An approach to optimize delta checks in test panels – The effect of the number of rules included

Rui Zhen Tan, Corey Markus, Tze Ping Loh

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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 ((Formula presented.) 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 (Formula presented.) rule reduces the false-positive rate and minimizes unnecessary, resource-intensive investigations for potentially erroneous results.

Original languageEnglish
Pages (from-to)215-222
Number of pages8
JournalAnnals of Clinical Biochemistry
Issue number3
Publication statusPublished - 1 May 2020
Externally publishedYes


  • analytical error
  • autoverification
  • Delta check
  • laboratory error
  • postanalytical error
  • preanalytical error
  • sample mix-up
  • wrong blood in tube


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