A female laboratorian in a lab, sitting at a desk, and working on a computer

It’s nearly impossible to evaluate quality in laboratory medicine without having analytical quality goals defined in advance. Quality goals, also expressed as total allowable error (TEa), specify the maximum amount of error—both imprecision and bias combined—that is allowed for an assay. Examples include evaluating new analytical methodology or equipment for patient testing, designing a quality control strategy, or evaluating instrument comparability.

Clinical laboratory personnel use TEa routinely: in evaluating patient comparison data prior to implementing a new lot of a reagent or when troubleshooting unacceptable quality control. Therefore, selecting appropriate quality goals is essential.

How Should You Select Quality Goals?

Setting quality goals in laboratory medicine has been a topic of discussion over the last several decades. In 1999, a conference held in Stockholm, Sweden, reached a consensus agreement that recommended a hierarchy of five models for quality goals: 1) clinical outcomes; 2a) biological variation, 2b) clinicians’ opinions; 3) professional recommendations; 4) regulatory and proficiency testing (PT)/external quality assessment (EQA) programs; and 5) state-of-the-art (1).

Because of this hierarchy’s complexity, attendees at a 2014 Milan Strategic Conference reduced these five models to three (2). They kept the first model the same, changed the second model to include biological variations only, and moved other approaches into the state-of-the-art model.

Though not based on a hierarchy this time, the 2014 conference recommended selecting TEa based on clinical outcomes or the biological variation of the analyte first, followed by a state-of-the-art approach if the first two models were unavailable. The 2014 revised consensus recognized that some models are more appropriate for certain analytes than for others, and it emphasized the importance of having high quality studies or data behind each recommendation. As a result, laboratories should evaluate TEa recommendations from different resources thoughtfully and understand their limitations.

Model 1: TEa Based on the Effect of Analytical Performance on Clinical Outcomes

Ideally, laboratories would set quality goals based on evidence of proven clinical outcomes, but have performed few studies on the topic. For example: From comparing HbA1C results between patients with poor glycemic control with good glycemic control in the Diabetes Control and Complications Trial (DCCT), it was estimated that an HbA1C assay could have TEa of ±9.4% (3). Nowadays, this error limit would not be recommended. For grading HbA1C PT, the College of American Pathologists uses an acceptance limit of ±6%, whereas allowing an error limit larger than that would risk unsatisfactory performance.

Model 2: TEa Based on Components of Biological Variation of the Analyte

Laboratories establish biological variation-based quality goals by evaluating the inherent biological variation of the analyte for three analytical performance specifications: minimum, desirable, and optimum. Many laboratories still use TEa based on the “desirable” specification. One reason this model gained wide acceptance is that by having these three specifications available, laboratories can fine-tune TEa depending on what is possible and suitable for the laboratory. Another advantage of using this model is access to the continuously updated and easily accessible database on biological variations managed by European Federation of Clinical Chemistry (EFLM) (4). The EFLM conducts a meta-analysis of papers on the components of biological variations from which the TEa is derived.

When using the biological variation model, laboratories must be mindful that there are instances in which TEa using the “desirable” specification for certain analytes are wider than regulatory limits. In that case, laboratories should evaluate error goals based on the more stringent, optimum specification if possible.

Model 3: TEa Based on State-of-the-Art of the Analyte

Milan’s state-of-the-art model includes quality goals set by regulatory agency, PT/EQA program organizers, professional recommendations, and those found in the literature and package inserts.

Goals set by regulatory bodies (CLIA’88 in the United States) and PT/EQA organizers are easily available and understood. Evaluation criteria are denoted as ±%, ±numerical value, or in terms of standard deviation (SD) multipliers. To generate TEa based on the SD multipliers, our laboratory evaluates coefficient of variation (CV) from the previous 6−10 PT events, calculates median CV of the desired analyte, and multiples the %CV x3 for a 3SD limit.

Even though CLIA limits are easy to use, they were established in the late 1980s from the observed variation in PT events and generally have not been changed since. The major disadvantage of CLIA goals is that they reflect what was achievable, yet not desirable. Therefore, in 2019, more stringent limits were proposed to reflect recent improvements in technology (5). These limits have not been adopted yet but offer another resource in helping laboratories select their TEa.

TEa set by professional recommendations have the advantage of being extensively evaluated and based on experimental data. However, the drawback is that those recommendations are only available for a subset of analytes. TEa reported in the literature and package inserts are easily accessible but the data could be skewed to show the best possible performance of the assay and not what is achievable in practice.

In conclusion, selecting TEa is a key process of laboratory medicine. Without such limits, there is no way for us to determine if the quality of patient results is aligned with our standards and performance expectations. As shown above, there are several resources and guidance documents available for selecting TEa. These sources differ in the magnitude of allowable error limit for each analyte. Therefore, laboratories should select TEa objectively, yet appropriately, to match their analytical system.

Kornelia Galior, PhD, DABCC, is assistant professor, director of clinical chemistry and point-of-care testing at University of Wisconsin-Madison. +EMAIL: [email protected]

Sanaa Al-Nattah, MD, is a pathology resident in the department of pathology and laboratory medicine at University of Wisconsin-Madison. +EMAIL: [email protected]


  1. Kallner A, et al. The Stockholm Consensus Conference on quality specifications in laboratory medicine, 25–26 April 1999. Scand J Clin Lab Invest 1999;59:475–6.
  2. Sandberg S, et al. Defining analytical performance specifications: Consensus Statement from the 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine. Clin Chem Lab Med 2015; 53:833-5.

  3. Panteghini M, et al. Strategies to define performance specifications in laboratory medicine: 3 years on from the Milan Strategic Conference. Clin Chem Lab Med 2017;55(12):1849-1856

  4. European Federation of Clinical Chemistry and Laboratory Medicine. EFLM Biological Variation Database. https://biologicalvariation.eu/ (Accessed November 2, 2021).

  5. Westgard QC. New CLIA Proposed Rules for Acceptance Limits for Proficiency Testing. https://www.westgard.com/2019-clia-changes.htm (Accessed November 2, 2021).