Laboratory quality control and patient safety

Jeremie M. Gras

• Editore: De Gruyter
• Anno: 2017
• ISBN:
  9783110384536
• Edizione: 1
• Argomento: Medicine
• Categoria: Clinical Medicine

Every clinical laboratory devotes considerable resources to Quality Control. Recently, the advent of concepts such as Analytical Goals, Biological Variation, Six Sigma and Risk Management has generated a renewed interest in the way to perform QC. However, laboratory QC practices remain highly non-standardized and a lot of QC questions are left unanswered. The objective of this book is to propose a roadmap for the application of an integrated QC protocol that ensures the safety of patient results in the everyday lab routine.

1How do we guarantee that laboratory results are correct? The impact of laboratory quality control on patient safety

It is estimated that clinical laboratory results play a role in approximately 70% of medical decisions [1]. Therefore, it is critical for laboratories to generate correct results that will have a positive impact on patient care. An error in a laboratory test may generate misdiagnosis that could lead to complications such as delay in patient care; in the worst-case scenario, a laboratory error could lead to severe patient harm, or even patient death. Testing in laboratory medicine is divided in three phases: pre-analytical, analytical, and post-analytical. The pre-analytical phase includes test ordering, phlebotomy, and data entry in the laboratory information system. The analytical phase refers to testing on laboratory instruments that will generate patient results. The post-analytical phase consists of the generation of laboratory reports, transmission to laboratory and hospital information systems (LIS and HIS), and communication of laboratory results to clinicians in an acceptable timeframe. Laboratory errors occurs mostly in the pre-analytical phase (46%–68.2%), followed by errors in the post-analytical phase (15.8%–47%), and finally the analytical phase (7%–13%) [2]. In recent years, the number of errors in the analytical phase has decreased due to automation and improvements in analytical technology. Although the error rate remains limited for the analytical phase, it should be mentioned that an unnoticed error occurring in that phase may have an impact on a large number of patients. This is especially true for modern analytical platforms that perform hundreds of tests per hour. Therefore, the control of the analytical phase remains pivotal for clinical laboratorians.

Medical laboratories were pioneers in applying statistical quality control (QC) principles to monitor the correctness of the results that they produce. The history of QC in clinical laboratories is summarized in Tab. 1.1 [3].

The most important instrument to guarantee the quality of laboratory results is statistical QC. It is also called conventional QC or internal QC (iQC) and is the subject of chapter 2. iQC is the periodic testing of a stable control material, which may be provided by the instrument and reagent manufacturer or by an independent QC provider. The results obtained with the QC material are plotted on a chart, which is called the Levey-Jennings chart (LJ chart) in laboratories. Table 1.2 describes the different tools available for clinical laboratories to monitor the quality of their testing. The first step in the design of a QC protocol is to select an appropriate analytical performance specification (APS). APS, expressed in percentage, represents the maximum variation that is tolerable in the expression of a test result, without having a negative impact in test interpretation, and ultimately, patient care. APSs provide an objective specification of the level of quality required for a clinical laboratory test. APS will also be needed to evaluate patient impact following QC failure. APS, a key information in laboratory medicine, have been the subject of two consensus conferences (Tab. 1.1) and are described in section 2.1.

Tab. 1.1: QC timeline.

Year	QC development
1950	Application of statistical QC concepts to clinical laboratories by Levey and Jennings
1952	Monitoring of individual QC value on a control chart by Henry and Sagalove
1965	Description of average of normals (AoN) by Hoffman
1977	Determination QC rules performance by Westgard
1981	Use of multiple QC rules by Westgard
1990	Introduction of the concept of total error allowable (TEa) by Westgard
1999	Stockholm Consensus Conference on quality requirements
2005	Application of Six Sigma to clinical laboratories by Westgard
2011	First edition of the CLSI EP23 document on QC based on risk management
2012	Second version of the ISO 15189 international guideline for quality requirements in clinical laboratories
2014	Milan Consensus Conference on analytical performance specifications (APSs)
2016	Fourth edition of the CLSI C24 document, the reference on statistical QC in clinical laboratories

APS selection will have an impact on the selection of different QC rules that may be applied on an LJ chart to identify QC failures or out-of-control conditions. Such QC failures may occur following technical problems on components on analytical systems, which are quite complex nowadays. QC rules have different sensitivity and specificity to detect medically significant errors and are discussed in section 2.2. Six Sigma is a management methodology that may be applied to laboratory medicine [4]. An important application in clinical laboratories is the quantification of laboratory test performance on the Sigma scale. Sigma metrics will define which QC rules need to be used for a specific test and are discussed in section 2.3. Apart from iQC, laboratories may use additional tools to verify correctness of laboratory results. These include patient-based QC, which uses individual or multiple patient results to supplement statistical QC. Chapter 3 describes the multiple available QC methodologies using patient data. An important component of quality assurance in clinical laboratories is based on the comparison of laboratory performance with its peers. This may be achieved by external quality assessment (EQA), also called proficiency testing (PT), or by inter-laboratory comparison of iQC data. These tools are described in chapter 5. Over the years, clinical laboratory testing has become a specialized area of medicine that includes special testing areas such as serology, molecular diagnostics, and point-of-care testing (POCT). These different laboratory disciplines have special issues regarding potential applicable QC protocols. QC for several specific laboratory departments is described in chapter 4. ISO 15189:2012 is the reference document on quality requirements for a clinical laboratory [5]. This standard requires iQC and participation to EQA/PT programs for laboratories seeking accreditation. However, it does not give practical guidance regarding how to fulfill these requirements. Chapter 6 deals with ISO 15189 requirements regarding QC and EQA, and describes the most important elements of reference documents regarding QC [6, 7] and EQA [8]. Articles that describe practical situations encountered in QC management are seldom. Chapter 7 provides many practical examples about the design of a QC protocol and management of QC failures. Future possible developments of laboratory QC are discussed in chapter 8. Finally, chapter 9 will summarize the most important messages regarding QC in medical laboratories, and chapter 10 provides a glossary of frequently used QC terms.

Tab. 1.2: Instruments used to guarantee correct laboratory results.

Tool	Use	Chapter/section
Statistical QC	Monitor analytical performance of laboratory analyzers	2
APSs	Set goals for test performance Define objectives for a QC protocol Verify patient impact following QC failure	2.1
QC rules	Detect laboratory errors in the analytica...