Patient-Based Real-Time Quality Control: Review and Recommendations

Abstract

For many years the concept of patient-based quality control (QC) has been discussed and implemented in hematology laboratories; however, the techniques have not been widely implemented in clinical chemistry. This is mainly because of the complexity of this form of QC, as it needs to be optimized for each population and often for each analyte. However, the clear advantages of this form of QC, together with the ongoing realization of the shortcomings of “conventional” QC, have driven a need to provide guidance to laboratories to assist in deploying patient-based QC. This overview describes the components of a patient-based QC system (calculation algorithm, block size, truncation limits, control limits) and the relationship of these to the analyte being controlled. We also discuss the need for patient-based QC system optimization using patient data from the individual testing laboratory to reliably detect systematic errors while ensuring that there are few false alarms. The term patient-based real-time quality control covers many activities that use data from patient samples to detect analytical errors. These activities include the monitoring of patient population parameters such as the mean or median analyte value or using single within-patient changes such as the delta check. In this report, we will restrict the discussion to population-based parameters. This overview is intended to serve as a guide for the implementation of a patient-based QC system. The report does not cover the clinical evaluation of the population.

A direct comparison of patient-based real-time quality control techniques: The importance of the analyte distribution

“…There is growing interest in PBRTQC with a number of approaches that can be applied. 2 There are currently a number of barriers to widespread implementation of PBRTQC with time, statistical skills and software support required to optimize and implement a PBRTQC program. 2 We show that managing the ‘population problem’ is not so much about excluding portions of the population but identifying where a population distribution is skewed and variance can be reduced by appropriate transformation.…”

“…6 and suggested as initial limits in recent recommendations. 2 Briefly, truncation limits are placed at ±4 standard deviations from the patient mean for roughly symmetrical result distributions in order to reduce the influence of outliers on the MA. Truncation limits appear in Table 1.…”

“…1 PBRTQC has a number of purported advantages over traditional quality control (QC) including commutability, cost and the opportunity for real-time monitoring. 1,2 All PBRTQC techniques suffer from what has been termed the ‘population problem,’ 1 in which variations in the patient population by time of day, week or month due to phlebotomy schedules or outpatient clinics cannot be distinguished from shifts in analytical performance. 1 Censoring data from certain patient populations (e.g.…”

“…hospital inpatients, intensive care, dialysis unit) can be used but may lead to a reduction in the number of data points in small hospital laboratories with a high number of inpatient samples, delaying the detection of bias. 2…”

When internal quality control is insufficient or inefficient: Consider patient-based real-time quality control!

“…More recently, this term has been used particularly in the context of moving-average or average-ofnormal-related techniques to support preanalytical and analytical quality control. 13 These techniques average multiple obtained results using various types of algorithms to reduce the population variation, thereby enabling the detection of systematic errors. Algorithms used include the mean, the median, the exponentially weighted moving average and the XbarB algorithm described by Bull et al 12,14 Even though these techniques were described many decades ago, 14,15 very few laboratories have adopted them, although to some extent the Bull's algorithm has been used for quality control of blood cell indices.…”