Quality indicators in the preanalytical phase of testing in a stat laboratory

Evaluation of Laboratory Performance in Consideration with Pre analytical and Post analytical Quality Indicators

Implementation of Quality indicators (QIs) plays an imperative role in improving the total testing process, as it provides a quantitative basis for evaluating the laboratory performance. Besides monitoring of analytical quality specifications, several lines of experimental and clinical evidence have alluded a pivotal role of extra-analytical phases in improving the quality of laboratory services and therefore a relevance of pre- and post-analytical steps have been speculated on the overall quality in the total testing process and consequently on clinical decision-making. This was a retrospective study designed to evaluate and review different extra-analytical quality indicators in NABL accredited clinical biochemistry laboratory at BJ Medical College and Civil Hospital, Ahmedabad, Gujarat in an endeavour to ameliorate the performance of the laboratory. All Clinical Chemistry Laboratory test requests with their respective samples from January 2018 to December 2021 were included in the study. A total of 1,439,011samples were processed, and were evaluated for seven QIs [(% of number of suitable samples not received; QI-8), (% of number of samples received in inappropriate container; QI-9), (% of number of samples hemolysed; QI-10), (% of number of samples with inadequate sample volume; QI 12) (% of number of samples received mismatched; QI 15), (% of number of samples reported after turnaround time; QI 21) and (% of number of samples with critical values informed; QI 22)] based on defined criteria of Quality Specification given by International Federation of Clinical Chemistry. Total number of preanalytical errors was 53,669 (3.72%). Among the preanalytical errors, inadequate sample volume (2.37% of total samples; 63.49% of total pre-analytical errors) was the most common anomaly followed by Not received samples (24.18%) hemolysis (8.26%) mismatched (3.91%) and 0.14% samples were received in Inappropriate container; manifesting that the error frequency was unacceptable for QI 21 and QI 8, acceptable for QI 10, minimally acceptable for QI 15 and optimum for QI QI 9. Furthermore, there was year-wise progressive decline in error rate of inadequate sample volume, hemolysed sample received and mismatched samples. Total number of post analytical errors were 19,002 (1.32%). TAT outlier and critical values communicated were the two QIs evaluated for this phase and results of both QI were within acceptable limits. Quality indicators serve as a tool to monitor process performance and consequently derived error rates warrant active intervention to improve the laboratory services and patient health care. Dissemination of certified documents, regular staff training and evaluation needs to be conducted.

Analysis of preanalytical errors in a clinical chemistry laboratory: A 2-year study

Patient safety and medical diagnosis of patients are mainly influenced by laboratory results. The present study aimed to evaluate the errors in the preanalytical phase of testing in a Clinical Chemistry diagnostic laboratory. A review was conducted at the Clinical Chemistry Laboratory of a hospital in Saudi Arabia from January 2019 to December 2020. Using the laboratory information system, the data of all canceled tests and requests were retrieved and evaluated for preanalytical errors. A total of 55,345 laboratory test requests and samples from different departments were evaluated for preanalytical errors. An overall rate of 12.1% (6705) was determined as preanalytical errors. The occurrence of these errors was found to be highest in the emergency department (21%). The leading preanalytical errors were nonreceived samples (3.7%) and hemolysis (3.5%). The annual preanalytical errors revealed an increasing rate in outpatient and inpatient departments, while a decreasing rate was observed in the emergency department. An increased rate of errors was also noted for the 2-year study period from 11.3% to 12.9%. The preanalytical phase has a significant impact on the quality of laboratory results. The rate of error in the study was high and the leading causes were nonreceived samples and hemolysis. An increased occurrence of hemolyzed samples in the outpatient department was noted. Enhanced educational efforts emphasizing specimen quality issues and training in sample collection among hospital staff must be carried out.

Detecting Preanalytical Errors Using Quality Indicators in a Hematology Laboratory

BACKGROUND AND OBJECTIVES

Monitoring laboratory performance continuously is crucial for recognizing errors and fostering further improvements in laboratory medicine. This study aimed to review the quality indicators (QIs) and describe the laboratory errors in the preanalytical phase of hematology testing in a clinical laboratory.

METHODS

All samples received in the Hematology Laboratory of the Maternity and Pediatric Hospital in Hail for 3 years were retrospectively reviewed and evaluated for preanalytical issues using a set of QIs. The rate of each QI was compared to the quality specifications cited in the literature.

RESULTS

A total of 95002 blood samples were collected for analysis in the hematology laboratory from January 2017 through December 2019. Overall, 8852 (9.3%) were considered to show preanalytical errors. The most common were "clotted specimen" (3.6%) and "samples not received" (3.5%). Based on the quality specifications, the preanalytical QIs were classified generally as low and medium level of performance. In contrast, the sigma-based performance level indicates acceptable performance on all the key processes. Further analysis of the study showed a decreasing rate of preanalytical errors from 11.6% to 6.5%.

CONCLUSIONS

Preanalytical errors remain a challenge to hematology laboratories. The errors in this case were predominantly related to specimen collection procedures that compromised the specimen quality. Quality indicators are a valuable instrument in the preanalytical phase that allows an opportunity to improve and explore clinical laboratory process performance and progress. Continual monitoring and management of QI data are critical to ensure ongoing satisfactory performance and to enhance the quality in the preanalytical phase.

Preanalytical errors in a satellite stat laboratory: A Six Sigma analysis of seven years' data

BACKGROUND

There have been few reports regarding the frequency and types of preanalytical errors in stat laboratories, in particular those occurring in the satellite laboratory setting. The impact of this error type on laboratory performance in this environment is largely unknown. We assessed the performance of a stat laboratory serving a population of predominantly elderly patients with suspected or established diagnoses of cancer using Six Sigma methodology and compared the results to previous work on this subject.

METHODS

We performed an observational retrospective study using data from the period 2013-2020. The clinical setting was a satellite laboratory supporting an outpatient medical clinic. The type and frequency of each type of preanalytical error were compiled and were used to derive the quarterly error rate. Overall and quarterly performance were calculated using Six Sigma methodology.

RESULTS

During the study period 1314 preanalytical errors were identified from 247,271 laboratory tests (0.5% of total test volume). There was a steady decrease in the error rate over the course of the study period, ranging from 1.4% in 2013 to 0.14% in 2020, despite a 290% increase in quarterly test volume during this period. The most common error types encountered were order error, hemolysis, collection error, and lab accident.

CONCLUSION

1) The overall performance of a satellite laboratory with a stat testing menu is comparable to hospital-based laboratory stat testing. 2) The most frequent error types encountered in satellite laboratory stat testing differ from those found in hospital-based laboratories. 3) There was an overall improvement in laboratory performance based on Six Sigma methodology.

Independent and combined effects of biotin and hemolysis on high-sensitivity cardiac troponin assays

OBJECTIVES

This study compared the independent and combined effects of hemolysis and biotin on cardiac troponin measurements across nine high-sensitivity cardiac troponin (hs-cTn) assays.

METHODS

Parallel cTn measurements were made in pooled lithium heparin plasma spiked with hemolysate and/or biotin using nine hs-cTn assays: Abbott Alinity, Abbott ARCHITECT i2000, Beckman Access 2, Ortho VITROS XT 7600, Siemens Atellica, Siemens Centaur, Siemens Dimension EXL cTnI, and two Roche Cobas e 411 Elecsys Troponin T-hs cTnT assays (outside US versions, with and without increased biotin tolerance). Absolute and percent cTn recovery relative to two baseline concentrations were determined in spiked samples and compared to manufacturer's claims.

RESULTS

All assays except the Ortho VITROS XT 7600 showed hemolysis and biotin interference thresholds equivalent to or greater than manufacturer's claims. While imprecision confounded analysis of Ortho VITROS XT 7600 data, evidence of biotin interference was lacking. Increasing biotin concentration led to decreasing cTn recovery in three assays, specifically both Roche Cobas e 411 Elecsys Troponin T-hs assays and the Siemens Dimension EXL. While one of the Roche assays was the most susceptible to biotin among the nine studied, a new version showed reduced biotin interference by approximately 100-fold compared to its predecessor. Increasing hemolysis also generally led to decreasing cTn recovery for susceptible assays, specifically the Beckman Access 2, Ortho VITROS XT 7600, and both Roche Cobas e 411 Elecsys assays. Equivalent biotin and hemolysis interference thresholds were observed at the two cTn concentrations considered for all but two assays (Beckman Access 2 and Ortho VITROS XT 7600). When biotin and hemolysis were present in combination, biotin interference thresholds decreased with increasing hemolysis for two susceptible assays (Roche Cobas e 411 Elecsys and Siemens Dimension EXL).

CONCLUSIONS

Both Roche Cobas e 411 Elecsys as well as Ortho VITROS XT assays were susceptible to interference from in vitro hemolysis at levels routinely encountered in clinical laboratory samples (0-3 g/L free hemoglobin), leading to falsely low cTn recovery up to 3 ng/L or 13%. While most assays are not susceptible to biotin at levels expected with over-the-counter supplementation, severely reduced cTn recovery is possible at biotin levels of 10-2000 ng/mL (41-8,180 nmol/L) for some assays. Due to potential additive effects, analytical interferences should not be considered in isolation.

Advancing Biomarker Development Through Convergent Engagement: Summary Report of the 2nd International Danube Symposium on Biomarker Development, Molecular Imaging and Applied Diagnostics; March 14-16, 2018; Vienna, Austria

Here, we report on the outcome of the 2nd International Danube Symposium on advanced biomarker development that was held in Vienna, Austria, in early 2018. During the meeting, cross-speciality participants assessed critical aspects of non-invasive, quantitative biomarker development in view of the need to expand our understanding of disease mechanisms and the definition of appropriate strategies both for molecular diagnostics and personalised therapies. More specifically, panelists addressed the main topics, including the current status of disease characterisation by means of non-invasive imaging, histopathology and liquid biopsies as well as strategies of gaining new understanding of disease formation, modulation and plasticity to large-scale molecular imaging as well as integrative multi-platform approaches. Highlights of the 2018 meeting included dedicated sessions on non-invasive disease characterisation, development of disease and therapeutic tailored biomarkers, standardisation and quality measures in biospecimens, new therapeutic approaches and socio-economic challenges of biomarker developments. The scientific programme was accompanied by a roundtable discussion on identification and implementation of sustainable strategies to address the educational needs in the rapidly evolving field of molecular diagnostics. The central theme that emanated from the 2nd Donau Symposium was the importance of the conceptualisation and implementation of a convergent approach towards a disease characterisation beyond lesion-counting "lumpology" for a cost-effective and patient-centric diagnosis, therapy planning, guidance and monitoring. This involves a judicious choice of diagnostic means, the adoption of clinical decision support systems and, above all, a new way of communication involving all stakeholders across modalities and specialities. Moreover, complex diseases require a comprehensive diagnosis by converging parameters from different disciplines, which will finally yield to a precise therapeutic guidance and outcome prediction. While it is attractive to focus on technical advances alone, it is important to develop a patient-centric approach, thus asking "What can we do with our expertise to help patients?"

The impact of biosafety enhancement on stat laboratory quality metrics: Lessons from the COVID-19 pandemic

OBJECTIVE

It is unclear if implementation of biosafety action plans in response to the COVID-19 pandemic has affected laboratory quality metrics.

METHODS

This retrospective study used quality data, including turnaround time (TAT) and number/type of unacceptable specimens from a stat laboratory supporting an outpatient medical clinic serving predominantly elderly cancer patients. Four months of data from the height of the COVID-19 pandemic (March-June 2020) were compared to the same months in 2019.

RESULTS

March-May 2020 test volumes were decreased compared to 2019. June 2020 test volume was slightly increased compared to 2019. TATs in 2020 were similar/ slightly improved compared to the same months in 2019, due to shortened collect to receive and receive to verify TATs. The number and types of unacceptable specimens were similar in 2020 and 2019.

CONCLUSIONS

Despite the challenges to the system caused by the pandemic, laboratory quality metrics were maintained.

Preanalytical Phase Errors: Experience of a Central Laboratory

Introduction: The study intends to observe the frequency of preanalytical phase errors both inside and outside the clinical laboratory according to certain quality indicators (QIs).

Methods: The one-week observation focused on 73 nurses drawing blood from 337 patients. It was performed in two stages: the observation of blood collection up to the receipt of the samples, and the receipt of the samples up to the analytical phase. The data pertaining to the number of patients, tests, and rejection rates were obtained from the laboratory information system (LIS) for the one-week and the one-year period and compared with the observational data.

Results: The process of blood sample collection from 337 patients taken into 1347 tubes was observed. Although the majority of the nurses (78%) used safety needles, the safety mechanism was properly activated only in 38% of the interventions. Evaluation of biochemistry tubes (n=971) revealed the following: the incorrect fill volume error was 40%; the hemolysis was seen by 17%, and the clotted sample and fibrin were observed by 6%. The incorrect fill volume error was 12% and 20% in ethylenediaminetetraacetic acid (EDTA) and citrated tubes, respectively. Clotted samples and platelet clumps were seen in 1% of EDTA tubes.

Conclusion: The study confirms the relative frequency of preanalytical phase error occurring inside and outside of the laboratory.

D-dimer: Preanalytical, analytical, postanalytical variables, and clinical applications

D-dimer is a soluble fibrin degradation product deriving from the plasmin-mediated degradation of cross-linked fibrin. D-dimer can hence be considered a biomarker of activation of coagulation and fibrinolysis, and it is routinely used for ruling out venous thromboembolism (VTE). D-dimer is increasingly used to assess the risk of VTE recurrence and to help define the optimal duration of anticoagulation treatment in patients with VTE, for diagnosing disseminated intravascular coagulation, and for screening medical patients at increased risk of VTE. This review is aimed at (1) revising the definition of D-dimer; (2) discussing preanalytical variables affecting the measurement of D-dimer; (3) reviewing and comparing assay performance and some postanalytical variables (e.g. different units and age-adjusted cutoffs); and (4) discussing the use of D-dimer measurement across different clinical settings.

Retrospective study showed that blood sampling errors risked children's well-being and safety in a Swedish paediatric tertiary care

AIM

Blood analyses containing preanalytical errors (PAEs) are hazardous for patients. This study investigated the frequency of PAEs in blood analysis and the corresponding quality indicators of the sampling process in Swedish paediatric tertiary care.

METHODS

Data were retrieved from the laboratory at Astrid Lindgren Children's Hospital between 2013 and 2014. Preanalytical blood sampling performance was analysed according to the Six Sigma scale, ranging from 0 to 6 (933 137-3.4 defects per million [DPM]).

RESULTS

Of the 1 148 716 analyses, 61 656 (5.4%) were rejected due to PAEs. The PAEs ranged between hospital specialities from 1.9 to 9.4% (p < 0.001) and work shift times, from 6.0% in the day to 5.7% in the evening and 4.3% at night (p values <0.001). Clotting was the most prominent error (51.3%), affecting mostly haematology and coagulation analyses. Incorrectly filled samples represented almost 25% of all PAEs, with effects on chemistry, haematology and coagulation analyses. The sigma score for the overall preanalytical phase (3.2) corresponded to 44 565 DPM.

CONCLUSION

Samples with PAEs were frequently clotted and insufficiently filled, and the distribution of errors varied within working shifts and specific analyses. The overall quality control in paediatric blood sampling was barely acceptable.

The Effects of Education and Training Given to Phlebotomists for Reducing Preanalytical Errors

BACKGROUND

The most common sources of error in the preanalytical phase are considered to be at the stage of patient preparation and sample collection. In order to reduce the preanalytical errors, we aimed to determine the level of phlebotomists knowledge about the preanalytic phase before and after planned trainings in the study.

METHODS

Training about preanalytical processes was given to the 454 health professionals and the majority of them were employed as nurse. Questionnaires before and after training were conducted. In order to assess the effect of the training into the process, preanalytical error rates were calculated before and after training.

RESULTS

The total correct answer rates of vocational school of health diplomaed were statistically lower than the total correct answer rates of other. It was observed significantly increase in the rate of correct answers to questionnaire and significantly decrease in preanalytical error rates after training.

CONCLUSIONS

The results of the survey showed that the attitudes of the phlebotomists were diverse in the preanalytical processes according to the levels of education and their practices. By providing training to all staff on a regular basis, their information about preanalytical phase could be updated and hence, it may possible to significantly reduce the preanalytical errors in health practice and nursing science.

Making sense of a haemolysis monitoring and reporting system: a nationwide longitudinal multimethod study of 68 Australian laboratory participant organisations

Background:

The key incident monitoring and management systems (KIMMS) quality assurance program monitors incidents in the pre- and postanalytical phases of testing in medical laboratories. Haemolysed specimens have been found to be the most frequent preanalytical error and have major implications for patient care. The aims of this study were to assess the suitability of KIMMS for quality reporting of haemolysis and to devise a meaningful method for reporting and monitoring haemolysis.

Methods:

A structured survey of 68 Australian KIMMS laboratory participant organisations was undertaken. Quarterly haemolysis reports (2011–2014) were analysed.

Results:

Among 110 million accessions reported, haemolysis rates varied according to the reporting methods that participants used for assigning accessions (16% of participants reported haemolysis by specimen and 83% reported by episode) and counting haemolysis rejections (61% by specimen, 35% by episode and 3% by test). More than half of the participants (56%) assigned accessions by episode and counted rejections by specimen. For this group, the average haemolysis rate per 100,000 episodes was 177 rejected specimens with the average rate varying from 100 to 233 over time. The majority of participants (91%) determined rejections using the haemolysis index. Two thirds of participants (66%) recorded the haemolysis manually in laboratory information systems.

Conclusions:

KIMMS maintains the largest longitudinal haemolysis database in the world. However, as a means of advancing improvements in the quality of the preanalytical laboratory process, there is a need to standardise reporting methods to enable robust comparison of haemolysis rejection rates across participant laboratories.

Patient identification and tube labelling - a call for harmonisation

Venous blood sampling (phlebotomy) is the most common invasive procedure performed in patient care. Guidelines on the correct practice of phlebotomy are available, including the H3-A6 guideline issued by the Clinical Laboratory Standards Institute (CLSI). As the quality of practices and procedures related to venous blood sample collection in European countries was unknown, the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for the Preanalytical Phase conducted an observational study in 12 European countries. The study demonstrated that the level of compliance of phlebotomy procedures with the CLSI H3-A6 guideline was unacceptably low, and that patient identification and tube labelling are amongst the most critical steps in need of immediate attention and improvement. The process of patient identification and tube labelling is an essential safety barrier to prevent patient identity mix-up. Therefore, the EFLM Working Group aims to encourage and support worldwide harmonisation of patient identification and tube labelling procedures in order to reduce the risk of preanalytical errors and improve patient safety. With this Position paper we wish to raise awareness and provide recommendations for proper patient and sample identification procedures.

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.