Interpretation of laboratory results: the Reference Intervals, a necessary evil?

Personalized reference intervals: from theory to practice

Using laboratory test results for diagnosis and monitoring requires a reliable reference to which the results can be compared. Currently, most reference data is derived from the population, and patients in this context are considered members of a population group rather than individuals. However, such reference data has limitations when used as the reference for an individual. A patient's test results preferably should be compared with their own, individualized reference intervals (RI), i.e. a personalized RI (prRI).The prRI is based on the homeostatic model and can be calculated using an individual's previous test results obtained in a steady-state situation and estimates of analytical (CV_A) and biological variation (BV). BV used to calculate the prRI can be obtained from the population (within-subject biological variation, CV_I) or an individual's own data (within-person biological variation, CV_P). Statistically, the prediction interval provides a useful tool to calculate the interval (i.e. prRI) for future observation based on previous measurements. With the development of information technology, the data of millions of patients is stored and processed in medical laboratories, allowing the implementation of personalized laboratory medicine. PrRI for each individual should be made available as part of the laboratory information system and should be continually updated as new test results become available.In this review, we summarize the limitations of population-based RI for the diagnosis and monitoring of disease, provide an outline of the prRI concept and different approaches to its determination, including statistical considerations for deriving prRI, and discuss aspects which must be further investigated prior to implementation of prRI in clinical practice.

Reasons for ordering laboratory tests and relationship with frequency of abnormal results

OBJECTIVE

Laboratory tests are ordered on a daily basis, even though disease probability is often very low. Abnormal results, especially mildly abnormal results, can be difficult to interpret in these circumstances. Further insights into the occurrence of abnormalities can help improve rational test ordering and test interpretation. The objective was therefore to examine the frequency of mildly and markedly abnormal results and their relationship with physicians' reasons for ordering tests.

DESIGN

Prospective study. Participants. A total of 87 primary care physicians in the Netherlands collected data on 1775 patients.

MAIN OUTCOME MEASURES

The physicians recorded the reason for ordering the tests, the most probable diagnosis and the pretest probability. The laboratories' reference values and specified "action limits" were used to assess the number of abnormal results and markedly abnormal results, respectively.

RESULTS

Laboratory results were received for 1621 patients and 15,603 tests were reported (mean 9.6). The proportion of abnormal test results increased with increasing pretest probability (from 13.9% to 34.7%) and was 13.4% for tests ordered to reassure the patient and 13.3% for psychosocial diagnoses. The proportion of patients with at least one abnormal test result was high: 53.1% for tests ordered to reassure and 57.7% in patients with low pretest probability. Corresponding values for a marked abnormality were 11.1% and 12.4%, respectively.

CONCLUSION

Abnormal laboratory test results were frequent, even when pretest probability was low. Physicians should therefore carefully consider when tests are necessary. Future research could explore physicians' interpretation of test results and its impact on diagnosis and management.