BACKGROUND: We sought to detect specimen mix-up by developing a new cumulative delta-check method applicable to a mixture of test items with heterogeneous units and distribution patterns. METHODS: The distributions of all test results were successfully made Gaussian using power transformation. Values were then standardized into z-score (zx) based on reference interval (RI) so that limits of RI take zx=±1.96. To find a weight for summing absolute value of delta between current and previous zx (Dz), we evaluated the distribution of Dz. Its central portion was always regarded as Gaussian despite the presence of symmetrical long tails. Thus, an adjusted SD (aSD) representing the center was estimated with an iterative method. By setting 1/aSD2 as a weight factor, we computed a weighted mean of Dz as an index for specimen mix-up (wCDI). RESULTS: The performance of wCDI was evaluated, using a model laboratory database consisting of 32 basic test items, by a simulation study generating artificial cases of mix-up. When wCDI was computed from three commonly ordered test sets consisting of 6-9 items each, its diagnostic efficiency in detecting the artificial cases was 0.937-0.967 expressed as area under ROC curves (AUC). When the performance of wCDI was evaluated simply by the number of test items (p) included in the computation, AUC gradually increased from 0.944 (p=5) to 0.976 (p=8). However, when p≥10, AUC stayed at approximately 0.98. CONCLUSIONS: wCDI was proven to be highly effective in uncovering cases of specimen mix-up. The diagnostic efficiency of wCDI depends only on the number of test items included in the computation.
BACKGROUND: We sought to detect specimen mix-up by developing a new cumulative delta-check method applicable to a mixture of test items with heterogeneous units and distribution patterns. METHODS: The distributions of all test results were successfully made Gaussian using power transformation. Values were then standardized into z-score (zx) based on reference interval (RI) so that limits of RI take zx=±1.96. To find a weight for summing absolute value of delta between current and previous zx (Dz), we evaluated the distribution of Dz. Its central portion was always regarded as Gaussian despite the presence of symmetrical long tails. Thus, an adjusted SD (aSD) representing the center was estimated with an iterative method. By setting 1/aSD2 as a weight factor, we computed a weighted mean of Dz as an index for specimen mix-up (wCDI). RESULTS: The performance of wCDI was evaluated, using a model laboratory database consisting of 32 basic test items, by a simulation study generating artificial cases of mix-up. When wCDI was computed from three commonly ordered test sets consisting of 6-9 items each, its diagnostic efficiency in detecting the artificial cases was 0.937-0.967 expressed as area under ROC curves (AUC). When the performance of wCDI was evaluated simply by the number of test items (p) included in the computation, AUC gradually increased from 0.944 (p=5) to 0.976 (p=8). However, when p≥10, AUC stayed at approximately 0.98. CONCLUSIONS: wCDI was proven to be highly effective in uncovering cases of specimen mix-up. The diagnostic efficiency of wCDI depends only on the number of test items included in the computation.