Preanalytical Errors Research Articles

Identifying and Addressing Errors in Anatomical Pathology: A Survey Analysis

Abstract Introduction/Objective To get an update on potential sources of pre-analytical, analytical, post-analytical and other sources of errors in anatomic pathology and strategies to mitigate such errors. Methods/Case Report The authors designed a 5-question survey in Google software and shared it widely on social media platforms. Results (if a Case Study enter NA) The survey, which garnered 43 responses from staff across various areas of the anatomic pathology laboratory, revealed significant insights. Labeling and fixation/preservation errors emerged as the most prevalent pre-analytical errors, while discrepancies between gross and microscopic examinations were common analytical errors. Specimen handling, inadequate clinical information, and transportation were the most frequently perceived other potential sources of errors. The implications of these findings are clear: robust protocols with multiple identifiers, staff training, and barcode labeling can significantly reduce patient identification errors. Similarly, standardized collection instructions, pre-labeled containers, and meticulous verification processes can effectively minimize such errors. Validated tests, standardized scoring systems, and proper investigation of quality control discrepancies are key to reducing analytical errors. Documented procedures, staff training, regular equipment calibration, and reagent testing are also crucial. Conclusion This study contributes to the literature on the current state of potential sources of errors in anatomic pathology. The study findings and recommendations on strategies to address such errors would improve the performance of the anatomic pathology laboratory and eventually improve patient care.

A-209 Preanalytical Phase Errors Constitute the Vast Majority of Errors in Clinical Laboratory Testing

Abstract Background Laboratory errors can occur during the preanalytical, analytic, or post-analytic phase of testing and may pose a considerable threat to patient safety. Studies have estimated that pre-analytical error encapsulates ∼45-68% of laboratory errors and occurs in up to 1.52% of specimens. However, limitations of previously published studies include the use of manual error detection, reliance on manual transcription, small study sizes, and exclusion of common errors. Herein, we aim to identify the types and frequency of errors occurring in each phase of clinical laboratory testing using a large retrospective dataset across a broad range of errors detected in clinical laboratories. Methods This study was performed in the Barnes-Jewish Hospital core laboratory which provides testing for a 1,400-bed tertiary care hospital and included all tests performed for clinical chemistry, special chemistry, toxicology, automated hematology, blood gas testing, coagulation, non-infectious serology, infectious disease serology, urinalysis, and send out testing between January 2022 and May 2023. Errors were compiled from two separate sources. The first source of errors was captured from the laboratory information system (LIS, Cerner). These errors were flagged manually by laboratory staff in real time. The second source of error was a daily report manually curated by a trained medial laboratory scientist as part of routine, laboratory operating procedures and standard workflow. Results There were ∼40,000,000 individual results from ∼11,000,000 specimens during the study period and 87,317 errors observed (∼0.8% of specimens). Of these errors, specimens that were hemolyzed sufficiently to impact result but still reportable were the majority (41,047, 47.2%). When excluding minimal hemolysis, there were 46,270 errors documented. Among these, 44,847 (97.0%) were in the preanalytical phase, 451 (1.0%) were in the analytical phase, and 972 (2.0%) occurred in the postanalytical phase. Subcategorization of preanalytical errors revealed that most of the errors were related to hemolyzed samples requiring results to be suppressed (n=19,701), quantity not sufficient samples (n=8,068), clotted specimens (n=5,840), collection errors (e.g., wrong tube type, n=5,780), not on ice (n=1,369) and IV fluid contamination (n=1,122). In the analytical phase, the causes of error were interfering substances (n=279), QC out of range (n=65), instrument problems (n=58) and reagent issues (n=49). Among the postanalytical errors, 581 were comment errors, 216 were results entry errors, 131 were comments errors with potential impact on patient care, and 44 were dilution errors. Conclusions This study demonstrates that the vast majority of lab errors (∼97.0%) occurring during the preanalytical phase. The high incidence of preanalytical errors, especially errors related to sample integrity implies a need for enhanced tools and training to minimizing errors from specimen collections, maintenance of sample stability, transportation, and processing.

A-057 Assessing Direct Renin Stability in EDTA Plasma Prior to Separation from Cells and Post-thaw

Abstract Background Renin is an enzyme secreted by the kidneys responsible for blood pressure regulation through the renin-angiotensin-aldosterone axis. Direct renin measurements are frequently used to investigate underlying causes of hypertension, including primary aldosteronism (PA), which is screened for using an aldosterone:renin ratio (ARR). Appropriate handling of renin samples in the preanalytical phase is critical, as cryoactivation of prorenin can occur when samples are refrigerated or cooled for extended periods of time, falsely elevating results. Manufacturer instructions for renin handling are often poorly defined and there is currently no consensus as to best handling practices. Two variables that frequently lead to renin sample rejections in our laboratory are time to centrifugation after collection and time kept at room temperature (RT). The objective of this study was to evaluate the stability of direct renin under ambient and freeze-thaw conditions in an effort to reduce test cancellations due to improper handling. Methods Plasma samples collected from five volunteers with consent in K2EDTA tubes were used for renin stability studies. Baseline renin samples were kept at RT for two hours after collection, centrifuged, measured, and subsequently frozen for post-thaw studies. To assess stability before separation from cells, samples kept at RT for two and four hours prior to centrifugation were assayed in triplicate for direct renin. For post-thaw stability studies, plasma samples were split into two aliquots each and frozen at -20⁰C for at least 24 hours, but no longer than 30 days. After removal from -20⁰C, one set of aliquots was thawed on the benchtop at RT and the second set was thawed in an RT water bath for up to one hour. Samples were centrifuged and assayed for direct renin in triplicate at baseline and six hours post-thaw. Direct renin was measured using the LIAISON XL chemiluminescent immunoassay (Diasorin). Bias was assessed after each treatment. The total allowable error (TEa) was ±2 pg/mL or ±20%. Results Measured renin concentrations ranged from 6.8-29.7 pg/mL. The percent bias in direct renin concentrations when comparing samples stored for two-hour and four-hour timepoints before centrifugation was 2.0% on average. Post-thaw studies comparing renin concentrations at the baseline and six-hour timepoints revealed an average percent bias of 13.1% and 12.2% for RT and water bath thawing methods, respectively. All samples tested were within the TEa. Conclusions Direct renin is stable in lavender K2EDTA tubes at RT for up to four hours, prior to centrifugation and separation from cells. Renin concentrations remained stable after storage at -20⁰C and thawing at RT for up to six hours, regardless of thawing method. By expanding direct renin stability criteria, we hope to reduce the number of cancellations due to preanalytical handling errors and prevent redundant phlebotomy for patients.

B-108 Delta Check Performance Assessment by Using Real-WorldData at the Temple University Health System​

Abstract Background Delta check is a post-analytic tool focusing on pre-analytical, analytical and post-analytical errors. It can be sensitive in detecting isolated errors affecting a single patient, as opposed to moving patient averages, which is another QC tool for detecting errors that affect a cohort/population of patients. As a computer-based system on selected tests, delta checks are to detect excessive changes from prior value in the patient test result and to detect possible specimen errors such as misidentified or compromised samples before results are reported. It may also play a role in detecting examination(analytical) issues and clinically significant results. If delta checks exceed established limits, the result is flagged. There is a common agreement that measurands with low indices of individuality (<0.6) make good candidates for the identification of cases of sample misidentification than higher indices of individuality (>1.4). CLSI EP33 Use of Delta Checks in the Medical Laboratory guidelines recommends that laboratories assess the validity of current delta check parameters by reviewing retrospective data on flagged results to determine if errors are being identified efficiently. At Temple University Health System, delta check limits were established historically by using published data, however, its performance characteristics have not been verified previously. Verification of clinical performance of predefined delta check limits can be performed either through mathematical calculations or via clinical audits. Clinical audits are achieved by documenting the follow-up actions and outcomes of the samples flagged by a delta check rule(s). It has been suggested that this should be performed using a protocolized follow-up procedure to ensure uniformity in the investigation and management. Corey M. et al. shared an excel template, a mathematical model that can be adopted easily by replacing the example data with retrospectively extracted data and modifying the parameters to suit their laboratory’s requirements. Methods We collected up to 30,000 data points for each 12 measurand in the clinical chemistry lab retrospectively. By using the excel template, clinical sensitivity and clinical specificity has been calculated. Furthermore, we created ROC curves and evaluated them using the (YI) Youden Index and AUC (Area Under the Curve). Results In general, YI values were found to be very low, less than 0.6. Highest YI was used to establish new delta check limits for each analyte; only the current creatinine delta check limit was found as effective, and no change required. 11 out of 12 measurand were required a revision of delta check limits and were proposed to replace with lower delta checks. AUC values were found between 0.56-0.794. Na showed the highest AUC, which correlates its low individuality index (0.5) and its effects on effectiveness of the delta check system. Conclusions It is of vital importance to establish a solid follow-up evaluation for delta check alerts and distinguish between integrity issues and clinically significant changes. Conclusively, this endeavor can develop into extensive information for the application, evaluation, and follow-up of delta checks whilst improving diagnostic accuracy and quality assurance.

A-182 Applications of artificial intelligence optical character recognition in laboratory clerical functions offsetting staffing shortages and error reduction

Abstract Background The clinical laboratory profession has been dealing with a decade long, and now critical retention and staffing problem. The average national turnover rate for laboratory technicians is 17%, and the highest turnover rate exhibited for phlebotomists is 36%. While turnover rates have an impact on a laboratory, or hospitals budget, more importantly they have a substantial impact on quality and error rates, particularly in the pre-analytical stage of testing; with studies showing up to 65.09% of errors impacting the pre-analytical phase. Methods In an effort to remedy this high error rate, a meta-analysis was performed on paper order entry and clerical pre-analytical error, in parallel with performance and accuracy data of advanced optical character recognition driven by artificial intelligence and machine learning (AI OCR). Results AI OCR studies demonstrated an untrained accuracy percentage, to include handwriting of 91.08%, and when trained utilizing both relational and hierarchal logic data bases accuracy increased to 98.4%. The rate at which AI OCR processed and classified medical information was 3-4 pages per second. It is estimated that 99.9%-100% accuracy can be achieved with additional form field training and mapping. The level of accuracy and efficiency for laboratory paper order-entry utilizing AI OCR can also be increased with the addition of natural language processing and data recognition algorithms when combined with logic databases containing information such as medical record numbers, ICD-10 codes, and laboratory test compendiums. Conclusions The performance of this technology demonstrates a strong application to reduce the burden of clerical paper order entry, while addressing the 30.8% dissatisfaction rate regarding manual entry processes among lab professionals surveyed. Moreover, AI OCR clerical data classification, extraction and entry processes augmented with human verification steps examined, demonstrated overall accuracy of 99.99%. The application of AI OCR in laboratory pre-analytical processes has the potential to improve test order accuracy and efficiency, ultimately impacting the quality of patient care and operational costs for healthcare facilities. When examined through the lens of staff retention, studies show that 80% of laboratory employees feel that a workplace with the necessary tools and technology positively impacts their job satisfaction. While organizations investing in technologies that drive both job efficiency and satisfaction see a 125% return on investment through productivity. Demonstrating that the application of sophisticated technologies that both increase productivity and decrease error rates have a substantial impact on job satisfaction and ultimately retention. Although laboratory clerical data and order entry processes were examined for this meta-analysis, the aggregate data compiled showed that the application of AI OCR in clerical processes could also have a substantial impact on the 25.9% annual turnover rate for hospitals as a whole in the United States.

A-116 Pleural Fluid pH - Are We Collecting and Measuring It Correctly?

Abstract Background Pleural fluid pH has diagnostic, therapeutic, and prognostic implications in the management of pleural space infections and malignancy. Pleural fluid sample collection, transportation and processing are known for source of pre-analytical error which contribute to inaccuracy of measured pH with clinical implication to patient care. Some clinical practices observed locally include pleural fluid collection in sterile container for ease of other biochemistry analytes measurement in addition to pH. This study aimed to compare the differences in pleural fluid pH and other commonly ordered biochemistry analytes collected using sterile container against heparinized blood gas syringe. Methods Total 20 Pleural fluid samples were collected from patient using a procedure called thoracentesis from, with approximately 2 to 3ml of fluid directly transferred into heparinized blood gas syringe as well as in a sterile container. Both samples were then transported to Clinical Biochemistry Laboratory within an hour. Both samples were analyzed on 3 analytical systems; 1) pH on Roche COBAS b221 blood gas analyzer, 2) Glucose, Total Protein, LDH on Beckman AU System and 3) Specific Gravity on Reflectometer. Statistical analysis was performed using Analyze-It software. Results pH fluid collected in sterile container demonstrates significant high bias (absolute of 0.4 units or 5%) as compared to sample collected in heparinizes blood gas syringe. Other commonly ordered tests in fluid profile (Glucose, Total Protein, LDH and SG) demonstrate good correlation (Altman-Bland Difference Plot <5%) for pleural fluid collected in sterile container as compared to heparinized syringe. Conclusions This positive bias observed is likely attributed to air exposure in pleural fluid collection in sterile container as compared to heparinized syringe. The accuracy of measured pleural pH is critically dependent on the method of sample collection. This study highlights the need for a standardized protocol for pleural pH collection for accurate measurement.

Novel In-Line Hemolysis Detection on a Blood Gas Analyzer and Impact on Whole Blood Potassium Results.

Preanalytical error due to hemolyzed blood samples is a common challenge in laboratory and point-of-care (POC) settings. Whole blood potassium (K+) measurements routinely measured on blood gas analyzers are particularly susceptible to hemolysis, which poses a risk for incorrect K+ results. The GEM Premier 7000 with IQM3 (GEM 7000) blood gas analyzer provides novel integrated hemolysis detection within the sample measurement process. Therefore, the GEM 7000 can detect and flag hemolyzed whole blood samples at the POC, warning the operator of potentially erroneous results. Heparinized venous or arterial whole blood samples were used for K+ interference studies and assessed for hemolysis agreement utilizing either a traditional volumetric method or chemistry analyzer serum index measurements with the Roche cobas c311 or Abbott Alinity c. Hemolysis interference studies performed at 2 different K+ concentrations (3.8 and 5.3 mmol/L) identified that a plasma free hemoglobin ≥116 mg/dL can impact K+ results on the GEM 7000. Hemolysis agreement studies demonstrated an excellent agreement of >99% with the volumetric method, 98.8% with cobas H index, and 96.4% with Alinity H index. GEM 7000 K+ results were correctly flagged for both native and spiked samples. GEM 7000 hemolysis detection provides a novel technology to detect hemolysis in whole blood samples. Moreover, the GEM 7000 demonstrates excellent agreement with traditional laboratory hemolysis detection methods and offers an integrated technological solution for assuring the quality of whole blood K+ results in POC settings.

Preanalytical Errors in Hematology: Insights From a Tertiary Care Hospital.

Introduction Errors occur in the laboratory at any level of the testing process. Recognizing these errors may cause patient distress by delaying diagnosis and management until results are released. This study aims to assess preanalytical errors in the laboratory and suggests methods to prevent them to improve accuracy and efficiency in the hematology department of a tertiary care hospital. The background emphasizes the critical role of preanalytical procedures in ensuring accurate hematological diagnoses and highlights the frequent mistakes that occur during specimen collection, handling, and transportation. Staff training, process standardization, using suitable collecting and transport equipment, putting quality control measures in place, and using automation technologies are some strategies for reducing errors and speeding up turnaround times. To increase diagnostic accuracy, patient care outcomes, and laboratory efficiency, hematology laboratories should address preanalytical mistakes and employ fostering methods. Aims and objectives The objective of this research is to evaluate the frequency and characteristics of preanalytical errors within the hematology laboratory of a tertiary care hospital. The study seeks to gauge the scope of the problem, identify key factors contributing to these errors, such as issues in specimen collection, handling, and transportation, and highlight areas where improvements can be made. Also, it intends to assess how preanalytical errors affect hematological accuracy and turnaround times, with a focus on patient care outcomes, and recommend techniques to reduce preanalytical errors and improve the accuracy of hematological results. Materials and methods This study was conducted at the hematology laboratory of our hospital from January 2023 to June 2024 after getting proper approval from the Institutional Review Board (IRB approval number 294/08/2024/PG/SRB/SMCH). It is a retrospective analytical study, and the study population comprised samples of patients from the emergency, inpatient, and outpatient departments, which included 51,155 complete blood count (CBC) and 5,449 peripheral smear(PS) samples. The study included only test samples sent specifically for hematological analysis, while those sent for cytological, biochemical, or microbiological testing were excluded. The distribution frequency of the samples and the number of rejected samples were analyzed, and the results were then compared and correlated. Results During the study period, a total of 56,604 samples were processed in the hematology laboratory. Of these, 886 samples (1.3%) were rejected due to preanalytical errors. The most frequent error was submitting an insufficient sample (54.17%), while the least frequent was the use of an empty or defective tube (0.4%). In the emergency department, the primary issues were insufficient and clotted samples, while in pediatric cases, errors were mainly due to inadequate or diluted samples. Conclusion Preanalytical errors in hematology laboratories, though often overlooked, can significantly impact diagnostic accuracy and patient care. Our study highlights that a substantial portion of errors arise from inadequate sample collection and handling, particularly in emergency and pediatric cases. Adherence to standard laboratory techniques can dramatically reduce preanalytical errors.

Stability of serum total cholesterol, triglycerides, and plasma glucose of healthy individuals at different storage conditions

Evaluation of the stability of serum and plasma analytes under different temperatures and time points is vital since it significantly affects the pre-analytical errors. The aim of the study was to determine the stability of serum total cholesterol and triglyceride and plasma glucose of healthy individuals under different storage conditions (25-30°C and 2-8°C) and at different time points (1-hour, 4-hour, 6-hour, and 24-hour). Venous blood samples were collected from healthy individuals aged between 20-50 years after a 12-hour fast. Plasma and serum were separated and stored at different temperatures (25-30°C and 2-8°C) and different time points (1-hour, 4-hour, 6-hour, and 24-hour). Results indicated a significant difference between the mean plasma glucose concentration at room temperature (25-30°C) and refrigerated temperature (2-8°C) at 1-hour (0.072%, p = 0.05). However, there were no significant differences in mean serum total cholesterol concentrations (p = 0.633) and mean serum triglyceride concentrations (p = 0.246) between these temperatures at 1-hour time point. Regarding time points (1-hour, 4-hour, 6-hour, and 24-hour), significant differences in plasma glucose concentrations occurred at 24-hour (10.03%, p = 0.001) at room temperature (25-30°C) and 6-hour (8.79%, p = 0.003) at refrigerated temperature (2-8°C) compared to the 1-hour reference values. Additionally, there were significant differences in serum total cholesterol concentration at 4-hour (12.55%, p = 0.000) and serum triglyceride at 24-hour (29.18%, p = 0.001) compared to the reference values obtained at 1-hour at room temperature (25-30°C). No significant differences were observed among the time points (1-hour, 4-hour, 6-hour, and 24-hour) for serum total cholesterol (p = 0.507) and serum triglyceride values (p = 0.761) at refrigerated temperature (2-8°C). Plasma glucose is unstable at both room temperature (25-30°C) and refrigeration (2-8°C), with recommended testing within 6 hours at room temperature (25-30°C) and stored in a refrigerator (2-8°C) if delayed. It is better to estimate serum total cholesterol within 1-hour of collection at room temperature (25-30°C) while serum triglyceride concentration within 6 hours at the same temperature to obtain accurate and reliable results.

Strategies for Error Reduction: Why More Stringent Premarket Evaluations Do Little to Prevent Laboratory Errors and Traffic Accidents

Laboratory testing is a complex process with a significant error rate. Studies of laboratory errors have found that the major causes are preanalytical factors, interferences, and process errors. Efforts by regulatory agencies to improve quality via more stringent premarket evaluations of laboratory tests therefore have poor prospects of reducing laboratory errors and improving test quality. Efforts toward increasing the regulation of laboratory tests are analogous to preventing traffic accidents by increasing the premarket evaluation of automobiles. This analogy illustrates how increased premarket evaluation has limited prospects for quality improvement and, in some cases, actually contributes to errors and lower quality. Tools that are used by laboratories to detect, prevent, and address analytical errors are discussed, and the increased implementation of such tools offers approaches that can be used to improve laboratory quality.

Machine Learning for Patient-Based Real-Time Quality Control (PBRTQC), Analytical and Preanalytical Error Detection in Clinical Laboratory.

The rapidly evolving field of machine learning (ML), along with artificial intelligence in a broad sense, is revolutionising many areas of healthcare, including laboratory medicine. The amalgamation of the fields of ML and patient-based real-time quality control (PBRTQC) processes could improve the traditional PBRTQC and error detection algorithms in the laboratory. This narrative review discusses published studies on using ML for the detection of systematic errors, non-systematic errors, and combinations of different types of errors in clinical laboratories. The studies discussed used ML for detecting bias, the requirement for re-calibration, samples contaminated with intravenous fluid or EDTA, delayed sample analysis, wrong-blood-in-tube errors, interference or a combination of different types of errors, by comparing the performance of ML models with human validators or traditional PBRTQC algorithms. Advantages, limitations, the creation of standardised ML models, ethical and regulatory aspects and potential future developments have also been discussed in brief.

Rapid complete blood count and C-reactive protein determination with the Horiba Microsemi analyzer: the experience in neonatal intensive care unit of Careggi University Hospital

Microsystems represent an alternative but proficient approach of analysis outside the laboratory, and their use could help in reducing the impact of pre-analytical errors, in particular in challenging newborn samples. The study purpose is to compare the Horiba Microsemi CRP LC-767G system for rapid 3-part complete blood count (CBC) and C-reactive protein (CRP) determination with the laboratory reference systems (respectively Sysmex XN-9100™ and Roche Cobas® c702) in samples of adult patients and newborns hospitalized in the neonatal intensive care unit (NICU) samples. The comparison between the analyzers was performed through Passing-Bablok regression analysis and Bland–Altman plot. One hundred eighty-three blood samples were analyzed. The regression analysis results, performed in the newborn (n = 70) and in adult (n = 113) populations, showed a good agreement between the instruments. The evaluation of the Bland–Altman plots showed comparable values of bias < 10% for most of the parameters, but not for MPV, lymphocyte, and monocyte count. Conclusion: The comparison between the Microsemi CRP LC-767G system and the laboratory instrumentations demonstrated comparable results. The Microsemi CRP LC-767G system provides reliable analytical data and faster turnaround time, particularly useful in NICU. What is Known:• Microsystems for point-of-care testing (POCT) represent an alternative but proficient approach of analysis outside the laboratory, in order to perform a rapid, safe, and exhaustive evaluation for critical patients’ management, acting as a valid support for treatment in acute care.What is New:• The Microsemi CRP LC-767G system can represent an alternative but effective testing approach outside the laboratory, particularly in NICU, to reduce the impact of pre-analytical errors on newborn samples.

NGS mutational status on first diagnostic tissue, liquid biopsy and mastectomy in G2-G3 breast cancer.

Breast cancer is one of the more frequently diagnosed cancers leading to death in women, and, like other tumor types, it is heterogeneous in its immunophenotype. It harbors mutations that modify tumor aggressiveness, therapy responses, residual disease, drug resistance, and relapse rates in advanced stages. This study aims to assess the mutational status of G2 and G3 tumors using next-generation sequencing (NGS) on initial tissue biopsies, liquid biopsies, and mastectomy specimens. The histopathological (HP) diagnosis for the 32 selected cases was established via Hematoxylin-Eosin (HE) staining by two observers. For the immunohistochemical (IHC) testing of estrogen receptor (ER), progesterone receptor (PGR) and human epidermal growth factor receptor 2 (HER2), we used the Ventana BenchMark Ultra. Ki67 testing was conducted using Bond-III from Leica. For cases with a score of 2+, gene amplification was assessed by silver-enhanced in situ hybridization (ISH) (SISH; Inform HER2 Dual ISH) on Ventana BenchMark Ultra. NGS analysis was initially performed on biopsies and plasma, and later on mastectomy specimens. After automated deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) extraction, concentrations were measured using the Invitrogen Qubit system. Libraries were created using Oncomine systems, and sequencing and analysis were done with the Ion Torrent system. Most tumors were graded as G3 (19 cases), with Luminal A being the predominant molecular subtype, and a significant number displayed HER2∕HER2-low characteristics (24 out of 32 cases). The NGS assessment showed that phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) mutations were the most frequent across all sample types. A significant limitation was the high number of invalid plasma tests due to pre-analytical handling errors or transport issues. Nonetheless, plasma testing (liquid biopsy) proved useful for monitoring tumor evolution and assessing residual disease.

Selection of Single-Analyte Delta Check Rules with Logistic Regression for Detection of Intravenous Fluid Contamination in a Clinical Chemistry Laboratory.

The conventional single-analyte delta check, utilized for identifying intravenous fluid contamination and other preanalytical errors, is known to flag many specimens reflecting true patient status changes. This study aimed to derive delta check rules that more accurately identify contamination. Results for calcium, creatinine, glucose, sodium, and potassium were retrieved from 326 103 basic or comprehensive metabolic panels tested between February 2021 and January 2022. In total, 7934 specimens showed substantial result changes, of which 1489 were labeled as either contaminated or non-contaminated based on chart review. These labeled specimens were used to derive logistic regression models and to select the most predictive single-analyte delta checks for 4 common contaminants. Their collective performance was evaluated using a test data set from October 2023 comprising 14 717 specimens. The most predictive single-analyte delta checks included a calcium change by ≤-24% for both saline and Plasma-Lyte A contamination, a potassium increase by ≥3.0 mmol/L for potassium contamination, and a glucose increase by ≥400 mg/dL (22.2 mmol/L) for dextrose contamination. In the training data sets, multi-analyte logistic regression models performed better than single-analyte delta checks. In the test data set, logistic regression models and single-analyte delta checks demonstrated collective alert rates of 0.58% (95% CI, 0.46%-0.71%) and 0.60% (95% CI, 0.49%-0.74%), respectively, along with collective positive predictive values of 79% (95% CI, 70%-89%) and 77% (95% CI, 68%-87%). Single-analyte delta checks selected by logistic regression demonstrated a low false alert rate.

Association between Iron deficiency and hypothyroxinemia in first-trimester pregnant women: A study from a tertiary care hospital

ABSTRACT Background: Iron deficiency (ID) is a common nutritional concern worldwide. Hypothyroidism during pregnancy also has harmful effects on both mother and child, such as post-partum hemorrhage, pre-eclampsia, low-birth weight, pre-term labour and abruptio placenta. Few articles have shown an association between ID and hypothyroidism. Aims: To evaluate the association between hypothyroidism, hypotriiodothyroninemia and ID in pregnant women. Materials and Methods: This cross-sectional study was conducted between January 2017 and June 2019 at JSS Hospital, a Tertiary Care Teaching and Research Hospital attached to JSS Medical College in Mysuru City, South India. Five hundred consecutive pregnant women aged 18–45 years in the first trimester of pregnancy were recruited. A venous blood sample of approximately 5 mL was drawn and sent for analysis of haemoglobin, thyroid-stimulating hormone (TSH), T3, T4, anti-thyroid peroxidase (TPO) antibodies and serum ferritin. T3, T4, TSH, anti-TPO antibody and serum ferritin levels were measured by chemiluminescence for all pregnant women. Correlation and linear regression analyses were performed to determine the association between ID and T3, T4 and TSH levels. Results: Out of 500 women, nine were excluded because of pre-analytical errors; hence, there were 491 pregnant women in the first trimester. Mean values of T3, T4, TSH and ferritin in the study were 1.45 ± 0.72, 9.29 ± 2.53, 2.37 ± 3.17 and 31.54 ± 30.28. There was a positive correlation between serum ferritin and T4 levels, with a correlation coefficient of 0.135. The scatter plot also showed a linear relationship between serum ferritin and T4 levels. There was no significant correlation between serum ferritin, T3 and TSH levels. Linear regression analysis also showed an association between ferritin and T4 levels, with a significance of 0.003. Linear regression analysis was insignificant for TSH, T3 and serum ferritin levels. Conclusion: ID anaemia (IDA) is associated with hypothyroxinemia in pregnant women. Further extensive studies are needed to evaluate the causal association between hypothyroidism and IDA. Iron therapy might improve thyroid parameters in patients with subclinical hypothyroidism and ID without thyroxine, which requires further research.

Point-of-care testing: state-of-the art and perspectives.

Point-of-care testing (POCT) is becoming an increasingly popular way to perform laboratory tests closer to the patient. This option has several recognized advantages, such as accessibility, portability, speed, convenience, ease of use, ever-growing test panels, lower cumulative healthcare costs when used within appropriate clinical pathways, better patient empowerment and engagement, and reduction of certain pre-analytical errors, especially those related to specimen transportation. On the other hand, POCT also poses some limitations and risks, namely the risk of lower accuracy and reliability compared to traditional laboratory tests, quality control and connectivity issues, high dependence on operators (with varying levels of expertise or training), challenges related to patient data management, higher costs per individual test, regulatory and compliance issues such as the need for appropriate validation prior to clinical use (especially for rapid diagnostic tests; RDTs), as well as additional preanalytical sources of error that may remain undetected in this type of testing, which is usually based on whole blood samples (i.e.,presence of interfering substances, clotting, hemolysis, etc.). There is no doubt that POCT is a breakthrough innovation in laboratory medicine, but the discussion on its appropriate use requires further debate and initiatives. This collective opinion paper, composed of abstracts of the lectures presented at the two-day expert meeting "Point-Of-Care-Testing: State of the Art and Perspective" (Venice, April 4-5, 2024), aims to provide a thoughtful overview of the state-of-the-art in POCT, its current applications, advantages and potential limitations, as well as some interesting reflections on the future perspectives of this particular field of laboratory medicine.

Determining the rate and reasons for specimen rejection among specimens referred for HIV viral load determination through referral linkage to Debre Tabor Comprehensive Specialized Hospital, north-central, Ethiopia,2023: Retrospective study

BackgroundThe preanalytical phase encompasses the time between the clinician's test order to the sample being ready for analysis. Of all errors during the laboratory diagnostic process,70 % appeared in the pre-analytical phase. In clinical laboratories, it is crucial to ensure proper specimen collection and handling, which is essential to guarantee quality assessment, monitoring process standardization, improving performance, and ensuring patient safety. Despite this importance, no study has been conducted in the study area to investigate the rate and reasons for human immunodeficiency virus viral load sample rejection. ObjectiveTo determine the rate of human immunodeficiency virus viral load sample rejection (number of preanalytical errors) documented during the preanalytical phase and articulate possible causes for specimen rejection. Material and methodsA retrospective study was conducted at Debre Tabor Comprehensive Specialized Hospital from January 1st to January 31, 2023. During the study period, 5950 samples were extracted from the human immunodeficiency virus viral load laboratory sample tracking log books, which were sent to the hospital for viral load testing between August 2021 to November 2022. The collected data were cleaned and entered into EPI data version 4.6 before transferred it to STATA version 14.0 for analysis. Descriptive statistics such as frequencies, percentages, and cross-tabulations were used to summarize the findings. ResultsThe study found that improper sample handling was common during the preanalytical phase. According to the current study, 3.6 % of the sample was rejected at pre analytical stage. The most common reasons for specimen rejection were using inappropriate containers (64.0 %) uncentrifuged specimens (20.4 %); hemolyzed specimens (7.0 %); insufficient specimen volume (6.2 %); clotted specimens (1.9 %); and specimen labeling problems (0.5 %). ConclusionThis study found that the most common preanalytical error was using an inappropriate sample collection container, followed by uncentrifuged samples, Therefore, it is recommended that mentorship programs be developed to educate staff on the preanalytical phase of laboratory testing, specifically on sample collection, storage, and transportation for HIV viral load testing. Additionally, the quality management system of laboratory processes should be strengthened to ensure accuracy and minimize errors.

Identification of Pre-analytical Errors in the Laboratory of National Institute of Infectious Diseases, Sri Lanka

Identification of Pre-analytical Errors in the Laboratory of National Institute of Infectious Diseases, Sri Lanka

Prevalence of pre-analytical error in diagnostic laboratory of National Heart Institute, Malaysia

Prevalence of pre-analytical error in diagnostic laboratory of National Heart Institute, Malaysia

Application of DMAIC methodology to reduce pre-analytical errors in a tertiary hospital clinical chemistry laboratory in India

Application of DMAIC methodology to reduce pre-analytical errors in a tertiary hospital clinical chemistry laboratory in India