With advances in automated technology and new clinical trials, with increasing cost, manpower and time pressures, quality assurance may seem like another complication in an already difficult puzzle. It is under precisely these conditions however, that quality assurance will provide increased reliability for the user in an environment with multiple variables.
Quality assurance of analytical systems is important to ensure the accuracy and precision of test results. Analysers are calibrated using serum-based samples to mimic the matrix of the patient or study participant and may only be required once every few days. However, to ensure accuracy of the detection on a daily basis, quality control serum is used to check the analyser is still accurately calibrated and the results are reliable. Of course, the number of times you are required to run controls depends on the reliability and quality of the analyser being tested. This will be determined through running a series of controls and establishing the correct interval to ensure reliable results.
Control sera contains the required levels of an analyte to validate each test result derived from the analyser, over its clinically significant ranges. Multi-sera is simply serum that contains a mix of different analytes being tested on the analyser. This makes quality control much quicker, simpler and more cost-effective when running a number of tests.
The choice of supplier can have a massive influence on the ease of use and suitability of quality control sera for trials being undertaken. As with all essential pieces of laboratory equipment, decisions on the most appropriate sera and QA schemes should not be taken lightly. The specific requirements for each trial must be carefully assessed and a determination made of the most appropriate internal quality assessment, peer-reviewed or external QA schemes.
Quality control is defined as a process to identify errors in a procedure. This is a reactive process, where errors have already occurred. Without quality control, errors in a clinical setting can lead to patient mis-diagnosis, delays in treatment or administration of the wrong treatment and increased costs associated with retesting. In the US alone, it has been estimated that avoidable retests cost ~$200 million per annum. Even a small calibration bias can have a dramatic effect on treatment rates. For example, a 1% bias in cholesterol testing can result in a 5% increase in patients exceeding the treatment threshold. If the bias increases to 3%, this leads to a 15% increase in patients exceeding this threshold, with a significant increase in cost incurred to the supporting healthcare system. This is where quality assurance schemes are vital, both in a clinical setting and in clinical trials. Quality assurance schemes are essentially procedures designed to ensure that a study being performed is compliant and that data being generated is accurate. This ensures the reliability of test results, to give the best patient care.
In the design of trials, there are two distinct groups of factors to be considered, both of which can be controlled with careful preparation. Pre-analytical factors include patient recruitment and exclusion criteria, correct patient data collection, detailing personal and family medical history, current treatments being taken, etc. Correct collection, processing and storage of the most appropriate samples are also paramount, but often overlooked. Quality control, however, focuses on the analytical phase of a trial, i.e. affecting the actions that take place within the laboratory.