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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]

• 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: 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]

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.

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]

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]