A Parametric Empirical Bayes Approach to Personalized Reference Intervals and Reference Change Values

Abstract

Background Population-wide reference intervals (RIpop) are commonly used in laboratory medicine but may not reflect an individual’s tightly regulated homeostatic interval. Personalized reference intervals (RIper) could enhance diagnostic precision by accounting for individual variability. A parametric empirical Bayes (PEB) framework stabilizes individual estimates using population parameters, enabling reliable RIper even from a limited number of individual results. Methods We applied the PEB framework to estimate RIper for 9 biomarkers: albumin, creatinine, phosphate, cortisone, cortisol, testosterone, androstenedione, 17-hydroxyprogesterone, and 11-deoxycortisol. The PEB parameters tested were derived from both routine Laboratory Information System (LIS) data and a local biological variation (BV) study. Using serial samples from healthy adults, we assessed the proportion of results flagged with a 95% prediction interval and compared RIper to conventional RIpop and reference change values (RCVs). Results LIS parameters were based on data from 1986 to 185 488 patients. PEB-based RIper were consistently narrower than RIpop while maintaining or reducing the proportion of flagged results. For example, albumin flagging decreased from 4.7% (RIpop) to 0.3% (RIper), phosphate from 5.4% to 3.7%, and cortisone from 7.1% to 3.9%. Conversely, 17-hydroxyprogesterone increased from 0.0% to 5.5% but remained close to the expected 5%. PEB thresholds were narrower than standard RCV estimates by correcting for regression toward the mean without increasing flagged results. Conclusions The PEB framework effectively provides personalized cutoffs for laboratory tests even when few individual patient results are available. PEB parameters can be established using LIS or BV data, offering a feasible and cost-effective implementation pathway.