Multivariate delta check method for detecting specimen mix-up.

Abstract

Among laboratory mistakes, "specimen mix-up" is the most frequent and the most serious. According to the Clinical Chemistry Laboratory Error Report of Toranomon Hospital, specimen mix-up was often detected when there were many large discrepancies between the results of a test and the results of a previous test. We present here a checking method to detect specimen mix-up. The method, which we call the "multivariate delta check" method, is an extension of the "delta check" method first presented by Nosanchuk and Gottmann (Am. J. Clin. Pathol. 62:707, 1974). Clinical evaluation has demonstrated the effectiveness of the method.

Delta Check Practices and Outcomes: A Q-Probes Study Involving 49 Health Care Facilities and 6541 Delta Check Alerts

“…It is also consistent with previous studies that show added value by use of multivariate delta check methods. 6 However, these types of calculations were not used by any of the participants in this study, and may therefore have less practical value than expected, perhaps because of limited capabilities of current laboratory information systems. Nevertheless, innovative methods, using complex formulas on multiple analytes, show promise for improving the value of delta checks, especially for detection of mislabeling problems.…”

“…Although the use of delta check calculations involving rate of change or multivariate methods has been described, 6,19,20 all study participants used only absolute and percentage change calculations. The median changes reported by participants for absolute and percentage changes were similar to criteria recommend by Kim et al, 21 except these investigators suggested using rate of change for enzymes and renal function tests (creatinine and blood urea nitrogen).…”

“…4 Because a large proportion of delta check alerts are not associated with any identified error, 1,2,5 it is important to select analytes and set parameters that are as effective as possible for detecting testing problems. Although several studies have investigated the use of delta checks for identifying mislabeled specimens, 1,4,6 information is lacking about routine delta check practices such as the types of tests commonly used, calculation methods, and sensitivity for detecting problems other than labeling errors. This Q-Probes study provides new information for understanding delta check practices, the effectiveness of delta check alerts for detecting problems, and the effects of delta check alerts on testing outcomes among a diverse group of clinical laboratories.…”

To Delta Check or Not to Delta Check? That Is the Question

“…given the improvements in automation and technology that have decreased the frequency of laboratory errors (3). Multivariate approaches have been highlighted as an alternative to improve detection of specimen mix-up errors, but often rely on complex statistical analyses that may be difficult to implement in a routine clinical laboratory (4).…”

Clinical Evaluation of an Algorithm for Short Sample Detection on a Multi-analyte Panel Using a Point-of-Care Analyzer

“…In addition, use of laboratory data from patients has been helpful in detecting analytical errors as well as pre-and postanalytical errors. Examples of error detection schemes that rely on use of patient-derived data include checking of patient test results against predetermined limits or against previous values generated for the same patient (1,2 ). These detection schemes, referred to as limit checks and delta checks, respectively, are in common use.…”