Published: June 2026
Medically reviewed by:
Mercè Tena Campos, Scientific Affairs Manager
What they are, why they matter and how they add value to your reports
At Thermo Fisher Scientific, we support evidence-based approaches to result interpretation, including the use of interval-specific likelihood ratios (LRs*).
By combining peer-reviewed evidence with quantitative assay design, Thermo Fisher helps laboratories translate numerical test results into clinically meaningful insights, strengthening the role of autoimmune diagnostics in clinical decision-making.
What is a likelihood ratio?1,2
A likelihood ratio indicates how much more (or less) likely a specific test result is in patients with a disease compared to those without the disease. Values greater than 1 increase the probability of disease, while values below 1 decrease it.
Adapted from: Deane KD, et al. From autoantibody test results to decision making: Incorporating likelihood ratios within medical practice. Autoimmun Rev. 2024;103537.
Unlike sensitivity or specificity, which summarize test performance at a single threshold, likelihood ratios can be defined for specific results or intervals. Results further from the cutoff generally produce larger shifts in disease probability.
Why should a laboratory care?1-4
Many laboratory reports still classify results as negative, equivocal, or positive. However, antibody levels often reflect a continuum of disease probability that is lost in simplified classifications.
1. Communicate clinical meaning — not just numbers
A LR translates assay-specific units (e.g., U/mL, IU/mL, CU, ratio) into clinical context which is clinically actionable information. For example:
“This result is 27 times more likely in a patient with rheumatoid arthritis than in someone without rheumatoid arthritis.”
2. Reduce misinterpretation of weak positive results
Clinicians may overestimate disease probability following a positive result. Likelihood ratios quantify how strongly specific result levels (e.g., antibody titers) shift probability: lower titers often correspond to smaller LRs, whereas higher titer results tend to produce larger LRs. When combined with pre-test probability, LRs support more accurate interpretation of test results and may help reduce misinterpretation.
3. Enable harmonized interpretation
Autoimmune assays vary widely in units, cutoffs, calibration and performance.
Because interval-specific LRs are derived from clinical performance of a given assay, they support a more standardized clinical interpretation. For EliA™ assays, likelihood ratios support consistent interpretation when used with results generated on Phadia™ Laboratory Systems.
4. Provide clinically relevant information
Sensitivity and specificity describe how often a test is positive or negative in patients with and without disease. Clinicians need the reverse: how test results are associated with disease. Likelihood ratios support answering that clinical question.
What is in it for your referring physicians?1,2
Better diagnostic reasoning
Likelihood ratios quantify how much a test result changes the probability of disease.
Starting with pre-test probability, clinicians use LR to re-evaluate disease probability (post-test probability). The clinician can integrate this intuitive information with data from clinical history, clinical examination, and technical investigations.
More precise interpretation of antibody levels
Interval-specific likelihood ratios calculated from, for example, the EliA™ CCP test illustrate how quantitative assay design supports differentiated interpretation. This allows clinicians to move beyond a simple positive/negative classification toward a more nuanced assessment of disease probability.
Clearer communication with patients
Rather than simply stating ‘Your test is positive,’ clinicians can explain: ‘At this level, this result is, for example, 27 or even more than 100 times more likely in patients with the disease than in those without it.’ This provides clearer context and supports shared decision-making.
Example:
- EliA CCP test result of 100 EliA U/mL → LR ~27;
- EliA CCP test result of >340 EliA U/mL → LR ~107
How can laboratories implement LRs?2-4
Published multicenter studies have established interval-specific LRs for many autoantibody tests, allowing laboratories to align assay results with evidence-based LR intervals.
Because LRs for a specific assay reflect clinical performance rather than assay-specific units alone, they facilitate more consistent clinical interpretation and can be implemented once established without requiring additional large outcome studies. It is important to note, however, that interval-specific LRs are assay-specific and are not interchangeable across different methods and manufacturers.
At Thermo Fisher, we support implementation of interval-specific LR reporting as part of a clinically integrated approach to autoimmune diagnostics. Drawing on peer-reviewed evidence, the EliA™ portfolio enables alignment with published LR intervals to reinforce clinical decision-making.
In summary1-4
LRs in autoimmune diagnostics quantify how strongly a test result increases or decreases the probability of the disease. Unlike positive/negative reporting, LRs translate antibody levels into changes in disease probability, enabling more precise interpretation.
- Low-positive results may have limited diagnostic impact (LR ~1–3).
- High antibody levels are almost always associated with strong diagnostic evidence (LR >10).
- LRs are independent of assay-specific units, enabling more consistent interpretation across different assays.
By implementing interval-specific likelihood ratios, laboratories can improve clinical interpretation, reduce misdiagnosis and increase the value of laboratory reports.
Thermo Fisher supports this approach through evidence-based solutions on the EliA™ portfolio.
Ready to learn more?
Please contact your local Thermo Fisher Scientific representative to learn more about introducing likelihood ratios in autoimmune diagnostics.
