The impact of pathological fluctuations versus biological variation on the interpretation of laboratory values

This is the authors' abstract. We don't add key points for this paper.

The current criterion used to determine whether the reference interval (RI) can be used for interpretation is based on the index of individuality (II), estimated using biological variation (BV). We hypothesized that pathological variation (PV), the shift between healthy and unhealthy states, varies across biomarkers and may be considered for interpretation with BV. We explored how jointly considering PV and BV impacts the clinical interpretation (diagnostic sensitivity and specificity) of RIs. We propose the index of pathology (IP), a ratio of within- to between-subject coefficients of variation that jointly considers PV and BV. Using a large EHR database from a tertiary care center, we obtained IP estimates for 19 laboratory tests. As a means of comparison, the II was obtained from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) BV database. PV impact was analyzed using the absolute difference between IP and II (Δ IP-II ). 798,800 observations from 17,082 adult patients were analyzed. For most biomarkers, the IP (mean=1.99, range=0.55-8.03) differed from the II (mean=0.54, range=0.27-0.86). Lowest IPs were for creatinine (IP=0.55, Δ IP-II =0.28) and bilirubin (IP=1.05, Δ IP-II =0.24). Highest IPs were for aspartate transaminase (IP=4.56, Δ IP-II =4.13) and creatine kinase (IP=8.03, Δ IP-II =7.60). Hormones and proteins exhibited high PV impact (Δ IP-II >1.0). Differences between variational estimates that only account for healthy states (II-BV) and those that consider healthy and unhealthy states (IP-BV+PV) vary widely among biomarkers, highlighting the differential impact of PV on their interpretation. For biomarkers where IP is high, the RI may be useful to identify unhealthy individuals. • Variation beyond the healthy state is different across biomarkers. • Considering the variation beyond the healthy state alters clinical interpretation. • Hormones, proteins exhibit highest impact from variation in the unhealthy state.