Governance of preanalytical variability: travelling the right path to the bright side of the moon?

Hemolysis, icterus and lipemia interfere with the determination of two oxidative stress biomarkers in canine serum

BACKGROUND

Oxidative stress has been proven to play a role in numerous human and canine diseases. Among the biomarkers of oxidative stress, Thiobarbituric Acid Reactive Substances (TBARS) and Total Antioxidant Status (TAS) are two of the most widely used. Preanalytical factors are crucial for obtaining accurate results in these assays. Hemolysis, icterus and lipemia (HIL) are common sources of preanalytical errors in the laboratory; however, limited information is available regarding the considerations for canine specimens. Therefore, the objective of this study was to evaluate the potential interferences of HIL in the determination of TBARS and TAS in canine serum.

METHODS

Solutions of pooled canine serum samples were prepared by adding increasing concentrations of hemolysate, bilirubin and a synthetic lipid emulsion. TBARS and TAS were determined, and biases from the control value caused by the interfering substances were calculated.

RESULTS

Hemolysis, icterus and lipemia induced significant interferences on TBARS and TAS, albeit to varying degrees depending on the specific biomarker and interfering substance. TBARS appeared to be more susceptible to interferences in this study. Slight hemolysis, moderate icterus and slight lipemia caused notable deviations in TBARS values, surpassing the acceptable threshold for interference. TAS assay was also affected by HIL, although to a lesser extent compared to TBARS. Significant biases from TAS control value were observed when icterus was moderate, and when hemolysis and lipemia were more pronounced.

CONCLUSIONS

In light of our results, we conclude that hemolyzed, icteric and lipemic specimens are not suitable for TBARS and TAS determination in canine serum. Our findings hold considerable practical utility, as a simple visual inspection would be sufficient for identifying and excluding such specimens.

The rise in preanalytical errors during COVID-19 pandemic

Introduction

The COVID-19 pandemic has posed several challenges to clinical laboratories across the globe. Amidst the outbreak, errors occurring in the preanalytical phase of sample collection, transport and processing, can further lead to undesirable clinical consequences. Thus, this study was designed with the following objectives: (i) to determine and compare the blood specimen rejection rate of a clinical laboratory and (ii) to characterise and compare the types of preanalytical errors between the pre-pandemic and the pandemic phases.

Materials and methods

This retrospective study was carried out in a trauma-care hospital, presently converted to COVID-19 care centre. Data was collected from (i) pre-pandemic phase: 1^st October 2019 to 23^rd March 2020 and (ii) pandemic phase: 24^th March to 31^st October 2020. Blood specimen rejection rate was calculated as the proportion of blood collection tubes with preanalytical errors out of the total number received, expressed as percentage.

Results

Total of 107,716 blood specimens were screened of which 43,396 (40.3%) were received during the pandemic. The blood specimen rejection rate during the pandemic was significantly higher than the pre-pandemic phase (3.0% versus 1.1%; P < 0.001). Clotted samples were the commonest source of preanalytical errors in both phases. There was a significant increase in the improperly labelled samples (P < 0.001) and samples with insufficient volume (P < 0.001), whereas, a significant decline in samples with inadequate sample-anticoagulant ratio and haemolysed samples (P < 0.001).

Conclusion

In the ongoing pandemic, preanalytical errors and resultant blood specimen rejection rate in the clinical laboratory have significantly increased due to changed logistics. The study highlights the need for corrective steps at various levels to reduce preanalytical errors in order to optimise patient care and resource utilisation.

Three years' experience of quality monitoring program on pre-analytical errors in china

Background

Various errors in the procedure of specimen collection have been reported as the primary causes of pre‐analytical errors. The aim of this study was to monitor and assess the reasons and frequencies of rejected samples in China.

Methods

A pre‐analytical external quality assessment (EQA) scheme involving six quality indicators (QIs) was conducted from 2017 to 2019. Rejection rate was calculated for each QI. The difference of the rejection rates over the time was checked by Chi‐square test. Furthermore, the 25th, 50th, and 75th percentiles of the results from total laboratories each year were calculated as optimum, desirable, and minimum level of performance specifications.

Results

In total, 423 laboratories submitted data continuously for six EQA rounds. The overall rejection rates were 0.2042%, 0.1709%, 0.1942%, 0.1689%, 0.1593%, and 0.1491%, respectively. The most common error was sample hemolysed (0.0514%–0.0635%), and the least one was sample not received (0.0008%–0.0014%). A significant reduction in percentages was observed for all QIs. For biochemistry and immunology, hemolysis accounted for more than half of the rejection causes, while for hematology, the primary cause shifted from incorrect fill level to sample clotted. The quality specifications had improved over time, except for the optimum level.

Conclusion

The significant reduction in error rates on sample rejection we observed suggested that laboratories should pay more attention to the standardized specimen collection. We also provide a benchmark for QIs performance specification to help laboratories increase awareness about the critical aspects in the need of improvement actions.

Stability of refrigerated whole blood samples for osmotic fragility test

Background

The osmotic fragility test (OFT), conventionally used for assisting the diagnosis of many erythrocyte disorders, is a manual and time-consuming analysis not daily performed in many medical laboratories. This study was aimed at defining the stability of whole blood samples used for assessing erythrocyte osmotic resistance.

Methods

Twenty-one consecutive routine whole blood samples collected into 5.4 mg K2EDTA were tested immediately after collection (day 0) and at different time intervals afterward (day 1, 2, 3, 4, 7, 10 and 14) after storage at 4 °C. The OFT was performed with the Osmored Monotest (1.3% glycerol; Eurospital, Trieste, Italy). Results at the different time points were compared with those obtained at day 0 and with the reference change value (i.e., 33%).

Results

The median value of both hyperosmolar and hyposmolar resistance increased from baseline, reaching statistical significance at day 7 for hyperosmolar resistance and at day 1 for hyposmolar resistance, respectively. The median relative increase of hemolysis percentage values become greater than the reference change value at day 3 for hyposmolar resistance, while this limit was never overcome for hyperosmolar resistance. A significant inverse association was found between the mean increase in hyperosmolar resistance and the baseline value of hyperosmolar resistance (r = −0.92), mean corpuscular volume (MCV; r = −0.46) or mean corpuscular hemoglobin (MCH; r = −0.44), as well as between the mean increase in hyposmolar resistance and the baseline value of hyposmolar resistance (r = −0.86), or patient age (r = −0.56).

Conclusions

The sample stability seems critical for the OFT. Whole blood specimens should not be stored refrigerated at 4 °C for >2 days before testing.

Reduction of pre-analytical errors in the clinical laboratory at the University Hospital of Korea through quality improvement activities

BACKGROUND

The clinical laboratory is responsible for reporting accurate and expeditious results. However, the pre-analytical phase is directly related to the procedure of specimen collection and is mostly out of the direct control of the laboratory; further, most pre-analytical errors are related to human factors. Therefore, education and training programs for the phlebotomy teams are considered the most significant and necessary measures to reduce these errors.

METHODS

A cross-sectional study was conducted to investigate the types and frequencies of pre-analytical errors in the hospital laboratory. Pre-analytical errors were categorized into four main categories: rejected sample, error related to test ordering, misidentification, and others. Several activities were performed for quality improvement in order to reduce the rates of these errors. The data were analyzed by comparing the pre-intervention and post-intervention results along with the results of questionnaires to assess knowledge to investigate the effects of the activities.

RESULTS

The rates of pre-analytical errors decreased from 0.42% in the pre-intervention period to 0.32% in the post-intervention period. The rejected sample category accounted for the highest rates in the pre- and post-intervention periods. In the questionnaires, the overall average score after the intervention was 71.5, which was a significant increase from 46.0 in the pre-intervention period.

CONCLUSIONS

Each clinical laboratory has various types of pre-analytical errors due to the complexity of the healthcare environment. Therefore, targeted intervention including a quality improvement program and its continuous maintenance should be conducted to reduce pre-analytical errors and to improve patient safety.

Analytical considerations and general diagnostic and therapeutic ramifications of milk hormones during lactation

In this review, we will discuss the changes that occur in the mammary gland from pregnancy to lactation and the issues surrounding the analysis of circulating and milk hormones during the stages of lactogenesis. There is a cascade of events that must occur to achieve milk synthesis, milk ejection, and successful transfer to the breastfeeding infant. The adequacy and success of this process is no small measure and the assessment of milk production, the hormones involved in this process and the ability to properly diagnose conditions and causes of low milk supply are critical for the health and well-being of the mother-infant breastfeeding dyad. The normative data that have been amassed in past decades suggest that there are certain values or circulating concentrations of milk hormones, that if lacking or low, could explain low milk supply status. Yet, in looking more closely at the tests themselves, the certainly of what constitutes "normal" can vary depending on the preanalytical conditions that the blood or milk sample were obtained, the methods used in obtaining circulating or milk concentrations, and the standardization of how that result is expressed. The standardization of these aspects of breast milk physiology are essential for providing important normative data to health care professionals and researchers and will result in more consistent findings across multi-disciplinary platforms.

Novel Opportunities for Improving the Quality of Preanalytical Phase. A Glimpse to the Future?

The preanalytical phase is crucial for assuring the quality of in vitro diagnostics. The leading aspects which contribute to enhance the vulnerability of this part of the total testing process include the lack of standardization of different practices for collecting, managing, transporting and processing biological specimens, the insufficient compliance with available guidelines and the still considerable number of preventable human errors. As in heavy industry, road traffic and aeronautics, technological advancement holds great promise for decreasing the risk of medical and diagnostic errors, thus including those occurring in the extra-analytical phases of the total testing process. The aim of this article is to discuss some potentially useful technological advances, which are not yet routine practice, but may be especially suited for improving the quality of the preanalytical phase in the future. These are mainly represented by introduction of needlewielding robotic phlebotomy devices, active blood tubes, drones for biological samples transportation, innovative approaches for detecting spurious hemolysis and preanalytical errors recording software products.

The economic impact of poor sample quality in clinical chemistry laboratories: results from a global survey

Background Despite advances in clinical chemistry testing, poor blood sample quality continues to impact laboratory operations and the quality of results. While previous studies have identified the preanalytical causes of lower sample quality, few studies have examined the economic impact of poor sample quality on the laboratory. Specifically, the costs associated with workarounds related to fibrin and gel contaminants remain largely unexplored. Methods A quantitative survey of clinical chemistry laboratory stakeholders across 10 international regions, including countries in North America, Europe and Oceania, was conducted to examine current blood sample testing practices, sample quality issues and practices to remediate poor sample quality. Survey data were used to estimate costs incurred by laboratories to mitigate sample quality issues. Results Responses from 164 participants were included in the analysis, which was focused on three specific issues: fibrin strands, fibrin masses and gel globules. Fibrin strands were the most commonly reported issue, with an overall incidence rate of ∼3%. Further, 65% of respondents indicated that these issues contribute to analyzer probe clogging, and the majority of laboratories had visual inspection and manual remediation practices in place to address fibrin- and gel-related quality problems (55% and 70%, respectively). Probe maintenance/replacement, visual inspection and manual remediation were estimated to carry significant costs for the laboratories surveyed. Annual cost associated with lower sample quality and remediation related to fibrin and/or gel globules for an average US laboratory was estimated to be $100,247. Conclusions Measures to improve blood sample quality present an important step towards improved laboratory operations.

Specimen rejection in laboratory medicine: Necessary for patient safety?

INTRODUCTION

The emergency laboratory in Hacettepe University Hospitals receives specimens from emergency departments (EDs), inpatient services and intensive care units (ICUs). The samples are accepted according to the rejection criteria of the laboratory. In this study, we aimed to evaluate the sample rejection ratios according to the types of pre-preanalytical errors and collection areas.

MATERIALS AND METHODS

The samples sent to the emergency laboratory were recorded during 12 months between January to December, 2013 in which 453,171 samples were received and 27,067 specimens were rejected.

RESULTS

Rejection ratios was 2.5% for biochemistry tests, 3.2% for complete blood count (CBC), 9.8% for blood gases, 9.2% for urine analysis, 13.3% for coagulation tests, 12.8% for therapeutic drug monitoring, 3.5% for cardiac markers and 12% for hormone tests. The most frequent rejection reasons were fibrin clots (28%) and inadequate volume (9%) for biochemical tests. Clotted samples (35%) and inadequate volume (13%) were the major causes for coagulation tests, blood gas analyses and CBC. The ratio of rejected specimens was higher in the EDs (40%) compared to ICUs (30%) and inpatient services (28%). The highest rejection ratio was observed in neurology ICU (14%) among the ICUs and internal medicine inpatient service (10%) within inpatient clinics.

CONCLUSIONS

We detected an overall specimen rejection rate of 6% in emergency laboratory. By documentation of rejected samples and periodic training of healthcare personnel, we expect to decrease sample rejection ratios below 2%, improve total quality management of the emergency laboratory and promote patient safety.

Impact of the phlebotomy training based on CLSI/NCCLS H03-a6 - procedures for the collection of diagnostic blood specimens by venipuncture

Introduction:

The activities involving phlebotomy, a critical task for obtaining diagnostic blood samples, are poorly studied as regards the major sources of errors and the procedures related to laboratory quality control. The aim of this study was to verify the compliance with CLSI documents of clinical laboratories from South America and to assess whether teaching phlebotomists to follow the exact procedure for blood collection by venipuncture from CLSI/NCCLS H03-A6 - Procedures for the Collection of Diagnostic Blood Specimens by Venipuncture might improve the quality of the process.

Materials and methods:

A survey was sent by mail to 3674 laboratories from South America to verify the use of CLSI documents. Thirty skilled phlebotomists were trained with the CLSI H03-A6 document to perform venipuncture procedures for a period of 20 consecutive working days. The overall performances of the phlebotomists were further compared before and after the training program.

Results:

2622 from 2781 laboratories that did answer our survey used CLSI documents to standardize their procedures and process. The phlebotomists’ training for 20 days before our evaluation completely eliminated non-conformity procedures for: i) incorrect friction of the forearm, during the cleaning of the venipuncture site to ease vein location; ii) incorrect sequence of vacuum tubes collection; and iii) inadequate mixing of the blood in primary vacuum tubes containing anticoagulants or clot activators. Unfortunately the CLSI H03-A6 document does not caution against both unsuitable tourniquet application time (i.e., for more than one minute) and inappropriate request to clench the fist repeatedly. These inadequate procedures were observed for all phlebotomists.

Conclusion:

We showed that strict observance of the CLSI H03-A6 document can remarkably improve quality, although the various steps for collecting diagnostic blood specimens are not a gold standard, since they may still permit errors. Tourniquet application time and forearm clench should be verified by all quality laboratory managers in the services. Moreover, the procedure for collecting blood specimens should be revised to eliminate this source of laboratory variability and safeguard the quality.

Role of intervention on laboratory performance: evaluation of quality indicators in a tertiary care hospital

Quality in laboratory has huge impact on diagnosis and patient management as 80-90% of all diagnosis is made on the basis of laboratory tests. Monitoring of quality indicators covering the critical areas of pre-analytical, analytical and post-analytical phases like sample misidentification, sample rejection, random and systemic errors, critical value reporting and TATs have a significant impact on performance of laboratory. This study was conducted in diagnostic laboratories receiving approximately 42,562 samples for clinical chemistry, hematology and serology. The list of quality indicators was developed for the steps of total testing process for which errors are frequent and improvements are possible. The trend was observed for all the QI before and after sensitisation of the staff over the period of 12 months. Incomplete test requisition form received in the lab was the most poor quality indicator observed (7.89%), followed by sample rejection rate (4.91%). Most significant improvement was found in pre- and post-analytical phase after sensitisation of staff but did not have much impact on analytical phase. Use of quality indicators to assess and monitor the quality system of the clinical laboratory services is extremely valuable tool in keeping the total testing process under control in a systematic and transparent way.

Preanalytical quality improvement: from dream to reality

Abstract Laboratory diagnostics (i.e., the total testing process) develops conventionally through a virtual loop, originally referred to as "the brain to brain cycle" by George Lundberg. Throughout this complex cycle, there is an inherent possibility that a mistake might occur. According to reliable data, preanalytical errors still account for nearly 60%-70% of all problems occurring in laboratory diagnostics, most of them attributable to mishandling procedures during collection, handling, preparing or storing the specimens. Although most of these would be "intercepted" before inappropriate reactions are taken, in nearly one fifth of the cases they can produce inappropriate investigations and unjustifiable increase in costs, while generating inappropriate clinical decisions and causing some unfortunate circumstances. Several steps have already been undertaken to increase awareness and establish a governance of this frequently overlooked aspect of the total testing process. Standardization and monitoring preanalytical variables is of foremost importance and is associated with the most efficient and well-organized laboratories, resulting in reduced operational costs and increased revenues. As such, this article is aimed at providing readers with significant updates on the total quality management of the preanalytical phase to endeavour further improvement for patient safety throughout this phase of the total testing process.

The prevalence of preanalytical errors in a Croatian ISO 15189 accredited laboratory

BACKGROUND

The preanalytical phase is the most common source of laboratory errors. The goal of this descriptive study was to analyze the prevalence and type of preanalytical errors in relation to the site of sample collection (inpatient vs. outpatient) and the type of laboratory unit (hematology and coagulation vs. biochemistry). For the biochemistry unit, the data were also analyzed relative to the type of the analysis (stat vs. routine).

METHODS

We retrospectively analyzed the sample and test request form error rate for a 1-year period, from January to December 2008.

RESULTS

The frequency of the sample errors differed significantly between the emergency and routine biochemistry unit (0.69% vs. 2.14%; p<0.0001), and between inpatients and outpatients (1.12% vs. 1.36%; p=0.0006). Hemolysis was the most frequent sample error, accounting for 65% of all unsuitable specimens in the emergency biochemistry unit. The total sample error rate did not differ between hematology and coagulation vs. the biochemistry unit. The frequency of test request form errors differed significantly with respect to the sample collection site (p<0.0001), laboratory unit (p<0.0001) and type of the analysis (p<0.0001). Errors in the test request form were least frequent in the outpatient unit (2.98%) and most frequent in the routine biochemistry unit (65.94%).

CONCLUSIONS

Sample and test request form errors in our laboratory are occurring with a frequency comparable to that reported by others. Continuous educational action is needed for all stakeholders involved in laboratory testing to improve the quality of the preanalytical phase of the total testing process.

Effect of pre-analytical errors on quality of laboratory medicine at a neuropsychiatry institute in north India

Advances in instrument technology and automation have simplified tasks in laboratory diagnostics reducing errors during analysis thereby improving the quality of test results. However studies show that most laboratory errors occur in the pre-analytical phase. In view of the paucity of studies examining pre-analytical errors, we examined a total of 1513 request forms received at our laboratory during a 3 month period. The forms were scrutinized for the presence of specific parameters to assess the pre-analytical errors affecting the laboratory results. No diagnosis was provided on 61.20% of forms. Type of specimen was not mentioned in 61.60% of the forms and 89.25% of all forms were illegible. Critical results were encountered in 17.30% of patients, and of these 76.60% were not communicated due to incomplete forms. Thus, by following standard operating procedures vigorously from patient preparation to sample processing the laboratory results can be significantly improved without any extra cost.

Influence of a light meal on routine haematological tests

INTRODUCTION

Patient-related variables, such as physical exercise, stress and fasting status are important sources of variability in laboratory testing. However, no clear indications about fasting requirements exist for routine haematological tests, nor has the influence of meals been assessed.

METHODS

We studied 17 healthy volunteers who consumed a light meal containing a standardized amount of carbohydrates, protein and lipids. Blood was taken for routine haematological tests before the meal and 1, 2 and 4 hours thereafter.

RESULTS

One hour after the meal, neutrophil count and mean corpuscular haemoglobin (MHC) increased significantly, whereas lymphocyte and monocyte counts, red blood cell distribution width, haematocrit, and mean corpuscular volume decreased significantly. A clinically significant variation was only observed for lymphocytes. Two hours after the meal, a significant increase was observed for neutrophils and MCH, whereas lymphocytes, eosinophils, haemoglobin and haematocrit decreased significantly. Clinically significant variations were recorded for lymphocytes, red blood cells (RBC), haemoglobin, haematocrit and MCH. Four hours after the meal MCH was significantly increased, while lymphocytes, eosinophils, RBC, haemoglobin and haematocrit were significantly decreased. Clinically significant variations were recorded for neutrophils, eosinophils, RBC, hematocrit and MCH.

CONCLUSION

The significant variation of several haematological parameters after a light meal demonstrates that the fasting time needs to be carefully considered in order to interpret the results of haematological tests correctly.

Comparison of visual vs. automated detection of lipemic, icteric and hemolyzed specimens: can we rely on a human eye?

BACKGROUND

Results from hemolyzed, icteric, and lipemic samples may be inaccurate and can lead to medical errors. These preanalytical interferences may be detected using visual or automated assessment. Visual inspection is time consuming, highly subjective and not standardized. Our aim was to assess the comparability of automated spectrophotometric detection and visual inspection of lipemic, icteric and hemolyzed samples.

METHODS

This study was performed on 1727 routine biochemistry serum samples. Automated detection was performed using the Olympus AU2700 analyzer. We assessed: 1) comparability of visual and automated detection of lipemic, icteric and hemolyzed samples, 2) precision of automated detection, and 3) inter-observer variability for visual inspection.

RESULTS

Weighted kappa coefficients for comparability of visual and automated detection were: 0.555, 0.529 and 0.638, for lipemic, icteric and hemolyzed samples, respectively. The precision for automated detection was high for all interferences, with the exception of samples being only slightly lipemic. The best overall agreement between observers was present in assessing lipemia (mean weighted kappa=0.698), whereas the lowest degree of agreement was observed in assessing icterus (mean weighted kappa=0.476).

CONCLUSIONS

Visual inspection of lipemic, icteric and hemolyzed samples is highly unreliable and should be replaced by automated systems that report serum indices.

An ignored risk factor in toxicology: The total imprecision of exposure assessment

Quality assurance of exposure biomarkers usually focuses on laboratory performance only. Using data from a prospective birth cohort study in the Faroe Islands, we have assessed the total imprecision of exposure biomarkers. As biomarkers of prenatal methylmercury exposure, mercury concentrations were determined in cord blood, cord tissue, and maternal hair. We determined their mutual correlations and their associations with the child's neurobehavioral effect variables at age 7 years. The exposure biomarkers correlated well with one another, but the cord blood mercury concentration showed the best associations with neurobehavioral deficits. Because at least three exposure parameters were available, factor analysis and structural equation modeling could be applied to determine the total imprecision of each biomarker. For the cord-blood parameter, the total imprecision was 25-30%, and almost twice as much for maternal hair. The total imprecision of these biomarkers much exceeded the normal laboratory variability of less than 5%. Such imprecision can cause underestimation of dose-related toxicity, and data analysis should therefore include sensitivity analyses that take this factor into account. Ignoring preanalytical imprecision may cause serious bias.