Preanalytical Errors Research Articles

Increased preanalytical errors during Covid-19 Pandemic

The clinical laboratories worldwide have faced numerous obstacles due to the COVID-19 pandemic. In the midst of this outbreak, mistakes that occur during the preanalytical phase of sample collection, transportation, and processing can result in unfavorable clinical outcomes. Therefore, the aim of this investigation was to ascertain and contrast the rate at which blood specimens are rejected by a clinical laboratory and to analyze and compare the types of preanalytical errors during the pre-pandemic and pandemic periods. The present retrospective study was done in Chhatrapati Shivaji Subharti Hospital; India. Information was gathered from two distinct time periods - the pre-pandemic phase, which spanned from December 30th, 2019 to March 22, 2020, and the pandemic phase, which ran from March 23th to November 30, 2020. The rate of rejection for blood samples was established by determining the percentage of blood collection tubes containing preanalytical mistakes out of the total number of tubes received. A total of 102,000 blood samples were examined, out of which 40,800 (40%) were collected during the pandemic. The pandemic phase witnessed a significantly higher rate of rejection of blood samples (4%) as compared to the pre-pandemic phase (1%) (P < 0.001). In both phases, the most common preanalytical error was the clotting of samples. There was a significant increase in improperly labeled samples during the pandemic, while there was a notable decrease in hemolyzed samples. Given the prevailing situation of the Corona pandemic, altered logistics have led to a significant surge in preanalytical errors and consequent rejection of blood specimens in clinical laboratories. Therefore, it is imperative to take corrective measures at different levels to minimize preanalytical errors, which would ultimately enhance patient care and optimize the utilization of resources.

Effect of Pre-Analytical Errors in Laboratory Testing Facilities: The Way Forward

Pre-analytical errors contribute a significant proportion of errors of all major sources of mistakes made in laboratory testing processes and are responsible for several patient safety risks. It contributes to wrong therapeutic interventions, irrelevant follow up laboratory investigations and diagnostic delays, which impact negatively on the economy and laboratory effectiveness of health services. 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. The aim of the study is to determine the nature and the occurrence of pre-analytical errors and recommendations on possible measures to reduce these errors. A total of 300 specimens were randomly sampled from a study population of 600 patients and analyzed for pre-analytical errors. One hundred and eighty-four (184) samples were found unsuitable for further processing accounting for 1.9% of all samples analyzed for pre-analytical errors and sample rejection. Rejections were due to following reasons: hemolysis 21.7 % (40) wrong tubes 19 % (35); clotted blood 17% (32); inappropriate timing of collection 15.7% (29); mislabeled specimens 15.2% (28); insufficient specimen quantity 6.5 % (12) and lipemic specimens 4.3% (8). The overall percentage of rejection was 1.9% and the substantial numbers of the rejected specimens were re-tested. Efforts aimed to reduce the rates of rejected samples can improve the quality of laboratory-based health care response. Keywords: Error, Laboratory, Pre-analytical, Rejection, Specimen.

Quality Management of the Pre-Analytical Phase Errors: Monitoring and Way Forward

Inadequate quality management during laboratory analysis can increase errors, specimen rejection, and economic resources wastages. A laboratory quality management system (QMS) helps to streamline and coordinate all the processes and operations within the lab, ensuring that each step is well planned, controlled, and correctly performed. Quality management ensures that results are accurate, reliable, and obtained under a traceable process that can easily detect errors. The study aimed to investigate quality practice among laboratory workers during pre-analytical phase activities. Unexpected deviation from quality procedures consequently compromise laboratory test results, leading to patient mismanagement and patient safety risks. Analysis of results showed that 97.1% (340 respondents) were knowledgeable on quality management of pre-analytical phase, and 2.9% (10 respondents) were not. 99.1% (347respondents) agreed to availability of quality management materials and 0.9% (3 respondents) had no resources. 4.6% (16 respondents) do not practice quality management system at pre-analytical phase and 94.5% (334 respondents) practiced quality improvements. 3.4% (12 respondents) agreed that quality improvement is the responsibility of phlebotomist and field staff. 96.6% (338 respondents) agreed that quality management is the responsibility of everyone at the facility. There was maximum knowledge of quality management at the facility. Availability of quality management resources at the facility was standard. The practice of quality management was below standard. Facility requires training in quality management. It is recommended that quality management system be integrated into the curriculum of school of health technology to guide graduates at work. Keywords: Quality, Improvement, Patient, Management, System.

Comparison of random urine protein/creatinine ratio with 24-hour urine protein in suspected pre-eclampsia

IntroductionProteinuria is one of the classical criteria for the diagnosis of pre-eclampsia. The gold standard remains the measurement of 24-h urine protein which is time consuming and prone to preanalytical errors. Random urine protein creatinine ratio (UPCR) is endorsed by clinical practice guidelines as a faster alternative. The aim of this study was to evaluate the correlation between the 24-h urine protein excretion and UPCR in the identification of proteinuria in suspected preeclamptic patients. MethodA total of 51 women with suspected pre-eclampsia from the maternal fetal clinic of our institution were retrospectively studied. The correlation between the UPCR in random urine samples and protein excretion in the 24-h urine collection was determined by Deming Regression analysis and Pearson correlation on EP evaluator and SPSS respectively. ResultThere was a significant positive correlation between the numerical values obtained by 24-h urine protein and the UPCR (R = 0.88, P < 0.001). Concordance analysis showed 81.1% positive agreement for proteinuria between methods (>300 mg/24hr and >0.3) and 71.4% negative agreement. The clinical sensitivity and specificity of the UPCR was 74% and 69% respectively. ConclusionOverall, UPCR was well correlated with 24-h urine protein and could be an effective and compliant screening tool to indicate proteinuria in preeclamptic patients.

Hook Effect in Semiquantitative SARS-CoV-2 Antispike Total Antibody Assay.

The hook effect is a common preanalytical error that results in falsely decreased analyte concentrations in immunoassays. We present here an example in a semiquantitative SARS-CoV-2 antispike total antibody assay and report the incidence of this error at our institution. All specimens with initial results within the reportable range of the assay were diluted. Results with higher results upon dilution were determined to have the hook effect. In a subset of specimens, these results were also confirmed as elevated on an alternative SARS-CoV-2 antibody assay. Over 1 month, 12 (9.1%) of 132 results were within the analytical measuring range of the assay. Of these, 11 showed the hook effect and required dilution to obtain accurate results. These represented 8.3% of our total testing volume. The hook effect was detected in a semiquantitative SARS-CoV-2 antispike total antibody assay at a high incidence. This error results in observed concentrations much lower than is accurate. Laboratories should be aware of this issue and consider manually diluting specimens within the reportable range of the assay to detect this issue.

EFFECT OF INTERVENTIONAL STRATEGIES ON THE SAMPLE REJECTION RATE OF COVID-19 SAMPLES

Background and objectives-Preanalytical errors constitute major sources of error among all the parts of total testing process (TTP). This study was aimed to assess the effect of timely interventional strategies on sample rejection rate of Covid-19 samples at Department of Microbiology, MGMMC, Indore. Methods- This is a prospective study conducted from July2021 to September2022in which a total of 3,86,871 samples were analyzed. Result- In the postintervention period, the sample rejection rate was reduced to 0.48%. Conclusion- Preanalytical errors are unavoidable sources of errors, but their frequency can be reduced by continuous monitoring and prompt intervention.

Pitfalls in the diagnosis of hematuria.

Detection of hemoglobin (Hb) and red blood cells in urine (hematuria) is characterized by a large number ofpitfalls. Clinicians and laboratory specialists must be aware of these pitfalls since they often lead to medical overconsumption or incorrect diagnosis. Pre-analytical issues (use of vacuum tubes or urine tubes containing preservatives) can affect test results. In routine clinical laboratories, hematuria can be assayed using either chemical (test strips) or particle-counting techniques. In cases of doubtful results, Munchausen syndrome or adulteration ofthe urine specimen should be excluded. Pigmenturia (caused by the presence of dyes, urinary metabolites such asporphyrins and homogentisic acid, and certain drugs in the urine) can be easily confused with hematuria. The peroxidase activity (test strip) can be positively affected by the presence of non-Hb peroxidases (e.g. myoglobin, semen peroxidases, bacterial, and vegetable peroxidases). Urinary pH, haptoglobin concentration, and urine osmolality may affect specific peroxidase activity. The implementation of expert systems may be helpful in detecting preanalytical and analytical errors in the assessment of hematuria. Correcting for dilution using osmolality, density, or conductivity may be useful for heavily concentrated or diluted urine samples.

Status of Pre-analytical Quality Management of Laboratory Tests at Primary Clinics in Korea.

The quality of laboratory test results is crucial for accurate clinical diagnosis and treatment. Pre-analytical errors account for approximately 60%-70% of all laboratory test errors. Laboratory test results may be largely impacted by pre-analytical phase management. However, primary care clinics currently do not have pre-analytical quality management audit systems. We aimed to understand the current status of pre-analytical quality management in laboratory medicine in Korean primary care clinics. Questionnaires were designed to focus on essential components of the pre-analytical process of primary care clinics. An online survey platform was used to administer the survey to internal medicine or family medicine physicians in primary care clinics. A total of 141 physicians provided a complete response to the questionnaire. In 65.2% of the clinics, patient information was hand-labeled rather than barcoded on the specimen bottles; 14.2% of clinics displayed only one piece of patient information (name or identification number), and 19.9% of clinics displayed two pieces of information. Centrifuges were not available in 29.1% of the clinics. Institutions carrying out the National Health Screening Program (NHSP) used more barcode system and had more centrifuges than institutions that did not carrying out the NHSP. Pre-analytical quality management is inadequate in many primary clinics. We suggest implementation of a mandatory management system, allowing for a pre-analytical quality management to be carried out in primary care clinics.

UNUSUAL COLOR OF URINE AND SERUM, SHOULD ONE INVESTIGATE THE CAUSE OR NOT?

Laboratory diagnosis has become the cornerstone for all clinical diagnosis and treatment, with the advent of genetic workups like DNA sequencing and FISH prognosis of diseases also coming under the umbrella of lab tests. However, the time-tested methods for the analysis of samples before processing (which in the era of accreditation comes under the pre-analytical phase) are still very important in suspecting if not diagnosing disorders apart from preventing pre-analytical errors because of hemolysis, lipemia, QNS, mislabeling, etc. A Yellow serum sample in a rack of tubes is quite common in the laboratory which suggests hyperbilirubinemia. We here like to report a case of 12 years old female child who presented for routine blood and urine testing as she was suffering from low grazed fever, abdominal pain, and loose stool. One look per se the picture favors an infective etiology. But on a collection of specimens the serum turned out to be greenish and the urine too was dark green in color (Fig 1&amp;2) So the questions that we tried to answer by this article are what caused this change in plasma color? what other conditions could cause blood discoloration? And are there concerns or possible interference with laboratory tests? On reviewing the literature, we came across various causes of green discoloration of urine, and interferences encountered routinely which we have discussed in our article. On follow up the child was all right after treatment and has no further episodes of such discoloration

Lab Medicine in Space.

As humanity continues to push towards interplanetary travel and beyond, short- and long-term adaptation of the human body to extreme environmental conditions in space remains a fundamental concern. An astronaut is subjected to physically and mentally enduring situations like microgravity, extreme heat or cold, radiation, isolation, and other unforeseen technical challenges. To manage the physiological impact of these situations, the use of real-time diagnostic laboratory medicine will be necessary for the health of the astronaut. With advancing technology, the recent implementation of point-of-care testing (POCT) on the International Space Station (ISS) provides a means to reduce the need to freeze and send specimens back to earth for analysis. However, the quality of these lab tests in a microgravity environment continues to be investigated. Similar to traditional laboratory medicine, there are laboratory analysis concerns during space travel such as pre-analytical errors, competency, interface issues, specimen type differences, reference intervals, and consumable storage. As the frequency and duration of human spaceflight increases, research in these areas of laboratory testing will be key for space travel and astronaut health. For this Q&A, we invited a diverse group of experts to explore the current and desired future state of laboratory medicine in space as well as innovations that will expand our current horizon within this area of interest.

Keep cool! Observed temperature variations at different process stages of the biobanking workflow – examples from the Leipzig medical biobank

Abstract Objectives Biobanked samples are becoming increasingly important for research outcomes. Most of the biobanking processes (from preparation to storage) are affected by temperature in a time-dependent manner and have a high impact on sample quality. We aimed to validate time and temperature sensitive processes such as sample preparation, transport, sorting, and storage, which have a high impact on sample quality. Methods Temperature was measured using internal or external temperature data loggers. We analysed the temperature and present real data from our sample transport on dry ice and with the CryoPod, from our ultra-low temperature freezers (UTFs) of different manufacturers and cryostores. We also tested sample sorting on dry ice and in a cryogenic workbench. Results In the UTFs, we identified temperature zones with a temperature difference from 4.7 °C up to 20.8 °C across the whole UTF. For sample transport within approximately 30 min we observed temperatures of −80.2 °C ± 4.0 °C and −173.9 °C ± 16.9 °C for dry ice boxes and the CryoPod, respectively. Sorting on dry ice was best in a polystyrene box half-filled with dry ice pellets, although the temperature increased by 7.5 °C within the first 5 min, whereas the temperature in the cryogenic workbench remained stable below −100 °C for hours. Conclusions Time and temperature play a crucial role in the entire biobanking process, with sometimes immense temperature fluctuations in some equipment. We recommend the validation or verification of all equipment and processes used to avoid pre-analytical errors in accordance with DIN EN ISO 20387.

Pre-analytical and analytical variability of reticulocyte counts in dogs.

Automated fluorescence-based haematology analysers are now available for reticulocyte enumeration in veterinary medicine, but manual counting is still largely used. This study aimed to evaluate potential sources of analytical and pre-analytical errors when performing automatedand manual counts. Automated and two-operator double-blind manual reticulocyte counts were performed on 15 blood samples. The intra-assay variation of the automated and manual counts and the interoperator variationin the manual counts were then calculated. In addition, the effects of storage were evaluated using samples refrigerated at 4°C or stored at room temperature for 2, 4, 12, 24, 48 or 72 hours after sampling. Intra-assay coefficients of variation were lower for automated counts than for manual counts. Comparison between automated and mean total manual reticulocyte count showed no significant differences. In both refrigerated samples and those stored at room temperature, an increase in reticulocyte count was recorded only after 72 hours. Staining artefacts occurred only in one stored sample counted manually. The presence of cytoplasmic particles other than RNA can cause misinterpretation of cells, leading to an erroneous reticulocyte count. The use of an automated analyser is preferable for reticulocyte enumeration in dogs. Common storage conditions seem to minimally affect reticulocyte evaluation; however, it is recommended to perform the analysis as soon as possible after sampling.

Analysis of Quality Indicators of the Pre-Analytical Phase on Blood Gas Analyzers, Point-Of-Care Analyzer in the Period of the COVID-19 Pandemic

We evaluated and compared blood gas analysis (EGA) non-conformities (NC) considered operator-dependent performed in Point-Of-Care (POC) analyzer as quality indicators (IQ) of the pre-analytical phase. To this end, four different NC registered in the resuscitation departments of the Hospital Polyclinic Bari from the beginning of the pandemic (March 2020) until February 2022 were evaluated. The results obtained were compared with those recorded in the pre-COVID period (March 2018-February 2020) to check if there were differences in number and type. GEM 4000 series blood gas analyzers (Instrumentation Laboratory, Bedford, MA, United States) are installed with integrated Intelligent Quality Management (iQM®), which automatically identify and log pre-analytical errors. All blood gas analyzers are connected to the company intranet and interfaced with the GEM Web Plus (Werfen Instrumentation Laboratory, Bedford, MA, United States) data management information system, which allows the core laboratory to remotely supervise all decentralized POC stations. The operator-dependent process NC were expressed in terms of absolute and relative proportions (percentiles and percentage changes). For performance evaluation, the Mann-Whitney U test, Chi-squared test and Six-Sigma Metric calculation for performance classification were performed. In the COVID period, 31,364 blood gas tests were performed vs. 16,632 tests in the pre-COVID period. The NC related to the suitability of the EGA sample and manageable by the operators were totals of 652 (3.9%) and 749 (2.4%), respectively, in the pre-COVID and COVID periods. The pre-analytical phase IQs used did not show statistically significant differences in the two periods evaluated. The Sigma evaluation did not show an increase in error rates. Considering the increase in the number of EGAs performed in the two periods, the training procedures performed by the core laboratory staff were effective; the clinical users of the POC complied with the indications and procedures shared with the core laboratory without increasing the operator-dependent NCs. Furthermore, the core laboratory developed monitoring activities capable of guaranteeing the maintenance of the pre-analytical quality.

PERFECTION FROM INCEPTION: STANDARDS FOR BIOCHEMISTRY UNDERGRADUATE CERTIFICATE PRACTICAL

Objectives: The undergraduate biochemistry practical’s are the routine test performed in all the medical laboratories. Few of these have been certified practical by competency-based medical education. This will help the teachers access the depth of theoretical and practical knowledge as well as judge their communication skills. To detect the basic concept of quality control in the biochemistry laboratory. Methods: Quantitative analysis was conducted with a purposive sample, at the Noida International institute of medical sciences by the department of biochemistry. 150 students performed the practical on serum albumin in two batches after the demonstration by the faculty. The results were collected through Google forms. The mean, SD, coefficient of variation, and bias were calculated. Results: Both batches showed significant improvement in repeating the practical. Batch a performance was markedly improved after disclosing the optical density of the standard. Conclusion: Teaching basic quality standards used in the biochemistry laboratory and emphasizing the preanalytical errors made during the 1st year of practical’s, markedly improved the performance.

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

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. 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. 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. 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.

Undefilled blood tube containing EDTA: Is it an inappropriate sample for HbA1c assay?

Blood samples having inappropriate volume are a substantial part of preanalytical errors. Inadequate sample volume for glycated haemoglobin (HbA1c) test may be a common problem of patients with diabetes mellitus having vascular changes. In this study, we compared HbA1c concentrations of underfilled and appropriately filled blood collection tubes. To compare HbA1c concentrations, blood samples were collected into 2 mL tubes containing K3-EDTA from 109 subjects. Two blood samples (underfilled and appropriately filled) were drawn from a patient by the same personnel and materials. HbA1c measurements were assayed on a Cobas 6000 analyser module c 501 (Roche Diagnostics, Mannheim, Germany). The HbA1c% results were compared by t-test and Wilcoxon's signed-rank statistical methods (SPSS Inc., Chicago, USA). Bias analysis was performed using Microsoft Excel 4.0. Underfilled samples were classified three groups (group 1, N = 44; group 2, N = 36; and group 3, N = 29) according to the filling ratio of the samples; 0.5 mL and below (< 25%), 0.5-1.0 mL (25-50%), and 1.0-2.0 mL (> 50%), respectively. When we compared underfilled tubes with pairing filled tubes, there was a statistically significant difference only with tubes filled less than 25% (P = 0.030). Furthermore, we have done bias analysis between paired tubes according to the diagnostic cut-off value of 6.5%. The bias was more prominent in up to 50% underfilled blood tubes (1.1%), when HbA1c concentrations were below the diagnostic cut-off of 6.5%. We suggest that the blood tubes with EDTA for HbA1c measurement should be filled with at least 50% to avoid clinical variations.

Preanalytical Errors in a Hematology Laboratory: An Experience from a Tertiary Care Center

Laboratory errors arise at any stage of testing. Detecting these inaccuracies before results are revealed might delay diagnosis and treatment, causing patient distress. Here, we studied the preanalytical errors in a hematology laboratory. This one-year retrospective analysis was conducted at the laboratory of a tertiary care hospital and included information on blood samples that were taken for hematology tests from both outpatients and inpatients. Laboratory records included sample collection and rejection information. The type and frequency of preanalytical errors were expressed as a proportion of total errors and sample number. Microsoft Excel was utilized to enter data. The results were presented in the form of frequency tables. This research included 67,892 hematology samples. For preanalytical errors, 886 samples (1.3%) were discarded. The most common preanalytical error was insufficient sample (54.17%), and the least common was an empty/damaged tube (0.4%). Erroneous samples in the emergency department were mostly insufficient and clotted, whereas pediatric sample errors were caused by insufficient and diluted samples. Inadequate samples and clotted samples account for the vast majority of preanalytical factors. Insufficiency and dilutional errors were most frequent from pediatric patients. Adherence to best laboratory practices can drastically cut down on preanalytical errors.

An Overview of Complete Blood Count Sample Rejection Rates in a Clinical Hematology Laboratory Due to Various Preanalytical Errors.

Introduction The Chughtai Laboratory collects blood samples for complete blood counts from various hospitals, emergency departments, ICUs, and through home sampling servicesall across the country. The preanalytical phase isan integral componentof laboratory medicine. A laboratory report has a key role in patient treatment and the clinician's decision in the management of the disease. Preanalytical errors are most frequently caused by the absence of a sample and/or inappropriate understanding of a test request, mislabeling, contamination from the sampling site, hemolyzed, clotted, insufficient samples, storage issues, and inappropriate blood to anticoagulant proportion or inappropriate choice of anticoagulant. Objective To identify the cause ofrejection rates ofthe complete blood countsamples and reduce the rejection rates by improving the accuracy of the results and loweringpre-analytical errors. Methods This cross-sectional study was done in the Hematology Department of Chughtai Laboratory's head office in Lahore between 19-06-2021 and 19-10-2021. Simple random sampling was applied to collect the data. About 3 ml of each blood sample was received in an ethylenediaminetetraacetic acid (EDTA) vial, inspected visually, run on Sysmex XN-9000 (Sysmex Corporation,Kobe, Hyogo, Japan), and was reviewed on peripheral smears. Results Out of 231,008 blood samples, 11,897 (5.15%) samples were rejected. The most common pre-analytical mistake was storage issues due to transportation delay (19.45%) followed by wrong medical records (19.16%), diluted samples (16.35%), incorrect tubes (16.01%), hemolyzed samples (15.13%), unlabeled samples (10.01%), and clotted sample (3.88%). Conclusion In the hematology department, the total rejection rate observed during the study period was 5.15%. Recognition of preanalytical errors and avoiding them will help uslower the sample rejection rateand raise the overall quality of laboratory management.

Retrospective analysis of intra-patient laboratory variation demonstrates that the BD Vacutainer® Barricor™ blood collection tube reduces troponin variation

ObjectiveThe BD Vacutainer® Barricor™ plasma blood collection tube uses a mechanical separator during centrifugation to separate plasma from the cellular elements of blood. Compared to use of plasma separator tubes (PST™) with gel, Barricor™ produces a cleaner sample with less residual cellular content. We sought to determine if Barricor™ reduces pre-analytical error compared to PST™. Design & MethodsWe used a model previously published that utilizes serial differences between intra-patient measurements transformed into a Taylor series of variation vs time with the y-intercept equal to the sum of short-term analytic variation, preanalytic variation and biologic variation. The intra-patient variation of chloride, sodium, potassium, and troponin-T (hs-TnT) obtained from the Emergency Department of a large tertiary care center sampled with PST™ (May 2015-April 2018, n = 59,762 specimens) or Barricor™ (May 2018-May 2021, n = 61,512 specimens) was evaluated. All specimens were analyzed on either Roche Modular or Cobas® instruments. For each analyte, pairs of intra-patient results were tabulated and separated by 1 h intervals. The average between-pair variations were then regressed against time. We also determined the number of intra-patient outliers using the reference change value for each analyte. ResultsThe Barricor™ hs-TnT y-intercept (-0.0132) was significantly lower than the PST™ intercept (0.9109; p = 0.022). This was also true for chloride (y-intercept = 1.0067 in Barricor™ and 1.3431 in PST™, p = 0.037). The percentage of hs-TnT outliers was significantly lower in Barricor™ (8.32 %) vs PST™ (12.2 %; p < 0.001). ConclusionThe analytical and biological variations are assumed to be steady over the study periods; we ascribe the difference in the y-intercept to the preanalytical effect of the Barricor™ tube reducing platelets and other cellular debris.

Determination of Platelet Count Estimation Factor on Peripheral Blood Smear Confirmation Using Field Number 22 Microscope

The automatic platelet count sometimes requires confirmation on the peripheral blood smear. Platelet count estimation can also be used for reporting platelet count if an automatic cell counter is not available, with an estimation factor according to the Field Number (FN) of the microscope used. This study aimed to determine the platelet count estimation factor based on peripheral blood smear confirmation using an FN 22 microscope. An observational cross-sectional study was carried out in patients who had routine hematological and peripheral blood smear examinations during September 2021 by determination of platelet count using the automatic cell counter method and an average number of platelet counts per field of view with 100x objective magnification. The estimation factor is the total ratio divided by sample size. The total ratio of 254 samples was 4.086. The platelet count estimation factor was 16, indicating that 1 platelet per field of view was equivalent to 16x103/µL. There was a very strong significant correlation between mean platelet count per field of view and platelet count using the automatic cell counter (p&lt;0.001, R&gt;0.750). The field number is the image diameter of the microscope eyepiece. The latest generations of microscope use FN 20 or more, which provides a wider field of view, enabling the observation of more platelets. Factor estimation was used to determine the estimated platelet count on a peripheral blood smear. A big difference between automatic cell counter and peripheral blood smear might indicate pre-analytic, analytic, and post-analytic errors. The platelet count estimation factor based on peripheral blood smear confirmation using the FN 22 microscope was 16. Each laboratory needs to determine the estimation factor according to the FN microscope used.