Personalized reference intervals by parametric empirical Bayes: a pragmatic step toward precision laboratory medicine

Traditionally, population-based reference intervals (popRIs) serve as a fundamental framework for interpreting laboratory results, playing a crucial role in assessing patient health and guiding clinical decision-making. However, measurands with high individuality characterized by low within-subject biological variation (BV) (CV_I) and high between-subject BV (CV_G), exhibit subtle intra-individual fluctuations within the popRI, which can obscure clinically significant changes when the variation falls within that range (1-3). For such analytes, personalized reference intervals (prRIs) may enhance diagnostic sensitivity and improve longitudinal patient monitoring. In this context, the Clinical Chemistry study “A Parametric Empirical Bayes Approach to Personalized Reference Intervals and Reference Change Values” provides a compelling and practically implementable framework for defining prRIs using Parametric Empirical Bayes method (PEB-prRIs) (4).

Røys et al. present a comprehensive and statistically robust solution that integrates prRI, popRI and reference change value (RCV). This approach dynamically adapts the interval by incorporating CV_I and CV_G through the shrinkage factor B_n, which weights the contribution of the individual mean relative to the population mean, and the number of prior results, after appropriate transformation of the measurand distribution (4). This method yields intervals that correct for regression toward the mean, providing more informative individualized decision limits than traditional RCVs. These PEB-prRIs are progressively refined for each patient, starting from popRI for the first measurement and becoming increasingly personalized as additional results accumulate.

Although PEB is related to Bayesian inference, it is an empirical Bayes approach in which population parameters are estimated from the data and then treated as fixed, rather than assigning fully probabilistic priors to all parameters (4). This distinction helps explain its pragmatic value and suitability for implementation in routine clinical laboratory settings. Røys et al. apply this framework to nine routine measurands (albumin, creatinine, phosphate, cortisone, cortisol, testosterone, androstenedione, 17-hydroxyprogesterone, and 11-deoxycortisol), assessing its methodological performance using serial samples from healthy adults restricted to the 95% reference limits, with population parameters derived from both Laboratory Information System (LIS) data and a local BV study. In this study, they observed a reduction in flagged results compared with popRIs for most of the analytes assessed; however, as the evaluation was limited to healthy volunteers, the applicability of these findings to clinical patient populations remains to be established.

PrRIs for tumor markers are particularly valuable, given their high individuality and limited diagnostic specificity. The PEB statistical approach has previously been evaluated in algorithms using repeated biomarker measurements (5-7). These algorithms demonstrated higher sensitivity as screening tools for early detection of new oncological findings compared with using popRIs for individual biomarkers. Other prRI calculation methods for tumor markers have also shown negative predictive values above 99% to rule out a new oncological finding, higher than achieved with conventional approaches such as popRI or RCV (8). Beyond tumor markers, the applicability of prRIs has recently been expanded by Røys et al. who successfully applied the PEB framework to a wide range of non-oncological measurands, including albumin, creatinine and phosphate, as well as several adrenal and gonadal steroids, demonstrating that personalized intervals can also refine the interpretation in routine chemistry and endocrine biomarkers with high individuality.

One limitation of PEB-prRIs using CV_I estimates is the assumption that CV_I remains uniform across the population, which is not the case for some analytes, as Røys et al. illustrate for cortisol and 17-hydroxyprogesterone. These markers showed higher proportions of flagged results with PEB-prRI compared with popRI, suggesting that PEB thresholds may not fully capture their true biological variability. This introduces an element of uncertainty that can be mitigated as the number of prior results increases (9).

In the era of personalized medicine and large-scale data utilization, tools such as prRI, and particularly the PEB-prRI approach, have the potential to redefine the usefulness of biomarkers as diagnostic or screening tools in selected clinical contexts. Prospective, patient-outcome-focused studies must demonstrate improvements in diagnostic yield and timeliness of intervention. Equally important is the validation of this approach within each clinical setting prior to implementation, as clinical utility will vary depending on patient characteristics, such as whether the approach is applied to healthy volunteers or clinical patients, as discussed earlier in this manuscript, and institutional protocols, including whether biomarkers are used as screening tools in the general population or to follow-up patients in specific clinical settings. If executed thoughtfully, PEB-prRIs have the potential to redefine the post‑analytical phase, bringing laboratory medicine closer to the individualized care it aspires to deliver.

Acknowledgments

We would like to thank Drs. María Romero and Hugo Casero for their support in the preparation of the manuscript.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Laboratory and Precision Medicine. The article has undergone external peer review.

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Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-2026-1-0006/coif). The authors have no conflicts of interest to declare.

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