and the pandemic times. For the pre-analytical phase, sigma scores declined for 1 QI, improved for 3 QIs and remained same for 2. In the post analytical phase, no change in sigma metrics was noted for critical values notification. Considerable increase in defect percentage of inappropriate turnaround times was noted.

CONCLUSION

The emergency preparedness proved to be fruitful as depicted by exceptional performance on the sigma metrics for most Qis both prior to and during the pandemic. The pre-analytical and the post analytical phases, being the most error sensitive requires strict vigilance.