Understanding Reference Ranges: How Are They Established and Why Do They Matter?
When you run testing for a patient, you get results or numbers. How do you interpret those numbers? You use reference ranges to help define normal versus abnormal results. This is why reference ranges are so important. The way reference ranges are established is even more important. Have you ever wondered where these numbers actually come from? Why might the same test have different reference ranges at different laboratories? Does a result outside the reference range automatically mean something is wrong with the patient? Reference ranges are more than just numbers printed beside a patient’s results. They are an important part of laboratory testing, helping healthcare providers interpret results and make informed decisions about patient care. What Are Reference Ranges? A reference range, also known as a reference interval, is a set of values used to help determine whether a patient’s laboratory result falls within an expected range. Reference intervals are typically established by collecting and analyzing samples from a group of individuals considered to be healthy. The results are then evaluated statistically to determine the range of values expected for that population. Most conventional reference intervals represent the central 95% of values from a selected reference population (ASCLS, 2019; Centra Diagnostics, n.d.). What does that actually mean? It means that approximately 95% of healthy individuals in that population are expected to have results within the reference interval. The remaining 5% may have results outside the interval, even though they are healthy. Reference intervals only provide a starting point for interpreting results. They should always be considered alongside the patient’s clinical history, symptoms, and other laboratory findings (Centra Diagnostics, n.d.). How Are Reference Ranges Established? Reference intervals are not randomly selected. Establishing them involves several steps, including selecting an appropriate reference population, collecting and analyzing specimens, and using statistical methods to determine the expected distribution of results (Higgins, 2009). Step 1: Selecting a Reference Population The first step is selecting individuals who represent the population for which the reference interval will be used. These individuals are generally considered healthy based on predetermined inclusion and exclusion criteria (NIH/PMC, n.d.). Age, sex, pregnancy status, and other physiological characteristics may influence the results. Depending on the analyte, separate reference intervals may be necessary for different patient populations. For example, think about hemoglobin. The expected values for a newborn differ from those for an adult. Using the same reference interval for both populations could lead to incorrect interpretation (Centra Diagnostics, n.d.). This is why selecting the appropriate reference population is such an important part of the process. Step 2: Collecting and Testing Samples Once the reference population has been selected, specimens are collected and analyzed. For the traditional nonparametric method, guidelines generally recommend collecting samples from at least 120 reference individuals to establish a reference interval. This sample size allows laboratories to estimate the central 95% of results and calculate confidence limits around the upper and lower reference limits (Higgins, 2009; NIH/PMC, n.d.). Preanalytical variables must also be controlled. These include: For example, cortisol concentrations naturally fluctuate throughout the day. If specimens are collected at inconsistent times, those differences could influence the distribution of results. Standardizing collection and testing conditions helps ensure that the resulting reference interval reflects the intended population rather than avoidable variations in specimen collection or handling. Step 3: Using Statistics to Establish the Reference Interval After the specimens have been analyzed, the laboratory uses statistical methods to determine the reference interval. One commonly recommended approach is the nonparametric method. With this approach, the laboratory arranges the results from lowest to highest and identifies the values corresponding to the 2.5th and 97.5th percentiles. These two values become the lower and upper reference limits (Higgins, 2009; NIH/PMC, n.d.). Imagine you have laboratory results from 120 healthy individuals. After arranging the results from lowest to highest, you identify the values that represent the central 95% of the population. Approximately 2.5% of the values fall below the lower limit, and another 2.5% fall above the upper limit. The values between those limits form the reference interval. It is important to remember that this statistical process identifies what is typical for the selected population, not necessarily what is ideal for every patient’s health. Establishing vs. Verifying Reference Intervals: What’s the Difference? Not every laboratory establishes its own reference intervals. Establishing reference intervals requires considerable time, resources, and access to an appropriate reference population. Many laboratories adopt reference intervals provided by manufacturers or established through published studies. This does not mean laboratories should automatically accept those intervals without proper evaluation. This is where reference interval verification comes in. Establishing a reference interval means generating the data needed to determine the limits. Verifying a reference interval means evaluating whether an existing interval is appropriate for the laboratory’s method and patient population. According to the CLSI approach discussed by NIH, a laboratory can use a verification procedure involving 20 appropriately selected reference individuals. If no more than two of the 20 results fall outside the proposed reference interval, the interval may be considered acceptable under that procedure. If three or more results fall outside the interval, further investigation and additional data may be necessary. This is a more practical way to evaluate existing reference intervals without conducting a new study. Why Do Reference Ranges Differ Between Laboratories? Have you ever noticed that two laboratories may report slightly different reference intervals for the same test? This does not mean one laboratory is wrong. Reference intervals can vary for several reasons. 1. Testing methods and instrumentation Different analyzers, reagents, and analytical methods may produce slightly different results. Therefore, a reference interval established using one method may not always be appropriate for another. 2. Patient populations The population used to establish a reference interval matters. Age, sex, pregnancy, and other physiological characteristics may influence expected laboratory values. 3. Specimen type Serum, plasma, and whole blood are not always interchangeable. Depending on the analyte, the specimen type can influence the measured result and the reference interval. 4. Preanalytical variables Fasting status,








