How do Common (Multicenter) Reference Intervals operate?

• A. Each laboratory derives its own RI independently
• B. Laboratories share patient data but compute separate RIs
• C. A central database pools results without standardization
• D. Multiple laboratories serving similar populations contribute samples to generate a shared RI, enabling larger sample sizes and narrower CI

Answer: (D)

Common multicenter reference intervals are built by pooling samples from multiple laboratories that serve similar populations to create a shared reference interval for a given analyte. This approach increases the total number of healthy reference samples, which improves the precision of the percentile estimates used to define the interval and often yields a narrower confidence interval around the limits. It also helps smooth out laboratory-specific biases by relying on a broader, more representative dataset and, when possible, harmonizes preanalytical and analytical methods so the resulting interval is applicable across centers.

If each lab derives its own reference interval, the result can be less reliable because the reference sample size is smaller and greater variability in instruments, reagents, and procedures can skew the interval. Simply sharing data while keeping separate reference intervals still tends toward fragmentation rather than a universal standard. Conversely, pooling results in a central database without any standardization risks introducing bias if differences in methods or populations across labs aren’t accounted for, undermining the validity of the shared interval.

So, pooling samples from multiple laboratories serving similar populations to generate a single, shared reference interval provides the largest, most robust basis for defining normal ranges applicable across centers.