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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]

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]

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]

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]