If we didn’t use reference intervals (RI), how would we know whether a person is “normal” or not? Or more accurately, how would we know whether a lab test result indicated health or disease? Reference intervals have been around as long as lab tests and they help clinicians diagnose and monitor a patient’s disease state. .
Most RI are developed using a specific patient population and should be used only with that population. However, some RIs are “health-based,” such as cholesterol and vitamin D. Both these analytes have RI that indicate what amount of the analyte should be present in a healthy individual, not how much is present in your specific population of patients. In general, health-based RI can be utilized in all populations, as long as the analyte assays are commutable. Thus these type of RI are often more useful than population-based intervals.
But should we be using reference intervals at all? One problem with population-based RI is that any given individual’s values may span a range that covers only part of the population RI due to biological variability. For example, an individual’s creatinine may be 0.6 – 0.9 mg/dL regularly. Since the RI for creatinine for his population is 0.4 – 1.4 mg/dL, a value of 1.2 mg/dL would not be flagged as be abnormal. However, 1.2 mg/dL may very well be an abnormal result for this individual We need to consider using reference change values (RCV) in addition to RI.
Reference change values are calculated values that are used to assess the significance of the difference between two measurements. Essentially, a RCV is the difference that must be exceeded between two sequential results for a change to be a significant change. The calculation requires knowledge of the imprecision of the analyte assay (CVA) and the biological variation (CVI) of the analyte. The formula for calculating RCV is: RCV=21/2 · Z · (CVA2 + CVI2)1/2 , where Z is the number of standard deviations for a given probability. Luckily, labs know the imprecision of their assays and there are tables available for biological variation.
It’s very likely that neither RI nor RCV by itself is adequate for interpreting analyte results. Using both may be a better alternative, especially using RCV for monitoring disease progression or therapeutic efficacy. Flagging sequential values that exceed the RCV—and reporting this change—should be considered.