Guide
Are your analysers interchangeable? Comparability of results across instruments and sites
A laboratory that runs the same test on more than one analyser, or at more than one site, has to show that a patient’s result does not depend on which one was used. This guide sets out what the standards ask, and how to report comparability where it matters: at the decision limits.
What ISO 15189:2022 asks
ISO 15189:2022 sets the requirements for quality and competence in medical laboratories. Its clause on the comparability of examination results covers examinations done on different methods or equipment, or at different sites. As one published EQA study summarises the clause, the laboratory needs a process to assess that comparability, and where differences are found, their impact on biological reference intervals and clinical decision limits should be evaluated and acted on, with users told of any clinically significant difference [Marrington et al., 2026].
Two points are easy to miss. The scope reaches beyond analysers of the same model: a point-of-care device and the main laboratory analyser measuring the same analyte are both in it. And the test of success is clinical: whether a difference changes what a result means at a reference interval or decision limit.
The EP31 approach
CLSI EP31 gives recommendations for verifying the comparability of quantitative results for individual patients between two or more instruments within one health care system, whether the instruments differ in method, in model, or are separate instruments of one type [CLSI EP31, 2025]. Its protocol compares patient samples, chosen at the concentrations of interest, across all the instruments, using a range test for up to ten instruments and modified strategies above that.
It works in routine use. A laboratory that applied it to two dozen chemistry tests across several analysers found the guideline useful for periodic verification, but noted that it may need acceptance criteria adjusted to the analytical performance of the technology available, and a number of measurements matched to the laboratory’s resources [Guiñón et al., 2024].
Patient samples, not only control material
Control and EQA material is designed to be stable, not to behave exactly like a patient sample. Two methods can agree on control material and still disagree on patient blood, and the difference can depend on the sample type as much as the instrument. EP31 is framed around patient results for that reason, and a periodic comparison with real samples near the limits the laboratory reports against answers this question better than a control chart.
Setting matters as much as sample. The same device used by a different operator, on capillary rather than venous blood, away from the laboratory, is in practice a different measuring system. The worked example below shows how much that changes.
Comparability at the decision limits
A comparability check usually ends in one number per analyte: a mean difference, or a maximum difference against an allowable limit. For the clinical question the standard asks, three further numbers are more useful, each one per decision limit.
- Bias at the limit. Read from the comparison line at the limit itself, not averaged over the range. A proportional difference can be small at one limit and large at another.
- Results that change category. Of the paired results, how many fall on one side of the limit on one instrument and on the other side on the second.
- The repeat-testing baseline. How many of those changes one instrument measuring the same samples twice would produce anyway, from its imprecision. The excess estimates the part due to the instruments differing; at a default imprecision it describes the counts and is not a test.
The third number needs the imprecision of each instrument: from replicate measurements where they exist, otherwise a stated default. The guide to bias at clinical decision limits works through the method in detail.
One analyser, three meters, two settings
Public data
A public study compared three point-of-care haemoglobin meters with one laboratory haematology analyser as the reference, first in a central laboratory and then in a community clinic, where the meters were used on finger-prick blood and the reference on a venous sample drawn at the same visit [Jaggernath et al., 2016]. Its data are deposited under CC BY 4.0 [Gelderblom, 2016]. The study also repeat-tested a subset of samples, so imprecision is measured. We ran all six comparisons with limits at 8, 12 and 13 g/dL; the study itself used 12 and 13 g/dL as its anaemia thresholds for women and men.
Meter A, central laboratory
Crossed between the two systems: 14
Expected from repeat testing alone: about 2
Meter B, central laboratory
Crossed between the two systems: 16
Expected from repeat testing alone: about 32
Meter C, central laboratory
Crossed between the two systems: 36
Expected from repeat testing alone: about 12
Meter A, community clinic
Crossed between the two systems: 36
Expected from repeat testing alone: about 7
Meter B, community clinic
Crossed between the two systems: 37
Expected from repeat testing alone: about 62
Meter C, community clinic
Crossed between the two systems: 65
Expected from repeat testing alone: about 25
Fig. 1. Against one laboratory analyser, three meters moved different numbers of results across the haemoglobin limits in the central laboratory and community clinic; for Meter B, the expected count from repeat testing is larger than the number the comparison moved. That expected count uses the meter’s own measured imprecision, the larger of the two; at the laboratory analyser’s own precision it would be smaller.
Source Data: Huub Gelderblom (2016). Performance characteristics of three point of care hemoglobin meters in Durban-2.xlsx. figshare. doi:10.6084/m9.figshare.3119449 (opens in a new tab). Licence: CC BY 4.0 (opens in a new tab). Supplement to Jaggernath M, Naicker R, Madurai S, Brockman MA, Ndung'u T, Gelderblom HC (2016). PLoS One. doi:10.1371/journal.pone.0152184 (opens in a new tab); PMC4821624 (opens in a new tab). Re-analysed by NakedSignal. Supplied: precision file (patient-sample replicates (5 per sample)), the larger of the two systems.
Show the numbers
| Comparison | Pairs | Slope | Mean difference, as % of old | Changed category | Expected from repeat testing | Re-test band |
|---|---|---|---|---|---|---|
| Meter A, central laboratory | 60 | 1.000 (0.946 to 1.022) | +4.7% | 14 | 1.6 | Indicative |
| Meter B, central laboratory | 56 | 1.130 (1.000 to 1.325) | −12.5% | 16 | 32.4 | Indicative |
| Meter C, central laboratory | 59 | 0.800 (0.723 to 0.883) | −24.1% | 36 | 12.3 | Indicative |
| Meter A, community clinic | 100 | 1.047 (0.953 to 1.167) | −1.3% | 36 | 6.5 | Full |
| Meter B, community clinic | 100 | 1.031 (0.923 to 1.148) | −4.4% | 37 | 62.3 | Full |
| Meter C, community clinic | 100 | 0.725 (0.617 to 0.868) | −23.6% | 65 | 25.1 | Full |
What the figure shows
Comparability belongs to a system in a setting, not to a device. A meter need not behave the same way in the laboratory and in the clinic: compare each meter’s slope and mean difference across the two settings under “Show the numbers”. The study’s authors reached the same conclusion from their own analysis: good performance of a point-of-care test in a central laboratory does not guarantee good performance in a community based clinic setting [Jaggernath et al., 2016]. A laboratory network verifying comparability should test each system where and how it is used.
Some disagreement is noise, not bias. For Meter B, the number of results expected to change category from repeat testing alone is larger than the number that did. The expected count uses the larger of the two systems’ measured imprecision, so near the limits that meter’s own scatter masks any difference between the systems. Fix the imprecision first; a correction factor alone cannot.
Small studies are graded, not hidden. The comparisons from the central laboratory phase have fewer pairs than a full re-test band needs, so their bands are graded indicative. A laboratory’s own periodic comparison is often this size; the count against repeat testing still informs, and the band is wider.
A second public comparison, from a clinical reference laboratory in Cameroon, sets a point-of-care analyser against a laboratory haematology analyser on 128 pairs [Sagnia et al., 2024]; it is in the change explorer with the six above.
Related topics
- Traceability and calibration. Why two methods differ, and how metrological traceability and harmonisation programmes reduce the difference, is its own subject.
- EQA scheme design. How schemes choose materials and assess participants complements the patient-sample comparison described here.
- The standards themselves. ISO 15189:2022 is summarised here from a published EQA study, not from the standard itself, and EP31 from its publisher’s description; the documents are the authority.
Sources
- Marrington R, Anderson M, Roch M, Davies S, Forster R, Williams G, d'Oyen-Fitchett J, MacKenzie F (2026). Use of EQA for assessing variation in post-analytical interpretation of results using a clinical case of ?adrenal insufficiency taking into consideration variation in analytical performance of cortisol. British Journal of Biomedical Science. pmc.ncbi.nlm.nih.gov/articles/PMC13461562/
- Clinical and Laboratory Standards Institute (2025). Verification of Comparability of Patient Results Within One Health Care System. CLSI guideline EP31, second edition. clsi.org/shop/standards/ep31-plus/
- Guiñón L, Illana FJ, Cuevas B, Canyelles M, Martínez-Bru C, García-Osuna Á (2024). Periodic verification of results' comparability between several analyzers: experience in the application of the EP31-A-IR guideline. Clinical Chemistry and Laboratory Medicine. doi.org/10.1515/cclm-2023-0994
- Jaggernath M, Naicker R, Madurai S, Brockman MA, Ndung'u T, Gelderblom HC (2016). PLoS ONE. pmc.ncbi.nlm.nih.gov/articles/PMC4821624/
- Gelderblom H (2016). Performance characteristics of three point of care hemoglobin meters in Durban-2.xlsx. figshare data set, CC BY 4.0. doi.org/10.6084/m9.figshare.3119449
- Sagnia B, Mbakop Ghomsi F, Moudourou S, Gutierez A, Tchadji J, Sosso SM, Ndjolo A, Colizzi V (2024). Accurate and reproducible enumeration of CD4 T cell counts and Hemoglobin levels using a point of care system: Comparison with conventional laboratory based testing systems in a clinical reference laboratory in Cameroon. PLoS ONE. pmc.ncbi.nlm.nih.gov/articles/PMC10954178/