Integrating moving average control procedures into the risk-based quality control plan in small-volume medical laboratories

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

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.

Advances in internal quality control

Quality control practices in the modern laboratory are the result of significant advances over the many years of the profession. Major advance in conventional internal quality control has undergone a philosophical shift from a focus solely on the statistical assessment of the probability of error identification to more recent thinking on the capability of the measurement procedure (e.g. sigma metrics), and most recently, the risk of harm to the patient (the probability of patient results being affected by an error or the number of patient results with unacceptable analytical quality). Nonetheless, conventional internal quality control strategies still face significant limitations, such as the lack of (proven) commutability of the material with patient samples, the frequency of episodic testing, and the impact of operational and financial costs, that cannot be overcome by statistical advances. In contrast, patient-based quality control has seen significant developments including algorithms that improve the detection of specific errors, parameter optimization approaches, systematic validation protocols, and advanced algorithms that require very low numbers of patient results while retaining sensitive error detection. Patient-based quality control will continue to improve with the development of new algorithms that reduce biological noise and improve analytical error detection. Patient-based quality control provides continuous and commutable information about the measurement procedure that cannot be easily replicated by conventional internal quality control. Most importantly, the use of patient-based quality control helps laboratories to improve their appreciation of the clinical impact of the laboratory results produced, bringing them closer to the patients.Laboratories are encouraged to implement patient-based quality control processes to overcome the limitations of conventional internal quality control practices. Regulatory changes to recognize the capability of patient-based quality approaches, as well as laboratory informatics advances, are required for this tool to be adopted more widely.

Integrating moving average control procedures into the risk-based quality control plan in small-volume medical laboratories

The modern approach to quality control (QC) in medical laboratories implies the development of a risk-based control plan. This paper aims to develop a risk-based QC plan for a laboratory with a small daily testing volume and to integrate the already optimized moving average (MA) control procedures into this plan. A multistage bracketed QC plan for ten clinical chemistry analytes was made using a Westgard QC frequency calculator. Previously, MA procedures were optimized by the bias detection simulation method. Aspartate aminotransferase, HDL-cholesterol and potassium had patient-risk sigma metrics greater than 6, albumin and cholesterol greater than 5, creatinine, chlorides, calcium and total proteins between 4 and 5, and sodium less than 4. Based on the calculated run sizes and characteristics of optimized MA procedures, for 6 tests, it was possible to replace the monitoring QC procedure with an MA procedure. For the remaining 4 tests, it was necessary to keep the monitoring QC procedure and introduce MA control for added security. This study showed that even in a laboratory with a small volume of daily testing, it is possible to make a risk-based QC plan and integrate MA control procedures into that plan.