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Regression Using 20 Samples to Harmonize a Point-of-Care Analyzer With a Commercial Laboratory Analyzer for Feline

Randolph M Baral1, Bente Flatland2, Susan M Jaensch3

  • 1University of Sydney/Paddington Cat Hospital, Sydney, Australia.

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|April 16, 2026
PubMed
Summary
This summary is machine-generated.

Regression analysis successfully harmonized point-of-care (POC) analyzer results with commercial laboratory (CL) analyzers for most feline plasma biochemistry analytes. This method allows for better comparison of patient results across different diagnostic platforms.

Keywords:
Bland–Altman difference plotsDeming regressionPassing–Bablok regressionbiological variationharmonizationlinear regressionordinary least squares regressionpoint of care analyzerquality goals

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Area of Science:

  • Veterinary Diagnostics
  • Clinical Chemistry
  • Analytical Chemistry

Background:

  • Harmonization of diagnostic test results is crucial for comparing patient data across different analyzers and measurement techniques.
  • Point-of-care (POC) analyzers often lack end-user calibration, necessitating alternative harmonization methods like regression analysis.
  • Previous studies have demonstrated the utility of regression for harmonizing POC results with commercial laboratory (CL) analyzers using paired samples.

Purpose of the Study:

  • To derive regression equations using 20 paired feline plasma samples to harmonize POC analyzer results with CL analyzer results.
  • To assess the quality of harmonized POC results against established quality goals.

Main Methods:

  • Systematic sampling was employed to select 20 paired feline plasma samples for calculating regression parameters.
  • Regression equations were applied to the remaining dataset to adjust POC results, harmonizing them to CL analyzer standards.
  • POC results were evaluated for bias and the proportion meeting quality goals before and after harmonization.

Main Results:

  • Regression analysis using 20 paired samples effectively harmonized POC analyzer results to CL analyzer results for 16 feline plasma biochemistry measurands.
  • The regression approach demonstrated significant improvement in the comparability of POC results to CL standards.

Conclusions:

  • Regression analysis is a viable method for harmonizing most feline plasma biochemistry analytes measured by POC analyzers to CL analyzer standards.
  • Limitations such as insufficient concentration ranges, non-linear relationships, and lack of clinical performance goals were identified for specific analytes (AST, lipase, total bilirubin, GGT, globulins).
  • Further research with samples covering clinically relevant value ranges and studies on other species and POC platforms are recommended.