Defining ‘normal’ in an era of precision medicine is challenging

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The definition of “normal” values for common laboratory tests often governs the diagnosis, treatment, and overall management of tested individuals. Some test results may depend on demographic traits of the tested population including age, race, and sex. Ideally, laboratory test results should be interpreted in reference to a population of “similar” “healthy” individuals. In many settings, however, it is unclear exactly who these individuals are. How much population stratification and what criteria for healthy individuals are optimal?

However, with the proliferation of large data sets emblematic of precision medicine, it is becoming feasible to study stratified variation and clinical outcomes at scale. Sample size limitations are no longer a challenge. However, the task of defining a “normal” population becomes even more challenging. Who should define normality and using which criteria? When should standardized efforts be used across populations and instrumentation? How can multiplicity across myriad population strata be overcome as the normal population becomes more precise and personalized?

It is essential to answer these questions for widely used clinical laboratory tests such as complete blood cell count and blood chemistries before delving into more rare tests. Such tests are a routine entry point for invasive and expensive follow-up tests and procedures, yet remain poorly characterized across strata.

Read full, original post: In the Era of Precision Medicine and Big Data, Who Is Normal?

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