title: A novel weighted cumulative delta-check method for highly sensitive detection of specimen mix-up in the clinical laboratory.
abstract: 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.
authors: Graduate School of Health Care Science, Jikei Institute, Osaka, Japan.
authors: Teppei
authors: T
authors: Yamashita
authors: Kiyoshi
authors: K
authors: Ichihara
authors: Ayaho
authors: A
authors: Miyamoto
mesh_terms: D019540:Area Under Curve; D002984:Clinical Laboratory Information Systems / Q000592:standards; D057225:Data Mining; D006801:Humans; D007755:Laboratories, Hospital / Q000592:standards*; D016011:Normal Distribution; D011786:Quality Control; D012372:ROC Curve; D012016:Reference Values