Development and Evaluation of “The Delta Plus-Minus Even Distribution Check”: A Novel Patient-Based Real-Time Quality Control Method for Laboratory Tests

Author:

Hatanaka Noriko1,Yamamoto Yoshikazu1,Shiozaki Yuya2,Kuramura Eiji2,Nagai Naoharu2,Kondo Akira1,Kamioka Mikio2

Affiliation:

1. Department of Clinical Laboratory Science, Faculty of Health Care, Tenri University , Tenri-city, Nara , Japan

2. Department of Laboratory Medicine, Tenri Hospital , Tenri-city, Nara , Japan

Abstract

Abstract Background Laboratory testing of large sample numbers necessitates high-volume rapid processing, and these test results require immediate validation and a high level of quality assurance. Therefore, real-time quality control including delta checking is an important issue. Delta checking is a process of identifying errors in individual patient results by reviewing differences from previous results of the same patient (Δ value). Under stable analytical conditions, Δ values are equally positively and negatively distributed. Methods The previous 20 Δ values from 3 tests (cholesterol, albumin, and urea nitrogen) were analyzed by calculating the R-value: “the positive Δ value ratio minus 0.5.” This method of monitoring optimized R-values is referred to as the even-check method (ECM) and was compared with quality control (QC) testing in terms of error detection. Results Bias was observed on 4 of the 120 days for the 3 analytes measured. When QC detected errors, the ECM captured the same systematic errors and more rapidly. In contrast, the ECM did not generate an alarm for the one random error that occurred in QC. While QC did not detect any errors, the percentage of R-values exceeding the acceptable range was under 2%, the number of days generating alarms was between 16 and 21 days, with short alarm periods, and a median number of samples per alarm period between 7 and 9 samples. Conclusions The ECM is a practical real-time QC method, controlled by setting R-value conditions, that quickly detects bias values.

Publisher

Oxford University Press (OUP)

Subject

General Medicine

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