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Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limitations of conventional methods. We analyzed five tumor marker test results: alpha-fetoprotein (AFP), cancer antigen 19-9 (CA19-9), cancer antigen 125 (CA125), carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA). A total of 246,261 records were used in the analysis. Of these, 179,929 records were used for model training and 66,332 records for performance evaluation. We developed a misidentification error detection model based on the random forest (RF) and deep neural network (DNN) methods. We performed an in silico simulation with 1 % random sample shuffling. The performance of the developed models was evaluated and compared to conventional delta check methods such as delta percent change (DPC), absolute DPC (absDPC), and reference change values (RCV). The DNN model outperformed the RF, DPC, absDPC, and RCV methods in detecting sample misidentification errors. It achieved balanced accuracies of 0.828, 0.842, 0.792, 0.818, and 0.833 for AFP, CA19-9, CA125, CEA, and PSA, respectively. Although the RF method performed better than DPC and absDPC, it showed similar or lower performance compared to RCV. Our research results demonstrate that an ML-based delta check method can more effectively detect sample misidentification errors compared to conventional delta check methods. In particular, the DNN model demonstrated superior and stable detection performance compared to the RF, DPC, absDPC, and RCV methods. [ABSTRACT FROM AUTHOR]

Introduction: The high-sensitivity cardiac troponin (hs-cTn) assay is a crucial diagnostic test that is obligatory in the triage of patients presenting with chest pain. It is essential for medical officers to have adequate knowledge regarding the procedures for testing and the interpretation of test outcomes to deliver optimal healthcare for patients. It has been observed that medical officers are lacking in awareness regarding this testing method, resulting in a greater likelihood of errors in testing and interpretation. This study aims to evaluate the level of understanding among medical officers regarding the hs-cTn assay in relation to the most recent European Society of Cardiology guideline released in 2020. Methodology: We conducted a cross-sectional study at two government hospitals in Gampaha District in June 2022. A self-administered e-questionnaire was used to assess knowledge regarding hs-cTn. Knowledge was measured by calculating a cumulative score for the answers to a questionnaire and categorised into good or poor knowledge. A score less than 60% was categorized as poor and vice versa. Descriptive statistics were used to summarize the data. Results: Of the 227 medical officers, only 14.5% (n=33) had good knowledge of the analytical component while 41.9% (n=95) had good knowledge on the clinical component. A score ≥60% on the analytical component knowledge was significantly associated with designation being a senior registrar or consultant (p

Few studies have reported on delta checks for tumour markers, even though these markers are often evaluated serially. Therefore, this study aimed to establish a practical delta check limit in different clinical settings for five tumour markers: alpha-fetoprotein, cancer antigen 19-9, cancer antigen 125, carcinoembryonic antigen, and prostate-specific antigen. Pairs of patients' results (current and previous) for five tumour markers between 2020 and 2021 were retrospectively collected from three university hospitals. The data were classified into three subgroups, namely: health check-up recipient (subgroup H), outpatient (subgroup O), and inpatient (subgroup I) clinics. The check limits of delta percent change (DPC), absolute DPC (absDPC), and reference change value (RCV) for each test were determined using the development set (the first 18 months, n=179,929) and then validated and simulated by applying the validation set (the last 6 months, n=66,332). The check limits of DPC and absDPC for most tests varied significantly among the subgroups. Likewise, the proportions of samples requiring further evaluation, calculated by excluding samples with both current and previous results within the reference intervals, were 0.2–2.9% (lower limit of DPC), 0.2–2.7% (upper limit of DPC), 0.3–5.6% (absDPC), and 0.8–35.3% (RCV99.9%). Furthermore, high negative predictive values >0.99 were observed in all subgroups in the in silico simulation. Using real-world data, we found that DPC was the most appropriate delta-check method for tumour markers. Moreover, Delta-check limits for tumour markers should be applied based on clinical settings. [ABSTRACT FROM AUTHOR]

Background: Sepsis is often accompanied with acute kidney injury (AKI). The incidence of AKI in patients visiting the emergency department (ED) with sepsis according to the new SOFA criteria is not exactly known, because the definition of sepsis has changed and many definitions of AKI exist. Given the important consequences of early recognition of AKI in sepsis, our aim was to assess the epidemiology of sepsis-associated AKI using different AKI definitions (RIFLE, AKIN, AKIB, delta check, and KDIGO) for the different sepsis classifications (SIRS, qSOFA, and SOFA). Methods: We retrospectively enrolled patients with sepsis in the ED in three hospitals and applied different AKI definitions to determine the incidence of sepsis-associated AKI. In addition, the association between the different AKI definitions and persistent kidney injury, hospital length of stay, and 30-day mortality were evaluated. Results: In total, 2065 patients were included. The incidence of AKI was 17.7–51.1%, depending on sepsis and AKI definition. The highest incidence of AKI was found in qSOFA patients when the AKIN and KDIGO definitions were applied (51.1%). Applying the AKIN and KDIGO definitions in patients with sepsis according to the SOFA criteria, AKI was present in 37.3% of patients, and using the SIRS criteria, AKI was present in 25.4% of patients. Crude 30-day mortality, prolonged length of stay, and persistent kidney injury were comparable for patients diagnosed with AKI, regardless of the definition used. Conclusion: The incidence of AKI in patients with sepsis is highly dependent on how patients with sepsis are categorised and how AKI is defined. When AKI (any definition) was already present at the ED, 30-day mortality was high (22.2%). The diagnosis of AKI in sepsis can be considered as a sign of severe disease and helps to identify patients at high risk of adverse outcome at an early stage. [ABSTRACT FROM AUTHOR]

Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection of sample mix-ups. The aim of the study was to develop a highly accurate DC method based on designed deep learning to detect sample mix-up. A total of 22 routine hematology test items were adopted for the study. The hematology test results, collected from two hospital laboratories, were independently divided into training, validation, and test sets. By selecting six mainstream algorithms, the Deep Belief Network (DBN) was able to learn error-free and artificially (intentionally) mixed sample results. The model's analytical performance was evaluated using training and test sets. The model's clinical validity was evaluated by comparing it with three well-recognized statistical methods. When the accuracy of our model in the training set reached 0.931 at the 22nd epoch, the corresponding accuracy in the validation set was equal to 0.922. The loss values for the training and validation sets showed a similar (change) trend over time. The accuracy in the test set was 0.931 and the area under the receiver operating characteristic curve was 0.977. DBN demonstrated better performance than the three comparator statistical methods. The accuracy of DBN and revised weighted delta check (RwCDI) was 0.931 and 0.909, respectively. DBN performed significantly better than RCV and EDC. Of all test items, the absolute difference of DC yielded higher accuracy than the relative difference for all methods. The findings indicate that input of a group of hematology test items provides more comprehensive information for the accurate detection of sample mix-up by machine learning (ML) when compared with a single test item input method. The DC method based on DBN demonstrated highly effective sample mix-up identification performance in real-world clinical settings. [ABSTRACT FROM AUTHOR]

To determine delta check limits for immunoglobulins and complements in outpatients and inpatients based on patient data and biological variation due to the lack of relevant studies.Patient data for IgA, IgG, IgM, IgE, C3, and C4 from 1 January 2022 to 31 December 2023 was collected from laboratory information system (LIS) in our clinical laboratory of Wuhan Union Hospital, which includes both outpatients and inpatients. The delta difference (DD), delta percent change (DPC), and reference change value (RCV) were calculated based on patient data and biological variation.For DDs, there are significant differences between outpatients and inpatients in C4, IgE, IgG, and IgM. For DPCs, the corresponding analytes which are significantly different are C3, C4, IgE, IgG, and IgM. Two sources of CVI to calculate the RCV of IgA, IgG, IgM, C3, and C4 were applied in this study, which revealed that two kinds of RCVs based on different biological variation databases are similar to each other, but both were smaller than delta check limits based on patient data, except for C4.The delta check is a useful tool to monitor potential errors which may occur in total testing process. We hope our findings could be helpful for future studies focused on delta checks in immunological analytes. [ABSTRACT FROM AUTHOR]

Peripheral blood lymphocyte subsets are important parameters for monitoring immune status; however, lymphocyte subset detection is time-consuming and error-prone. This study aimed to explore a highly efficient and clinically useful autoverification system for lymphocyte subset assays performed on the flow cytometry platform. A total of 94,402 lymphocyte subset test results were collected. To establish the limited-range rules, 80,427 results were first used (69,135 T lymphocyte subset tests and 11,292 NK, B, T lymphocyte tests), of which 15,000 T lymphocyte subset tests from human immunodeficiency virus (HIV) infected patients were used to set customized limited-range rules for HIV infected patients. Subsequently, 13,975 results were used for historical data validation and online test validation. Three key autoverification rules were established, including limited-range, delta-check, and logical rules. Guidelines for addressing the issues that trigger these rules were summarized. The historical data during the validation phase showed that the total autoverification passing rate of lymphocyte subset assays was 69.65% (6,941/9,966), with a 67.93% (5,268/7,755) passing rate for T lymphocyte subset tests and 75.67% (1,673/2,211) for NK, B, T lymphocyte tests. For online test validation, the total autoverification passing rate was 75.26% (3,017/4,009), with 73.23% (2,191/2,992) for the T lymphocyte subset test and 81.22% (826/1,017) for the NK, B, T lymphocyte test. The turnaround time (TAT) was reduced from 228 to 167 min using the autoverification system. The autoverification system based on the laboratory information system for lymphocyte subset assays reduced TAT and the number of error reports and helped in the identification of abnormal cell populations that may offer clues for clinical interventions. [ABSTRACT FROM AUTHOR]

• 610,437 TFT results analyzed; significant DCL differences noted. • Need for tailored DCLs for TFTs in various clinical settings highlighted. • Age impacts on DCLs for TFTs are minor, showing universal thresholds may suffice. This study aimed to determine practical delta check limits (DCLs) for thyroid function tests (TFTs) to detect sample misidentifications across various clinical settings. Between 2020 and 2022, 610,437 paired TFT results were collected from six university hospitals. The absolute DCL (absDCL) was determined using the 95th percentile for each clinical setting from a random 60 % of the total data. These absDCLs were then tested within and across different settings using the remaining 40 % of the data, alongside mix-up datasets for result and sample comparisons. The sensitivities of absDCL were calculated within and across groups in the mix-up datasets. Health screening absDCLs were notably lower than in other settings (2.58 vs. 5.93–7.08 for thyroid-stimulating hormone; 4.12 vs. 8.24–10.04 for free thyroxine; 0.49 vs. 0.82–0.91 for total triiodothyronine). The proportion of results exceeding absDCL of health screening differed from those of other clinical settings. Furthermore, sensitivity between health screening and other clinical settings was significantly different in both the result mix-up and sample mix-up datasets. This study determined practical DCLs for TFTs and highlighted differences in absDCLs between health screening and other settings. These findings emphasize the importance of tailored DCLs in improving the accurate reporting of TFTs. [ABSTRACT FROM AUTHOR]

Objective: Continuous process improvements enhance accuracy and productivity in a clinical laboratory setting. This study aimed to investigate the accuracy and efficiency of a new autoverification (AV) system designed to improve the consistency and uniformity of reported laboratory test results. Methods: Limit checks, delta checks, and consistency checks were established, and then retrospective data from 500 requested tests were used to evaluate the accuracy of AV rules compared to manual verification, which was performed by five experienced medical technologists. Efficiency was evaluated by comparing turnaround time (TAT), error rates, workload, and staff satisfaction between before and after AV implementation. Results: AV had 100% sensitivity, 77.6% specificity, and a 22% false-positive rate. The AV passing rate was 95%, 85%, 42%, and 39% for chemistry, coagulation, microscopy, and hematology, respectively. The overall passing rate was 65%. After implementation, the mean overall TAT decreased from 54.2±26.6 to 52.4±24.2 min (p

A patient may have two contemporary hemoglobin test results; one received from a hematology analyzer and the other from a blood gas analyzer. Such results may pose a dilemma if validated without investigation. A 27-year-old female patient presented to the emergency department because of worsening dyspnea and hemoptysis. Her hemogram revealed Hb 4.5 g/dL; Hct 16.1% while blood gas analysis showed Hb 8.1 g/dL; Hct 25.3%. The results were evaluated to be suspicious. All results were rejected and new requests were made. Hb 9.8 g/dL; Hct 31.9% were seen in repeated hemogram while blood gas analysis showed Hb 10.2 g/dL; Hct 31.5%. It was understood that the nurse used the vein with an intravenous fluid for sampling the first hemogram. A multitude of factors affect the accuracy of hemoglobin concentration measurements. Literature suggests that hemoglobin values of hematology analyzers and blood gas analyzers should be in harmony. Any discordance must be an alarm to stop and review the test results before validation. [ABSTRACT FROM AUTHOR]

Delta checks are a post-analytical verification tool that compare the difference in sequential laboratory results belonging to the same patient against a predefined limit. This unique quality tool highlights a potential error at the individual patient level. A difference in sequential laboratory results that exceeds the predefined limit is considered likely to contain an error that requires further investigation that can be time and resource intensive. This may cause a delay in the provision of the result to the healthcare provider or entail recollection of the patient sample. Delta checks have been used primarily to detect sample misidentification (sample mix-up, wrong blood in tube), and recent advancements in laboratory medicine, including the adoption of protocolized procedures, information technology and automation in the total testing process, have significantly reduced the prevalence of such errors. As such, delta check rules need to be selected carefully to balance the clinical risk of these errors and the need to maintain operational efficiency. Historically, delta check rules have been set by professional opinion based on reference change values (biological variation) or the published literature. Delta check rules implemented in this manner may not inform laboratory practitioners of their real-world performance. This review discusses several evidence-based approaches to the optimal setting of delta check rules that directly inform the laboratory practitioner of the error detection capabilities of the selected rules. Subsequent verification of workflow for the selected delta check rules is also discussed. This review is intended to provide practical assistance to laboratories in setting evidence-based delta check rules that best suits their local operational and clinical needs. [ABSTRACT FROM AUTHOR]

Background: This study aimed to evaluate delta check limits set by reference change value (RCV) and patient data. Methods: Patient data of 11 clinical chemistry analytes from June 2018 to May 2019 were collected. RCV with 95% or 99% levels of probability were calculated based on biological variation. The corresponding delta check limits for outpatients and inpatients were calculated by 95% or 99% central range of delta% which was the difference of two consecutive results within thirty days of the same patient for each analyte. Patient data in June 2019 were used to analyze the utility of delta check limits. Results: In total, 434,927 paired results for these 11 analytes were included. The delta check limits were different between outpatients and inpatients, but were wider than those established by RCV. The difference between Glu's outpatient and inpatient boundaries was the largest, 95% central range from the outpatient (inpatients) was from -32.29% (-56.97%) to 38.78% (106.00%) while 99% central range from the outpatient (inpatients) was from - 56.86% (-90.56%) to 89.96% (262.54%). The RCV is mainly determined by within-individual biological variation so that the RCV of each analyte varied from each other. As for RCV, Na had the lowest value and BUN had the highest one. In addition, the main reason for delta% exceeding delta check limits was a clinically significant change. Conclusions: Laboratories could use delta check procedure to find out errors in sample collection and monitor clinical significance. When delta% of patients exceed corresponding delta check limits in a short time, clinicians and personnel of clinical laboratory should pay more attention. Delta check limits should be reviewed regularly to check the utility of procedure. [ABSTRACT FROM AUTHOR]

Objective: Continuous process improvements enhance accuracy and productivity in a clinical laboratory setting. This study aimed to investigate the accuracy and efficiency of a new autoverification (AV) system designed to improve the consistency and uniformity of reported laboratory test results. Methods: Limit checks, delta checks, and consistency checks were established, and then retrospective data from 500 requested tests were used to evaluate the accuracy of AV rules compared to manual verification, which was performed by five experienced medical technologists. Efficiency was evaluated by comparing turnaround time (TAT), error rates, workload, and staff satisfaction between before and after AV implementation. Results: AV had 100% sensitivity, 77.6% specificity, and a 22% false-positive rate. The AV passing rate was 95%, 85%, 42%, and 39% for chemistry, coagulation, microscopy, and hematology, respectively. The overall passing rate was 65%. After implementation, the mean overall TAT decreased from 54.2±26.6 to 52.4±24.2 min (p<0.001). However, TAT during peak hours increased (p<0.05). Incident reports decreased 8-fold (p<0.05), net workload decreased by 0.76 full-time equivalent, and overall staff satisfaction increased (p<0.001). Conclusion: Our laboratory's new AV system demonstrated an overall passing rate of 65% with decreases in TAT, incident reports, and workload, and an increase in staff satisfaction. [ABSTRACT FROM AUTHOR]

Modern clinical laboratories have to confirm that the procedures used for specific tests are reliable and valid. There are several sources of errors and interferences that can invalidate the results. Medical validation refers to the plausibility check of the test results. Implausible results indicate that something might went wrong with a sample retrieved from the patient, e.g., the blood sample got contaminated with another fluid, which requires re-examination. Here, we describe how an integrated R-based business intelligence (BI) tool can be developed that increase the efficiency of the medical validation at the Institute of Clinical Chemistry (ICC) of the University Hospital Zurich. A BI software environment allowed us to digitalize steps in the validation process that were manually done in Excel worksheets, e.g., importing the data, calculating percentiles, and producing graphical outputs. [ABSTRACT FROM AUTHOR]

• Performance of delta check rules may be dependent on the biological variation. • Mathematical models were developed to explore these relationships. • Sensitivity and specificity for different delta check rules were formulated. • Predicted sensitivity corresponded well with empirical performance. • Predicted specificity were lower than empirical data. The performance of delta check rules has been considered to be dependent on the biological variation characteristics of the analyte of interest. The assumed relationships have not been formally studied. The mathematical relationship between biological variation and delta check rules is explored in this study. From the mathematical model for absolute difference delta check, the threshold for specificity and sensitivity are observed to be normalized differently. For specificity, the threshold is normalized by the within-subject biological variation (expressed as a coefficient of variation, CV i), whereas for sensitivity the threshold is normalized by the between-subject biological variation (expressed as a coefficient of variation, CV g). This highlights the different roles the two biological variations play in affecting the absolute difference distribution for correct and switched patient samples. Analogous to absolute difference delta checks, for relative difference delta checks, the expressions for specificity and sensitivity are scaled by CV i and CV g , respectively. However, the expressions are independent of μ g (the average of the population). A comparison between the mathematical model and empirical/ historical laboratory data obtained from patients was conducted for both absolute and relative difference delta checks. In general it was found that the specificity obtained from the historical laboratory data was less than the model predicted values, while on the other hand, good correspondence was obtained between the experimental sensitivity and predicted sensitivity. The difference in within-subject biological variation in different patients may contribute to the observed discrepancy in predicted and empirical delta check performance. [ABSTRACT FROM AUTHOR]

Objectives: The interpretation of delta check rules in a panel of tests should be different to that at the single analyte level, as the number of hypothesis tests conducted (i.e. the number of delta check rules) is greater and needs to be taken into account.Methods: De-identified paediatric laboratory results were extracted, and the first two serial results for each patient were used for analysis. Analytes were grouped into four common laboratory test panels consisting of renal, liver, bone and full blood count panels. The sensitivities and specificities of delta check limits as discrete panel tests were assessed by random permutation of the original data-set to simulate a wrong blood in tube situation.Results: Generally, as the number of analytes included in a panel increases, the delta check rules deteriorate considerably due to the increased number of false positives, i.e. increased number hypothesis tests performed. To reduce high false-positive rates, patient results may be rejected from autovalidation only if the number of analytes failing the delta check limits exceeds a certain threshold of the total number of analytes in the panel (N). Our study found that the use of the (N2 rule) for panel results had a specificity >90% and sensitivity ranging from 25% to 45% across the four common laboratory panels. However, this did not achieve performance close to some analytes when considered in isolation.Conclusions: The simple N2 rule reduces the false-positive rate and minimizes unnecessary, resource-intensive investigations for potentially erroneous results. [ABSTRACT FROM AUTHOR]

Objectives: Preanalytical processes in pediatric patients are generally manual and associated with a higher risk of error. The optimized delta check rules for detecting misidentified children samples are examined.Methods: Relative difference and absolute different delta check limits were applied on original and reshuffled (to simulate sample mislabeling/mix-up) paired deidentified pediatric results of 57 laboratory tests. The sensitivity, specificity, and accuracy of a range of delta check limits were determined. The delta check limit associated with the highest accuracy was considered optimal.Results: In general, the delta check limits had poor to moderate accuracy (0.50-0.81) in detecting misidentified patient samples. The sensitivity (rule out misidentified sample) quickly deteriorated at increasing delta check limits. At the same time, the specificity (rule in misidentified sample) of the delta check limit was also low. The performance of the relative difference and absolute difference delta check rules was similar.Conclusions: Our findings showed poor delta check performance in the pediatric population. The high false-positive flag rate may lead to wasteful resource-intensive investigations and delay in result reporting. In addition, we observed that the optimized pediatric delta check correlated strongly with within-subject biologic variation, whereas delta check accuracy correlated poorly with index of individuality. [ABSTRACT FROM AUTHOR]

Background: This study aimed to understand the status quo of delta checks in Chinese clinical laboratories through a nationwide online survey. Methods: The survey was divided into two parts. The first part was a general situation survey in which clinical laboratories had to provide information about the laboratories, including delta checks used. In the second part, clinical laboratories were asked to record the delta check alerts generated in their laboratories from June 1st, 2019 to June 30th, 2019. Results: The most frequently used analytes in delta checks were potassium (K), glucose (Glu), creatinine (Cre) for clinical chemistry and hemoglobin (Hgb), platelet (PLT) count and white blood cell (WBC) count for clinical hematology. The median maximum time interval between specimens for all analytes was 5 days. The most commonly used delta check calculation modes in Chinese clinical laboratories were percentage change and absolute change. K and Hgb were the analytes most involved in clinical chemistry and clinical hematology delta check alerts. The most common causes of delta check alerts were that the patients had received treatment, which was followed by the change in the patient's physiological state and interference from hemolysis, lipemia and icterus. The two most common outcomes of delta check alerts were 'no problems found, standard report issued' and 'no problems found, report issued with comment'. Conclusions: This study was the first nationwide survey of delta checks in China, the results of which help us to understand the current situation of delta checks in Chinese clinical laboratories. [ABSTRACT FROM AUTHOR]

Background: The delta check time interval limit is the maximum time window within which two sequential results of a patient will be evaluated by the delta check rule. The impact of time interval on delta check performance is not well studied. Methods: De-identified historical laboratory data were extracted from the laboratory information system and divided into children (≤18 years) and adults (>21 years). The relative and absolute differences of the original pair of results from each patient were compared against the delta check limits associated with 90% specificity. The data were then randomly reshuffled to simulate a switched (misidentified) sample scenario. The data were divided into 1-day, 3-day, 7-day, 14-day, 1-month, 3-month, 6-month and 1-year time interval bins. The true positive- and false-positive rates at different intervals were examined. Results: Overall, 24 biochemical and 20 haematological tests were analysed. For nearly all the analytes, there was no statistical evidence of any difference in the true- or false-positive rates of the delta check rules at different time intervals when compared to the overall data. The only exceptions to this were mean corpuscular volume (using both relative- and absolute-difference delta check) and mean corpuscular haemoglobin (only absolute-difference delta check) in the children population, where the false-positive rates became significantly lower at 1-year interval. Conclusions: This study showed that there is no optimal delta check time interval. This fills an important evidence gap for future guidance development. [ABSTRACT FROM AUTHOR]

Introduction: The delta check quality control measure has been applied in haematology testing for a long time. Although the delta check graph of haematology test had been reported by Berend Houwen in 1989, problems still exist in its clinical application. This study aims to provide a simplified and practical process for the improvement of validity in haematology testing. Methods: We analysed a total of 5260 patients with two sequential complete blood counting results, taken in 1 week, to verify the delta check curve. The delta check equations were simulated using the sigma plot 13.0 software. Validations of test results were performed on patients from the haematology, oncology and other departments. Results: Three equations of the delta check limits for mean corpuscular volume (MCV), haemoglobin, white blood cell and platelets were simulated. The following two limitation lines were selected: limit A (ΔA) and limit B (ΔB), wherein ΔA was to simplify the review rules and ΔB to check random errors. Delta check procedures including rules and equations were established for clinical use, and the re‐inspection rate was reduced in different departments using the delta check model in clinical validation. Conclusion: The simplified delta check limitation formulae are meaningful for data review and may help the laboratory technician optimize workflow and reduce workload. [ABSTRACT FROM AUTHOR]

Background: Blood grouping is the single most important test performed by each and every transfusion service. A blood group error has a potential for causing severe life-threatening complications. A number of process strategies have been adopted at various institutions to prevent the occurrence of errors at the time of phlebotomy, pretransfusion testing, and blood administration. A delta check is one such quality control tool that involves the comparison of laboratory test results with results obtained on previous samples from the same patient. Materials and Methods: We retrieved the records of all transfusion-related incidents reported in our institute, between January 2008 and December 2014. Errors identified as “Failed Delta checks” and their root cause analyses (RCA) were reviewed. Results: A total of 17,034 errors related to blood transfusion were reported. Of these, 38 were blood grouping errors. Seventeen blood group errors were identified due to failed delta checks, where the results of two individually drawn grouping samples yielded different blood group results. The RCA revealed that all of these errors occurred in the preanalytical phase of testing. Mislabeling resulting in wrong blood in tube was the most commonly identified cause, accounting for 11 of these errors, while problems with correct patient identification accounted for 5 failed delta checks. Conclusion: Delta checks proved to be an effective tool for detecting blood group errors and prevention of accidental mismatched blood transfusions. Preanalytical errors in patient identification or sample labeling were the most frequent.

Aims: Establishing the reference change values (RCVs) and validating the delta check auto-verification in the hospital information system (HIS). Materials and Methods: This study was conducted in the Hospital Laboratory-Biochemistry. Fifty-one parameters were analyzed in three phases. Phase I: Delta check reference change values were established. Phase II: Delta check auto-verification was validated in the hospital information system. Phase III: Calculation of test requiring manual verification, true and false positive rates. Results: Out of all the test results, 1.35% failed the RCV-delta check thus requiring manual verification, and the remaining 98.65% were auto-verified. Only 0.12% test results were true positives indicating laboratory error, and 1.23% were false positives and were correlated clinically. Ten percent simulated data results and 0.37% actual patient results were not identified by the newly introduced HIS. Conclusions: RCV-delta check is a refined form of the delta checks used to analyze acceptable analytical and biological variation in laboratories. Majority of tests passed the RCV-delta check auto-verification, implying that very few test reports require manual verification. True positives can be detected in the laboratory. All HISs should be validated before implementing complete auto-verification.

Objectives: Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limitations of conventional methods., Methods: We analyzed five tumor marker test results: alpha-fetoprotein (AFP), cancer antigen 19-9 (CA19-9), cancer antigen 125 (CA125), carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA). A total of 246,261 records were used in the analysis. Of these, 179,929 records were used for model training and 66,332 records for performance evaluation. We developed a misidentification error detection model based on the random forest (RF) and deep neural network (DNN) methods. We performed an in silico simulation with 1 % random sample shuffling. The performance of the developed models was evaluated and compared to conventional delta check methods such as delta percent change (DPC), absolute DPC (absDPC), and reference change values (RCV)., Results: The DNN model outperformed the RF, DPC, absDPC, and RCV methods in detecting sample misidentification errors. It achieved balanced accuracies of 0.828, 0.842, 0.792, 0.818, and 0.833 for AFP, CA19-9, CA125, CEA, and PSA, respectively. Although the RF method performed better than DPC and absDPC, it showed similar or lower performance compared to RCV., Conclusions: Our research results demonstrate that an ML-based delta check method can more effectively detect sample misidentification errors compared to conventional delta check methods. In particular, the DNN model demonstrated superior and stable detection performance compared to the RF, DPC, absDPC, and RCV methods., (© 2023 the author(s), published by De Gruyter, Berlin/Boston.)

Objectives: An unfortunate reality of laboratory medicine is that blood specimens collected from one patient occasionally get mislabeled with identifiers from a different patient, resulting in so-called "wrong blood in tube" (WBIT) errors and potential patient harm. Here, we sought to develop a machine learning-based, multianalyte delta check algorithm to detect WBIT errors and mitigate patient harm.Methods: We simulated WBIT errors within sets of routine inpatient chemistry test results to develop, train, and evaluate five machine learning-based WBIT detection algorithms.Results: The best-performing WBIT detection algorithm we developed was based on a support vector machine and incorporated changes in test results between consecutive collections across 11 analytes. This algorithm achieved an area under the curve of 0.97 and considerably outperformed traditional single-analyte delta checks.Conclusions: Machine learning-based multianalyte delta checks may offer a practical strategy to identify WBIT errors prior to test reporting and improve patient safety. [ABSTRACT FROM AUTHOR]

Objectives: To assess the utility of reference change values (RCVs) as delta check limits.Methods: A total of 1,650,518 paired results for 23 general chemistry test results from June 1, 2014, to October 31, 2016, were analyzed. The RCVs for each analyte were calculated from the analytical imprecision and within-subject biological variation. The percent differences between two consecutive results in one patient were categorized into one of four groups: outpatients, inpatients, emergency care, and general health care. For each, 2.5th and 97.5th percentile values were computed and compared with their RCVs. The distributions were assessed for normality using the Kolmogorov-Smirnov test.Results: Most of the estimated limits were larger than the corresponding RCVs and, furthermore, with notable differences across the groups. Patients in the emergency care group usually demonstrated larger delta percent values than those in the other groups. None of the distributions of the percent differences passed tests of normality when subjected to Kolmogorov-Smirnov analysis.Conclusions: Comparison of estimated RCVs and real-world patient data revealed the pitfalls of applying RCVs in clinical laboratories. Laboratory managers should be aware of the limitations of RCVs and exercise caution when using them. [ABSTRACT FROM AUTHOR]

Background: Blood grouping is the single most important test performed by each and every transfusion service. A blood group error has a potential for causing severe life-threatening complications. A number of process strategies have been adopted at various institutions to prevent the occurrence of errors at the time of phlebotomy, pretransfusion testing, and blood administration. A delta check is one such quality control tool that involves the comparison of laboratory test results with results obtained on previous samples from the same patient. Materials and Methods: We retrieved the records of all transfusion-related incidents reported in our institute, between January 2008 and December 2014. Errors identified as “Failed Delta checks” and their root cause analyses (RCA) were reviewed. Results: A total of 17,034 errors related to blood transfusion were reported. Of these, 38 were blood grouping errors. Seventeen blood group errors were identified due to failed delta checks, where the results of two individually drawn grouping samples yielded different blood group results. The RCA revealed that all of these errors occurred in the preanalytical phase of testing. Mislabeling resulting in wrong blood in tube was the most commonly identified cause, accounting for 11 of these errors, while problems with correct patient identification accounted for 5 failed delta checks. Conclusion: Delta checks proved to be an effective tool for detecting blood group errors and prevention of accidental mismatched blood transfusions. Preanalytical errors in patient identification or sample labeling were the most frequent. [ABSTRACT FROM AUTHOR]

Background Biological variation is used in the calculation of reference change values (RCVs) for a delta check. In this study, we examined the correlation between intra-individual biological coefficients of variation (CV I ) and delta check limits according to population distribution. Methods A total of 1,533,359 paired test results of nine routine chemistry tests were used to make the population distributions of delta percent changes. Their 0.5th, 2.5th, 97.5th, and 99.5th percentiles were then used for delta check limits. Results A large difference was observed between the chemistry tests in the percentage exceeding the delta check limits according to the RCVs. The mean percentage of test results of each test item exceeding the delta check limits of RCV 95% ranged from 12.3% to 40.6%. Delta percent changes of protein, albumin, sodium (Na), potassium (K) and chloride (Cl) showed a symmetric distribution. However, an asymmetric distribution was observed in the delta percent changes of glucose, aspartate transaminase (AST), alanine aminotransferase (ALT) and creatinine. A good correlation was observed between CV I and the delta check limits according to population distribution and a closer correlation was observed when using the test items with CV I of < 5.0%. Conclusions Intra-individual biological coefficients of variation (CV I ) would be useful for the establishment of delta check limits. [ABSTRACT FROM AUTHOR]

Autoverification is a process of using computer-based rules to verify clinical laboratory test results without manual review. But to date, there are few published articles on the use of autoverification over the course of years in a clinical laboratory. In our study, we firstly described the development and implementation of autoverification rules for enzyme-linked immunosorbent assay (ELISA) results of hepatitis B virus (HBV) serological markers in a clinical immunology laboratory. We designed the autoverification rules for HBV by using Boolean logic on five clinically used serological markers in accordance with the framework of AUTO-10A, issued by the American Clinical Laboratory Standards Institute in 2006. The rules were written into the laboratory information system (LIS) and installed in the computer, so we could use the LIS to screen the test results. If the results passed the autoverification rules, they could be sent to doctors immediately. To evaluate the autoverification rules, we applied the real-time data of 11,585 patients with the autoverification rules. The autoverification rate of the five HBV serological markers was 79.5%. Furthermore, the turnaround time (TAT) was reduced by 38% (78 minutes vs. 126 minutes). The error rate was nearly eliminated. These results show that using LIS with autoverification rules can shorten TAT, enhance efficiency, and reduce manual review errors. [ABSTRACT FROM AUTHOR]

The article presents a study on the contribution of electronic alerts (e-alerts) to highlight acute kidney injury (AKI) based on alterations in serum creatinine which may facilitate earlier recognition and treatment, and sophisticated algorithms for AKI detection. According to Acute Kidney Injury Network (AKIN) criteria, a 12-day period alert determine baseline creatinine and stage AKI. It is inferred that a simple automated delta check can monitor AKI in real time.

Delta check is an electronic error detection tool. It compares the difference in sequential results within a patient against a predefined limit, and when exceeded, the delta check rule is considered triggered. The patient results should be withheld for review and troubleshooting before releasing to the clinical team for patient management. Delta check was initially developed as a tool to detect wrong-blood-in-tube (sample misidentification) errors. It is now applied to detect errors more broadly within the total testing process. Recent advancements in the theoretical understanding of delta check has allowed for more precise application of this tool to achieve the desired clinical performance and operational set up. In this Chapter, we review the different pre-implementation considerations, the foundation concepts of delta check, the process of setting up key delta check parameters, performance verification and troubleshooting of a delta check flag., (Copyright © 2023. Published by Elsevier Inc.)

Objective: In this study we give a detailed description of how to construct verification rules and then evaluate the benefits brought to the laboratory.Methods: All logic processes and verification rules are constructed in middleware with reference to the CLSI Auto10-A Guideline. There were 569,001 patient test results collected to establish the range of the limit check, delta check, and the consistence rule check.Results: Daily results show the autoverification (AV) passing rate of all test results to be 92%∼95%. About 80% of test reports can be auto-released. Conclusions: Individual differences in the verification of test results are eliminated, turnaround time (TAT) is shortened, and full-time equivalent (FTE) are reduced, thus enabling medical technologists to devote more time and effort to handling intercepted test reports, which, in turn, improves the quality of patient care. [ABSTRACT FROM AUTHOR]

Abstract: Background: Despite their widespread use, the performance of delta check rules is rarely evaluated because errors are rare and lack a gold standard for detection. In this study we used a simulation-based approach to compare strategies for empirically defining criteria for univariate delta checks, and assessed the performance of these rules for detecting mislabeled specimens in 2 inpatient populations. Methods: We performed simulations using historical laboratory test results by randomly sampling pairs of specimens successively drawn from the same patient or two different patients. We evaluated the performance of delta check rules using a variety of thresholds, including those currently in use in our laboratory. Result: Mean corpuscular volume had the highest positive predictive value for specimen mislabeling, and produced the fewest false positives. Conversely, rules using other laboratory tests had considerably poorer performance. Several of the “best guess” thresholds historically used in our laboratory, notably those for potassium and anion gap, were predicted to have extremely low yields. In addition, rule performance was not consistent between the two patient populations. Conclusions: The low yield of delta checks based on any single analyte should prompt careful evaluation of their practical utility. Furthermore, our results indicate that it may not be possible to generalize delta rules across institutions. [Copyright &y& Elsevier]

This paper introduces an extensive revision of the types of components of biological variation (BV), i.e. intraindividual (random fluctuation of analytes around the setting point of each individual) and interindividual (overall variation from the different person's setting point), briefly explains estimation of the magnitude of within- and between subject BV in healthy and non-healthy subjects, details the eight common applications of BV estimates and discusses the most debated points of interests. The aim is to discuss how quality specifications derived from BV determined in healthy individuals are attainable with current technology and in what cases data from non-healthy subjects should be used. Finally, the paper promotes further development of BV application, such as notifying doctors about changes in patient status. [ABSTRACT FROM AUTHOR]

Quality control plays a vital role helping to ensure the reliability of laboratory test results. The application of statistical quality control has been a component of laboratory medicine for approximately 50 years. Many of the control rules based on the early applications of statistical quality control have remained essentially unchanged since their initial introduction. Optimization of quality control rules can vary depending on the application for which a test is to be used. This review explores the various applications of laboratory quality control procedures and their role in identifying laboratory error. The ubiquitous use of computers in today's laboratories has enabled the development of more sophisticated means of assessing laboratory quality. The use of the Six Sigma technique and its adoption by the laboratory community is one example. Other examples include the use of patient-derived quality control procedures as a means of assessing laboratory performance. Early examples of these types of applications include use of Bull's algorithm, anion gap measurements, and delta checking. More recent applications include the correlation of laboratory test results, the average of normals procedure, and the Bhattacharya method. [ABSTRACT FROM AUTHOR]

The wealth of quantitative data on random biological variation has been used for setting quality specifications, assessing the utility of conventional reference values, and deciding of the significance of changes in serial laboratory results. Most analytes have marked individuality and this makes conventional population-based reference values of low utility. In consequence, reference limits are not ideal for autoverification strategies. Clinical decision limits may be better criteria for holding results for verification by laboratory professionals. Changes in serial results are significant only when the reference change value is exceeded. Such values can be generated by all laboratories and can be implemented, not only to flag reports, but also in delta checking and autoverification since these are objective rather than empirical. We have put these considerations into operation into our laboratory. Apart from special cases, our general approach is that results flagged as having changed 0.05< P<0.01, flagged as just outside the reference limits, or not flagged in any way, are autoverified and reported to the user without intervention. Only results outside pre-set clinical limits and those that have changed highly significantly P<0.01 are held for verification by clinical scientists and medical staff. This strategy allows autoverification of ca. 60% of reports. [ABSTRACT FROM AUTHOR]

Introduction: Delta checks use two specimen test results taken in succession in order to detect test result changes greater than expected physiological variation. One of the most common and serious errors detected by delta checks is specimen mix-up errors. The positive and negative predictive values of delta checks for detecting specimen mix-up errors, however, are largely unknown. Materials and Methods: We addressed this question by first constructing a stochastic dynamic model using repeat test values for five analytes from approximately 8000 inpatients in Calgary, Alberta, Canada. The analytes examined were sodium, potassium, chloride, bicarbonate, and creatinine. The model simulated specimen mix-up errors by randomly switching a set number of pairs of second test results. Sensitivities and specificities were then calculated for each analyte for six combinations of delta check equations and cut-off values from the published literature. Results: Delta check specificities obtained from this model ranged from 50% to 99%; however the sensitivities were generally below 20% with the exception of creatinine for which the best performing delta check had a sensitivity of 82.8%. Within a plausible incidence range of specimen mix-ups the positive predictive values of even the best performing delta check equation and analyte became negligible. Conclusion: This finding casts doubt on the ongoing clinical utility of delta checks in the setting of low rates of specimen mix-ups.

Objectives: Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection of sample mix-ups. The aim of the study was to develop a highly accurate DC method based on designed deep learning to detect sample mix-up., Methods: A total of 22 routine hematology test items were adopted for the study. The hematology test results, collected from two hospital laboratories, were independently divided into training, validation, and test sets. By selecting six mainstream algorithms, the Deep Belief Network (DBN) was able to learn error-free and artificially (intentionally) mixed sample results. The model's analytical performance was evaluated using training and test sets. The model's clinical validity was evaluated by comparing it with three well-recognized statistical methods., Results: When the accuracy of our model in the training set reached 0.931 at the 22nd epoch, the corresponding accuracy in the validation set was equal to 0.922. The loss values for the training and validation sets showed a similar (change) trend over time. The accuracy in the test set was 0.931 and the area under the receiver operating characteristic curve was 0.977. DBN demonstrated better performance than the three comparator statistical methods. The accuracy of DBN and revised weighted delta check (RwCDI) was 0.931 and 0.909, respectively. DBN performed significantly better than RCV and EDC. Of all test items, the absolute difference of DC yielded higher accuracy than the relative difference for all methods., Conclusions: The findings indicate that input of a group of hematology test items provides more comprehensive information for the accurate detection of sample mix-up by machine learning (ML) when compared with a single test item input method. The DC method based on DBN demonstrated highly effective sample mix-up identification performance in real-world clinical settings., (© 2021 Rui Zhou et al., published by De Gruyter, Berlin/Boston.)

Objectives: Peripheral blood lymphocyte subsets are important parameters for monitoring immune status; however, lymphocyte subset detection is time-consuming and error-prone. This study aimed to explore a highly efficient and clinically useful autoverification system for lymphocyte subset assays performed on the flow cytometry platform., Methods: A total of 94,402 lymphocyte subset test results were collected. To establish the limited-range rules, 80,427 results were first used (69,135 T lymphocyte subset tests and 11,292 NK, B, T lymphocyte tests), of which 15,000 T lymphocyte subset tests from human immunodeficiency virus (HIV) infected patients were used to set customized limited-range rules for HIV infected patients. Subsequently, 13,975 results were used for historical data validation and online test validation., Results: Three key autoverification rules were established, including limited-range, delta-check, and logical rules. Guidelines for addressing the issues that trigger these rules were summarized. The historical data during the validation phase showed that the total autoverification passing rate of lymphocyte subset assays was 69.65% (6,941/9,966), with a 67.93% (5,268/7,755) passing rate for T lymphocyte subset tests and 75.67% (1,673/2,211) for NK, B, T lymphocyte tests. For online test validation, the total autoverification passing rate was 75.26% (3,017/4,009), with 73.23% (2,191/2,992) for the T lymphocyte subset test and 81.22% (826/1,017) for the NK, B, T lymphocyte test. The turnaround time (TAT) was reduced from 228 to 167 min using the autoverification system., Conclusions: The autoverification system based on the laboratory information system for lymphocyte subset assays reduced TAT and the number of error reports and helped in the identification of abnormal cell populations that may offer clues for clinical interventions., (© 2021 Walter de Gruyter GmbH, Berlin/Boston.)

Objective: Tumor markers are used to monitor disease progression and determine the responsiveness to cancer treatment. However, there are no standardized criteria for monitoring serial tumor marker measurements. Herein, we have developed a monitoring system for interpreting changes in tumor markers using overlapping 95% confidence intervals (CIs)., Methods: Two-year data, including 117,289 results for 11 tumor markers in our laboratory, were analyzed. CI ranges for each tumor marker were set based on biological variation, and data were analyzed for each patient assessed at health check-ups and clinics, individually and overall., Results: The 95 th percentile cut-offs for each tumor marker were much higher in the clinic group than in the health check-up group. In decreasing order, the percentages of results with no overlap in 95% CIs were thyroglobulin antigen, 14.9%; protein induced by vitamin K absence-II (PIVKA), 11.9%; and prostate-specific antigen, 9.8%. After correction using the reference interval, the percentages decreased to less than 5%, except for PIVKA (10.9%)., Conclusion: We suggest that our monitoring system can serve as a criterion for the auto-verification of tumor markers. Further studies are required to validate and demonstrate this concept in real clinical situations using actual clinical data reflecting disease progression in cancer patients and responsiveness to cancer treatment., (© 2021 by the Association of Clinical Scientists, Inc.)

Background: Delta check is a patient-based QC tool for detecting errors by comparing current and previous test results of patient. Reference change value (RCV) is adopted in guidelines as method for delta check, but the performance is not verified. We applied RCV-based delta check method to patients' data and modified for application., Materials and Methods: Reference change value were calculated using results of internal QC materials and biological variation data. Test results of 17 analytes in inpatients, outpatients, and health examination recipients were collected. The detection rates of currently used delta check method and those of RCV-based method were compared, and the methods were modified., Results: Reference change value-based method had higher detection rates compared to conventional method. Applied modifications reduced detection rates. Removing the pairs of results within reference interval reduced detection rates (0.42% ~ 10.92%). When RCV was divided by time interval, the detection rates were similar to prior rates in outpatients (0.19% ~ 1.34%). Using RCV multiplied by twice the upper limit of reference value as cutoff reduced the detection rate (0.07% ~ 1.58%)., Conclusions: Reference change value is a robust criterion for delta check and included in clinical laboratory practice guideline. However, RCV-based method generates high detection rates which increase workload. It needs modification for use in clinical laboratories., (© 2020 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.)

The delta check is used in clinical laboratories to detect specimen mislabeling or misidentification. However, test results can differ markedly pre-versus post-dialysis, and the delta check in fact typically has little value, as well as being labor intensive. We propose the "blood urea nitrogen/creatinine change ratio" (BCCR) as a new delta check method for dialysis cases. A total of 1,116 specimens with same-day pre- and post-dialysis test results were analyzed. Also, the performance of the BCCR was evaluated by simulating specimen mix-up. Among the 1,116 specimens, the median BCCR was 0.80 and the 2.5th, 25th, 75th, and 97.5th percentiles were 0.62, 0.74. 0.84, and 0.93, respectively. In the simulated misidentification dataset, the median BCCR was 0.79 and the 2.5th, 25th, 75th, and 97.5th percentiles were 0.34, 0.61, 1.02, and 1.77, respectively. When the 2.5th and 97.5th percentile values of the BCCR were set as the upper and lower limits, the delta check detected 61.0% of the simulated misidentified specimens. In summary, the BCCR enables detection of changes in important measures and could reduce the rate of false-positives., Competing Interests: None., (IJCEP Copyright © 2020.)