Introduction

In the ever-evolving field of laboratory medicine, clinicians and laboratorians rely heavily on tools to interpret changes in serial test results. One such critical tool is the Reference Change Value (RCV), which helps distinguish between clinically significant changes and random variations (within-subject biologicl variation). However, traditional methods for calculating RCVs often fall short in real-world settings, where variability extends beyond the confines of controlled research environments. A recent study published in Clinical Chemistry by Roys et. al, introduces a groundbreaking methodology that addresses these limitations by leveraging routine patient data.

The Problem with Traditional RCVs

Conventional RCVs are calculated using within-subject biological variation (CVI) and analytical variation (CVA). While these formulas have been foundational, they exclude key elements such as preanalytical variability, unstandardized sampling frequencies, and the complexity of clinical environments. This disconnect often renders traditional RCVs less practical and misaligned with clinicians’ experiences, limiting their use in routine practice.

A Novel Approach: Leveraging Routine Patient Data

The new study proposes a methodology that uses real-world data extracted from Laboratory Information Systems (LIS) to calculate RCVs. By employing the refineR algorithm, this approach filters pathological data, applies robust statistical transformations, and estimates RCVs based on actual clinical variability. Unlike traditional methods, this model accounts for preanalytical factors and adapts to the specific population and workflow of each laboratory.

How It Works

The refineR algorithm operates in several steps:

• Serial patient results are extracted from the LIS.
• Pathological data are filtered to focus on non-pathological variations.
• The remaining data are subjected to a Box-Cox transformation to normalize skewed distributions.
• Back-transformed percentiles are used to calculate clinically relevant RCVs
• This process allows laboratories to derive RCVs that reflect local practice, offering a more tailored and accurate tool for interpreting serial results.

Key Findings

The study’s results highlight the effectiveness of this novel approach:

• Wider RCV Ranges: Compared to traditional methods, LIS-derived RCVs showed slightly wider intervals, incorporating real-world variability.

Example RCVs for common analytes:

Albumin: -10% to +11%.

Creatinine: -16% to +14%.

Cortisol: -54% to +51%.

11-Deoxycortisol: -86% to +123%.

• Monte Carlo Validation: Simulations confirmed that this approach maintains robust RCV estimates for Gaussian and skewed data distributions. The model’s accuracy holds even with significant variations, provided there are at least 5,000 valid ratios and pathological noise remains under 30%.
• Increased Clinical Utility: The refined RCVs address both preanalytical variability and real-world operational inconsistencies, making them more practical for clinical decision-making.

Technical Insights

• The methodology’s robustness stems from several advanced statistical measures:
• Box-Cox Transformation: Applied to normalize skewed data distributions and ensure Gaussian-like behavior.
• Percentile Back-Transformation: Allows for accurate derivation of RCVs that reflect real-world ranges
• Outlier Filtering: refineR excludes outliers and pathological data efficiently, minimizing noise in the final RCV calculation.
• Scalability: Effective with routine biomarkers that have sufficient data points.

Clinical Implications

• This patient-centric approach has significant implications for laboratory medicine, pathology, and diagnostics professionals:
• Improved Relevance: By reflecting the variability seen in routine clinical practice, LIS-derived RCVs align better with clinician expectations and experiences.
• Enhanced Decision-Making: Clinicians can rely on these RCVs for more accurate interpretation of serial results, improving patient care.
• Cost-Effectiveness: Using existing LIS data eliminates the need for extensive, controlled research studies.

Strengths and Limitations

Strengths:

• Validated Approach: Robust validation through Monte Carlo simulations.
• Adaptability: Applicability to both Gaussian and heavily skewed data distributions.
• Real-World Utility: Incorporates preanalytical variability into the model.

Limitations:

• Data Dependency: Requires a minimum of 5,000 ratios for reliable calculations.
• Noise Sensitivity: Pathological noise above 30% could compromise accuracy.
• Analyte-Specific Challenges: Some highly skewed biomarkers, such as 11-deoxycortisol, may require additional filtering.

Future Directions

The study opens the door for further exploration. Future research could focus on refining the methodology to accommodate smaller datasets and heavily skewed distributions. Additionally, incorporating regression toward the population mean could enhance the clinical applicability of these findings.

Conclusion

This study marks a significant step forward in the application of RCVs in routine clinical practice. By integrating real-world variability, it bridges the gap between research-based formulas and the dynamic nature of laboratory medicine. Laboratories adopting this approach can provide clinicians with tools that are not only scientifically robust but also clinically meaningful. As personalized medicine continues to rise, methodologies like these will undoubtedly play a pivotal role in shaping the future of diagnostics.

Reference

Røys EÅ, Viste K, Kellmann R, Guldhaug NA, Alaour B, Sylte MS, Torsvik J, Strand H, Marber M, Omland T, Theodorsson E, Jones GRD, Aakre KM. Estimating Reference Change Values Using Routine Patient Data: A Novel Pathology Database Approach. Clin Chem. 2024;00:1–12. https://doi.org/10.1093/clinchem/hvae166