Assay validity of point-of-care platelet function tests in thrombocytopenic blood samples

IntroductionPoint-of-care (POC) platelet function tests are faster and easier to perform than in-depth assessment by flow cytometry. At low platelet counts, however, POC tests are prone to assess platelet function incorrectly. Lower limits of platelet count required to obtain valid test results were defined and a testing method to facilitate comparability between different tests was established.Materials and methodsWe assessed platelet function in whole blood samples of healthy volunteers at decreasing platelet counts (> 100, 80-100, 50-80, 30-50 and < 30 x10⁹/L) using two POC tests: impedance aggregometry and in-vitro bleeding time. Flow cytometry served as the gold standard. The number of platelets needed to reach 50% of the maximum function (ED₅₀) and the lower reference limit (ED_ref) were calculated to define limits of test validity.ResultsThe minimal platelet count required for reliable test results was 100 x10⁹/L for impedance aggregometry and in-vitro bleeding time but only 30 x10⁹/L for flow cytometry. Comparison of ED₅₀ and ED_ref showed significantly lower values for flow cytometry than either POC test (P value < 0.05) but no difference between POC tests nor between the used platelet agonists within a test method.ConclusionCalculating the ED₅₀ and ED_ref provides an effective way to compare values from different platelet function assays. Flow cytometry enables correct platelet function testing as long as platelet count is > 30 x10⁹/L whereas impedance aggregometry and in-vitro bleeding time are inconsistent unless platelet count is > 100 x10⁹/L.     

Performance evaluation of the high sensitive troponin I assay on the Atellica IM analyser

IntroductionThe Fourth Universal Definition of Myocardial Infarction Global Taskforce recommends the use of high sensitive troponin (hs-Tn) assays in the diagnosis of acute myocardial infarction. We evaluated the analytical performance of the Atellica IM High-sensitivity Troponin I Assay (hs-TnI) (Siemens Healthcare Diagnostics Inc., Tarrytown, USA) and compared its performance to other hs-TnI assays (Siemens Advia Centaur, Dimension Vista, Dimension EXL, and Abbott Architect (Wiesbaden, Germany)) at one or more sites across Europe.Materials and methodsPrecision, detection limit, linearity, method comparison, and interference studies were performed according to Clinical and Laboratory Standards Institute protocols. Values in 40 healthy individuals were compared to the manufacturer’s cut-offs. Sample turnaround time (TAT) was examined.ResultsImprecision repeatability CVs were 1.1–4.7% and within-lab imprecision were 1.8–7.6% (10.0–25,000 ng/L). The limit of blank (LoB), detection (LoD), and quantitation (LoQ) aligned with the manufacturer’s values of 0.5 ng/L, 1.6 ng/L, and 2.5 ng/L, respectively. Passing-Bablok regression demonstrated good correlations between Atellica IM analyser with other systems; some minor deviations were observed. All results in healthy volunteers fell below the 99th percentile URL, and greater than 50% of each sex demonstrated values above the LoD. No interference was observed for biotin (≤ 1500 µg/L), but a slight bias at 5.0 g/L haemoglobin and 50 ng/L Tn was observed. TAT from was fast (mean time = 10.9 minutes) and reproducible (6%CV).ConclusionsReal-world analytical and TAT performance of the hs-TnI assay on the Atellica IM analyser make this assay fit for routine use in clinical laboratories.     

The rise in preanalytical errors during COVID-19 pandemic

IntroductionThe 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 methodsThis 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.ResultsTotal 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).ConclusionIn 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.     

Haemoglobin A1c-based screening for prediabetes and diabetes mellitus: a multi-center study in Croatian adult population

IntroductionBased on the hypothesis that there is a substantial rate of adults with prediabetes and undiagnosed diabetes mellitus (DM), our aim was to perform haemoglobin A1c (HbA_1c)-based screening in a cohort of Croatian adults and estimate the prevalence of prediabetes and undiagnosed DM according to American Diabetes Association criteria.Materials and methodsThis multi-center, cross-sectional study performed in six Croatian hospitals included 5527 patients aged 40 to 70 years admitted to the Emergency Department or undergoing a primary care check-up. Haemoglobin A1c was measured from leftover whole blood samples using the enzymatic method on either Alinity c or Architect c-series analyser (Abbott Laboratories, Chicago, USA). Haemoglobin A1c between 39-47 mmol/mol was classified as prediabetes, while ≥ 48 mmol/mol as undiagnosed DM.ResultsAfter exclusion of 435 patients with known DM, the final cohort included 5092 patients (median age 57; 56% males). A total of 882 (17.3%) patients had HbA_1c values between 39 and 47 mmol/mol. There were 214 (4.2%) patients with HbA_1c ≥ 48 mmol/mol. Prediabetes prevalence ranged from 14.2% to 20.5%, while undiagnosed DM from 3.3% to 7.3%, with statistically significant differences among settings (P < 0.001). Age-stratified analysis showed that prediabetes and undiagnosed DM prevalence increase with age (P < 0.001), being 25.4% and 5.8%, respectively, in patients aged 60 to 70 years.ConclusionUnderlying impairment of glucose metabolism was identified in about one in five adults, with significant number of patients with already overt DM. These results should serve as a starting point for further steps directed towards promotion of preventive measures for DM in Croatia.     

Verification of a 6-part differential haematology analyser Siemens Advia 2120i

IntroductionThe aim of this study was to perform a comprehensive verification of a 6-part differential haematology analyser Siemens Advia 2120i (Erlangen, Germany), prior to its routine implementation.Materials and methodsOur verification protocol included: precision (within- and between-run), estimated bias (%) as measure of trueness, which was calculated from observed and manufacturers’ declared value, analytical measuring interval (AMI), carryover, confirmation of a limit of blank (LoB), determination of a limit of detection (LoD) and limit of quantitation (LoQ). The K₂ ethylenediaminetetraacetic acid (EDTA) patients’ leftover samples were used for verification of analyser Advia 2021i. Acceptance criteria were based on manufacturer technical specifications (Siemens), 2016 state-of-the-art criteria (Vis and Huisman), and EFLM Biological Variation Database.ResultsThe within- and between-run precision were acceptable for all parameters and the lowest coefficients of variation were observed for mean corpuscular volume (MCV) (0.3% and 0.6%, respectively). Estimated bias was within the acceptance criteria for all parameters except for MCV (the estimated bias was 2.2% (acceptance criteria 2.0%). AMI was confirmed for all tested parameters (r > 0.99). The carryover estimates ranged from 0.1% for platelet count (Plt) to 0.6% for red blood cell count and were within the manufacturers’ specifications (≤ 1%). Manufacturers’ claims for LoB were confirmed for leukocytes, erythrocytes, haemoglobin, and platelets. The estimated LoD and LoQ were 0.05 x10⁹/L and 0.1 x10⁹/L for white blood cell count, while for Plt values were 2 x10⁹/L and 3 x10⁹/L, respectively.ConclusionsAnalytical performance of the Siemens Advia 2120i meets predefined quality goals and is suitable for routine use in a clinical laboratory.Key words: verification, haematology analyser, haematology, blood cell count     

Comparative analyses for beta human chorionic gonadotropin

Serum Beta human chorionic gonadotropin (BhCG) was measured in 25 healthy controls and 75 cases with gestational trophoblastic neoplasms and testicular tumor using RIA kit from BARC, Bombay and ELISA kit from Abbott Diagnostics, USA. Regression Analysis of the values obtained for controls and cases by both the methods were statistically significant P 0.001.     

Moving average procedures as an additional tool for real-time analytical quality control: challenges and opportunities of implementation in small-volume medical laboratories

IntroductionMoving average (MA) is one possible way to use patient results for analytical quality control in medical laboratories. The aims of this study were to: (1) implement previously optimized MA procedures for 10 clinical chemistry analytes into the laboratory information system (LIS); (2) monitor their performance as a real-time quality control tool, and (3) define an algorithm for MA alarm management in a small-volume laboratory to suit the specific laboratory.Materials and methodsMoving average alarms were monitored and analysed over a period of 6 months on all patient results (total of 73,059) obtained for 10 clinical chemistry parameters. The optimal MA procedures were selected previously using an already described technique called the bias detection simulation method, considering the ability of bias detection the size of total allowable error as the key parameter for optimization.ResultsDuring 6 months, 17 MA alarms were registered, which is 0.023% of the total number of generated MA values. In 65% of cases, their cause was of pre-analytical origin, in 12% of analytical origin, and in 23% the cause was not found. The highest alarm rate was determined on sodium (0.10%), and the lowest on calcium and chloride.ConclusionsThis paper showed that even in a small-volume laboratory, previously optimized MA procedures could be successfully implemented in the LIS and used for continuous quality control. Review of patient results, re-analysis of samples from the stable period, analysis of internal quality control samples and assessment of the analyser malfunctions and maintenance log have been proposed for the algorithm for managing MA alarms.     

Utility of icteric index in clinical laboratories: more than a preanalytical indicator

IntroductionTotal bilirubin tests are highly demanded in clinical laboratories. Since icteric index (I-index) has zero cost, we aimed to evaluate its clinical utility and cost-effectiveness to determine if total bilirubin is necessary to be tested. We took into account if haemolysis could interfere to icteric index determination.Material and methodsRetrospectively we reviewed I-index results in two cohorts (43,372 and 8507 non-haemolysed and haemolysed samples, respectively). All determinations were done using Alinity c chemistry analysers (Abbott Diagnostics). Receiver operating characteristic (ROC) curve was used to determine the optimal index cut-off to discriminate between normal and abnormal bilirubin concentration (20.5 µmol/L).ResultsThe ROC curve analysis suggested 21.4 µmol/L as the optimal I-index cut-off but differences in sensitivity and specificity were detected between patient derivation. For rejecting purpose, 15.4 µmol/L and 17.1 µmol/L I-index thresholds were selected based on patient derivation (inpatients and emergency room; and primary care and outpatients, respectively) with 97% sensitivity and 0.25% false negative results. Sensitivity was much lower in haemolysed samples. We selected 34.2 µmol/L I-index as threshold to detect hyperbilirubinemia with 99.7% specificity and 0.26% false positive results, independent of haemolysis. With the icteric index cut-offs proposed, we would save 66% of total bilirubin requested and analyse total bilirubin in around 2% of samples without total bilirubin requested.ConclusionsThis study supports the use of I-index to avoid bilirubin determination and to identify patients with hyperbilirubinemia. This work considers that the economic and test savings could help to increase the efficiency in clinical laboratories.     

Evaluation of the clinical chemistry tests analytical performance with Sigma Metric by using different quality specifications - Comparison of analyser actual performance with manufacturer data

IntroductionThe interest in quality management tools/methodologies is gradually increasing to ensure quality and accurate results in line with international standards in clinical laboratories. Six Sigma stands apart from other methodologies with its total quality management system approach. However, the lack of standardization in tolerance limits restricts the advantages for the process. Our study aimed both to evaluate the applicability of analytical quality goals with Roche Cobas c 702 analyser and to determine achievable goals specific to the analyser used.Materials and methodsThe study examined under two main headings as Sigma_laboratory and Sigma_analyser. Sigma_laboratory was calculated using internal and external quality control data by using Roche Cobas c 702 analyser for 21 routine biochemistry parameters and, Sigma_analyser calculation was based on the manufacturer data presented in the package inserts of the reagents used in our laboratory during the study. Sigma values were calculated with the six sigma formula.ResultsConsidering the total number of targets achieved, Sigma_analyser performed best by meeting all CLIA goals, while Sigma_laboratory showed the lowest performance relative to biological variation (BV) desirable goals.ConclusionsThe balance between the applicability and analytical assurance of “goal-setting models” should be well established. Even if the package insert data provided by the manufacturer were used in our study, it was observed that almost a quarter of the evaluated analytes failed to achieve even “acceptable” level performance according to BV-based goals. Therefore, “state-of-the-art” goals for the Six Sigma methodology are considered to be more reasonable, achievable, and compatible with today’s technologies.     

Verification of Atellica 1500 and comparison with Iris urine analyser and urine culture

IntroductionThe aims of study were to assess: 1) performance specifications of Atellica 1500, 2) comparability of Atellica 1500 and Iris, 3) the accuracy of both analysers in their ability to detect bacteria.Materials and methodsCarryover, linearity, precision, reproducibility, and limit of blank (LoB) verification were evaluated for erythrocyte and leukocyte counts. ICSH 2014 protocol was used for estimation of carryover, CLSI EP15-A3 for precision, and CLSI EP17 for LoB verification. Comparison for quantitative parameters was evaluated by Bland-Altman plot and Passing-Bablok regression. Qualitative parameters were evaluated by Weighted kappa analysis. Sixty-five urine samples were randomly selected and sent for urine culture which was used as reference method to determine the accuracy of bacteria detection by analysers.ResultsAnalytical specifications of Atellica 1500 were successfully verified. Total of 393 samples were used for qualitative comparison, while 269 for sediment urinalysis. Bland-Altman analysis showed statistically significant proportional bias for erythrocytes and leukocytes. Passing-Bablok analysis for leukocytes pointed to significant constant and minor proportional difference, while it was not performed for erythrocytes due to significant data deviation from linearity. Kappa analysis resulted in the strongest agreements for pH, ketones, glucose concentrations and leukocytes, while the poorest agreement for bacteria. The sensitivity and specificity of bacteria detection were: 91 (59-100)% and 76 (66-87)% for Atellica 1500 and 46 (17-77)% and 96 (87-100)% for Iris.ConclusionThere are large differences between Atellica 1500 and Iris analysers, due to which they are not comparable and can not be used interchangeably. While there was no difference in specificity of bacteria detection, Iris analyser had greater sensitivity.     

Coping with Illness Online: The Case of Patients'Online Communities

This article identifies and analyzes characteristics of patients' online communities (POC) in Sweden. Using a qualitative approach, it presents a fine-grained picture of the communities covering both the structural and cultural factors. Acknowledging the debate about quality of health information online and the potential risks of e-health, the article argues that a deeper understanding of the dynamics of POC, which offer unique aides for the coping process of chronically ill patients, can contribute to the design of the emergent forms of Internet-based health care resources.     

Order of draw of blood samples affect potassium results without K-EDTA contamination during routine workflow

IntroductionA specific sequence is recommended for filling blood tubes during blood collection to prevent erroneous test results due to carryover of additives. However, requirement of this procedure is still debatable. This study was aimed to investigate the potassium ethylenediaminetetraacetic acid (K-EDTA) contamination in blood samples taken after a tube containing the additive during routine workflow. The study was also carried out to examine the effect of order of draw on potassium results, regardless of K-EDTA contamination.Materials and methodsIn 388 outpatients, to determine the probability of K-EDTA cross-contamination, blood was drawn sequentially into a serum tube, followed by a tube containing K-EDTA, and by another serum tube. In another 405 outpatients, to evaluate the effect of order of draw blood unrelated to K-EDTA contamination, two serum tube were successively collected. Potassium was measured on Cobas 6000 c501 analyser (Roche Diagnostic GmbH, Mannheim, Germany) by indirect ion selective electrode method.ResultsOf paired samples collected before and after a K-EDTA tube, 24% had a potassium difference of above 0.3 mmol/L. However, no EDTA contamination was detected in these samples as well as 95% confidence intervals (CI) of limits of agreement for calcium were within the allowable error limits based on reference change values. Interestingly, of blood samples drawn successively, 24% had also a difference greater than 0.3 mmol/L for potassium.ConclusionIncorrect order of draw using closed blood collection system does not cause K-EDTA contamination, even in routine workflow. However, regardless of K-EDTA contamination, order of draw has significant influence on the potassium results.     

The response of total testing process in clinical laboratory medicine to COVID-19 pandemic

IntroductionFollowing a pandemic, laboratory medicine is vulnerable to laboratory errors due to the stressful and high workloads. We aimed to examine how laboratory errors may arise from factors, e.g., flexible working order, staff displacement, changes in the number of tests, and samples will reflect on the total test process (TTP) during the pandemic period.Materials and methodsIn 12 months, 6 months before and during the pandemic, laboratory errors were assessed via quality indicators (QIs) related to TTP phases. QIs were grouped as pre-, intra- and postanalytical. The results of QIs were expressed in defect percentages and sigma, evaluated with 3 levels of performance quality: 25^th, 50^th and 75^th percentile values.ResultsWhen the pre- and during pandemic periods were compared, the sigma value of the samples not received was significantly lower in pre-pandemic group than during pandemic group (4.7σ vs. 5.4σ, P = 0.003). The sigma values of samples transported inappropriately and haemolysed samples were significantly higher in pre-pandemic period than during pandemic (5.0σ vs. 4.9σ, 4.3σ vs. 4.1σ; P = 0.046 and P = 0.044, respectively). Sigma value of tests with inappropriate IQC performances was lower during pandemic compared to the pre-pandemic period (3.3σ vs. 3.2σ, P = 0.081). Sigma value of the reports delivered outside the specified time was higher during pandemic than pre-pandemic period (3.0σ vs. 3.1σ, P = 0.030).ConclusionIn all TTP phases, some quality indicators improved while others regressed during the pandemic period. It was observed that preanalytical phase was affected more by the pandemic.     

Age- and Gender-Specific Reference Intervals for Fasting Blood Glucose and Lipid Levels in School Children Measured With Abbott Architect c8000 Chemistry Analyzer

Reference intervals for pubertal characteristics are influenced by genetic, geographic, dietary and socioeconomic factors. Therefore, the aim of this study was to establish age-specific reference intervals of glucose and lipid levels among local school children. This was cross-sectional study, conducted among Saudi school children. Fasting blood samples were collected from 2149 children, 1138 (53%) boys and 1011 (47%) girls, aged 6 to 18 years old. Samples were analyzed on the Architect c8000 Chemistry System (Abbott Diagnostics, USA) for glucose, cholesterol, triglycerides, HDL and LDL. Reference intervals were established by nonparametric methods between the 2.5th and 97.5th percentiles. Significant differences were observed between boys and girls for cholesterol and triglycerides levels in all age groups (P < 0.02). Only at age 6–7 years and at adolescents, HDL and LDL levels were found to be significant (P < 0.001). No significant differences were seen in glucose levels except at age 12 to 13 years. Saudi children have comparable serum cholesterol levels than their Western counterparts. This may reflect changing dietary habits and increasing affluence in Saudi Arabia. Increased lipid screening is anticipated, and these reference intervals will aid in the early assessment of cardiovascular and diabetes risk in Saudi pediatric populations.     

Indirect reference intervals for haematological parameters in capillary blood of pre-school children

IntroductionIndirect estimation of reference intervals (RIs) is straightforward and inexpensive procedure for determination of intra-laboratory RIs. We applied the indirect approach to assess RIs for haematological parameters in capillary blood of pre-school children, using results stored in our laboratory database.Materials and methodsWe extracted data from laboratory information system, for the results obtained by automatic haematology analyser in capillary blood of 154 boys and 146 girls during pre-school medical examination. Data distribution was tested, and logarithmic transformation was applied if needed. Reference intervals were calculated by the nonparametric percentile method.ResultsReference intervals were calculated for: RBC count (4.2-5.4 x10¹²/L), haemoglobin (114-146 g/L), MCH (25.0-29.4 pg), MCHC (321-368 g/L), RDW-SD (36.1-43.5 fL), WBC count (4.5-12.3 x10⁹/L), neutrophils count (1.7-6.9 x10⁹/L) and percentage (29.0-69.0%), lymphocytes count (1.6-4.4 x10⁹/L) and percentage (21.9-60.7%), PLT (165-459 x10⁹/L), MPV (8.1-11.4 fL) and PDW (9.2-14.4%). Gender specific RIs were calculated for monocytes count (male (M): 0.2-1.6 x10⁹/L; female (F): 0.1-1.4 x10⁹/L) and percentage (M: 2.5-18.3%; F: 1.8-16.7%), haematocrit (M: 0.34-0.42 L/L; F: 0.34-0.43 L/L), MCV (M: 73.4-84.6 fL; F: 75.5-84.2 fL) and RDW (M: 12.1-14.3%; F: 11.7-13.9%), due to observed gender differences in these parameters (P = 0.031, 0.028, 0.020, 0.012 and 0.001; respectively). Estimated RIs markedly varied from the literature based RIs that are used in the laboratory.ConclusionsIndirect method employed in this study enables straightforward assessment of RIs in pre-school children. Herein derived RIs differed from the literature-based ones, indicating the need for intra-laboratory determination of RIs for specific populations and sample types.     

Education and communication is the key for the successful management of vitamin D test requesting

Introduction

Pre-preanalytical and post-postanalytical phases are steps where the laboratory professional may play a crucial role. Measuring the serum circulating 25 hydroxyvitamin D level (25(OH)D) is recommended to evaluate vitamin D status in patients at risk for vitamin D deficiency while 1,25 hydroxyvitamin D (1,25(OH)₂D) is only recommended to monitor several particular conditions (chronic kidney disease, hereditary phosphate-losing disorders, and some other) clearly defined by the current clinical guidelines of Endocrine Society.
Our research hypothesis was that through education and communication through comments in the Laboratory Information System (LIS), we could improve appropriateness in the request vitamin D tests.

Materials and methods

A retrospective observational cross-sectional study was conducted from January 2005 to December 2014. Each 1,25(OH)₂D request was reviewed individually by a member of the laboratory staff. Starting in November 2011, each inappropriate 1,25(OH)₂D request was registered in LIS and 25(OH)D was measured instead of 1,25(OH)₂D. We counted the overall number of 1,25(OH)₂D requests and the number of inappropriate requests which then were marked with a comment.

Results

The request of 25(OH)D increased along years. However, 1,25(OH)₂D requests increased until 2012 when demand began to diminish.

Conclusions

Education and communication through comments in the LIS, corrected the inappropriate request of 1,25(OH)₂D and promoted the use of 25(OH)D to study vitamin D deficiency.Key words: clinical laboratory services, utilization, vitamin D, communication, education     

利用H指数与二八法则划分期刊文献核心区的优势比较

【目的】采用H指数和二八法则基于文献的被引频次来划分出期刊文献的核心区,为科研工作者提供一种把握研究热点的新方法。【方法】 以WoS中收录的84种的图书情报学期刊15592篇文献为研究样本,并采用H指数和二八法则基于文献的被引频次来划分出期刊文献的核心区。【结果】采用二八法则划分文献核心区,其核心区文献量占总文献量为6.12%~48.61%,均值为33.81%;而采用H指数划分文献核心区,其核心区文献量占总文献量为0.56%~26.09%,均值为7.80%。对学科采用二八法则划分文献核心区,其核心区文献量占总文献量80%的文献量为38.05%;采用H指数划分文献核心区,其核心区文献占总文献量为0.67%。核心区文献篇均被引频次为H指数划分大于二八法则划分。【结论】采用H指数划分期刊文献核心区比二八法则更具集中趋势,故而H指数能更好地划分出期刊文献核心区。     

Ten years of preanalytical monitoring and control: Synthetic Balanced Score Card Indicator

Introduction

Preanalytical control and monitoring continue to be an important issue for clinical laboratory professionals. The aim of the study was to evaluate a monitoring system of preanalytical errors regarding not suitable samples for analysis, based on different indicators; to compare such indicators in different phlebotomy centres; and finally to evaluate a single synthetic preanalytical indicator that may be included in the balanced scorecard management system (BSC).

Materials and methods

We collected individual and global preanalytical errors in haematology, coagulation, chemistry, and urine samples analysis. We also analyzed a synthetic indicator that represents the sum of all types of preanalytical errors, expressed in a sigma level. We studied the evolution of those indicators over time and compared indicator results by way of the comparison of proportions and Chi-square.

Results

There was a decrease in the number of errors along the years (P < 0.001). This pattern was confirmed in primary care patients, inpatients and outpatients. In blood samples, fewer errors occurred in outpatients, followed by inpatients.

Conclusion

We present a practical and effective methodology to monitor unsuitable sample preanalytical errors. The synthetic indicator results summarize overall preanalytical sample errors, and can be used as part of BSC management system.Key words: Preanalytical phase, errors in laboratory medicine, balanced scorecard, patient safety     

Systematic Differences Between Total and Free Prostate-Specific Antigen Immunoassays: Comparison Using Passing and Bablok Regression

Recent studies have shown that there are systematic differences among total and free prostate-specificantigen (PSA) immunoassays. In this study we analyzedintermethod differences in total PSA (tPSA) and free PSA(fPSA) measurement using ARCHITECT i2000SR (Abbott Diagnostics) and COBAS E601 (Roche Diagnostics). A number of 160 blood samples were tested for tPSA and 50 samples for fPSA (selecting only sampleswith tPSA: 4.1–10.0 μg/L). Passing–Bablok regression analysis was used to compare the two analytical methods fortPSA, fPSA and percentage of fPSA (%fPSA). A strong correlation was noticed between ARCHITECT i2000SR and COBAS E601 for tPSA, fPSA and %fPSA (r between 0.94 and 0.99). Concentrations of tPSA and fPSA measured by COBAS E601 were higher thanthose measured by ARCHITECT i2000SR with a bias of 0.8 μg/L for tPSA and 0.14 μg/L for fPSA. Analyzing therelative difference between methods for fPSA and %fPSA, COBAS E601 exceed a 10% relative difference limit. Our study confirms that there are differences in measured concentrations of tPSA and fPSA byvarious commercial methods. Because clinical judgment on subsequent diagnostic procedures, such as prostatebiopsy, is based on tPSA and fPSA results, tests harmonization should be a priority.     

Validation of a laboratory and hospital information system in a medical laboratory accredited according to ISO 15189

Introduction

The aim of the study was to present a protocol for laboratory information system (LIS) and hospital information system (HIS) validation at the Institute of Clinical Chemistry and Laboratory Medicine of the Merkur University Hospital, Zagreb, Croatia.

Materials and methods:

Validity of data traceability was checked by entering all test requests for virtual patient into HIS/LIS and printing corresponding barcoded labels that provided laboratory analyzers with the information on requested tests. The original printouts of the test results from laboratory analyzer(s) were compared with the data obtained from LIS and entered into the provided template. Transfer of data from LIS to HIS was examined by requesting all tests in HIS and creating real data in a finding generated in LIS. Data obtained from LIS and HIS were entered into a corresponding template. The main outcome measure was the accuracy of transfer obtained from laboratory analyzers and results transferred from LIS and HIS expressed as percentage (%).

Results:

The accuracy of data transfer from laboratory analyzers to LIS was 99.5% and of that from LIS to HIS 100%.

Conclusion:

We presented our established validation protocol for laboratory information system and demonstrated that a system meets its intended purpose.