Anti-neutrophil cytoplasmic antibody testing by indirect immunofluorescence: Computer-aided versus conventional microscopic evaluation of routine diagnostic samples from patients with vasculitis or other inflammatory diseases

Effect of antinuclear antibody positivity on antineutrophil cytoplasmic antibody results by indirect immunofluorescence assay

BACKGROUND

The indirect immunofluorescence assay (IIFA) utilizing antineutrophil cytoplasmic antibodies (ANCA) is widely used as a diagnostic test for autoimmune vasculitis. The presence of antinuclear antibodies (ANA) might lead to a misleading interpretation of ANCA. This study aims to explore the impact of the presence of ANA on the interpretation of ANCA.

METHODS

This retrospective research examined samples negative for antiMPO and antiPR3 ANCA by IIFA and explored correlations between the ANA-IIFA results and the ANCA interpretation frequencies. Our analysis involved the use of suitable statistical methods, including Chi-square and kappa statistics.

RESULTS

Up to 75.2% of the ANCA-IIFA-positive samples exhibited a positive p-ANCA pattern when using the ethanol-fixed substrate, with c-ANCA positivity at 24.8%. In the ANA-IIFA-positive samples, ~77.3% displayed p-ANCA patterns on ethanol-fixed substrates. A comparison between the ANA-IIFA titers and the p-ANCA results revealed that p-ANCA positivity was notably more common in samples with higher titers, and this correlation was found to be statistically significant.

CONCLUSION

Positive ANA results by IIFA tests are linked to a higher incidence of p-ANCA interpretation, particularly in cases with higher titer patterns. This insight aids laboratories in establishing effective workflows to investigate potential p-ANCA interference.

Interference of anti-nuclear antibodies on determination of anti-neutrophil cytoplasmic antibodies in patients suspected of vasculitis: a case series

Anti-neutrophil cytoplasmic antibodies (ANCA) are mainly associated with medium and small vessel vasculitis. Two main methodologies currently available for detection of these antibodies are indirect immunofluorescence (IIF) and monospecific proteinase 3 (PR3) and myeloperoxidase (MPO) based immunoassays. However, well-defined guidelines regarding mode of testing for ANCA in laboratories still don't exist, leading to problems in diagnosis and further patient management. Anti-neutrophil cytoplasmic antibodies testing by IIF and enzyme linked immunosorbent assay (ELISA) often pose a significant challenge in diseases other than vasculitis and in overlapping autoimmune conditions. Anti-neutrophil cytoplasmic antibodies reporting by IIF can be challenging in certain circumstances. This case series aims to discuss four cases with probable interference of anti-nuclear antibodies (ANA) during ANCA testing by IIF resulting in ANCA false positivity. All four cases on subsequent reflex testing by line immunoassay (LIA) for PR3, MPO and glomerular basement membrane (GBM) antigens proved otherwise. While analysing for the presence of ANCA by IIF, the possible interference of ANA leading to a false positive ANCA result should be kept in mind and alternative methods of testing like ELISA, extended granulocyte based IIF assays with MPO and PR3 coated beads, etc., should also be advised. Probability of atypical ANCA in diseases other than vasculitis should also be considered in case of ambiguous results.

Artificial Intelligence Applications in Clinical Chemistry

Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promise of AI across all phases of testing including preanalytic, analytic, and postanalytic phases; this includes novel methods for detecting common specimen collection errors, predicting laboratory results and diagnoses, and enhancing autoverification workflows. Although AI applications pose several ethical and operational challenges, these technologies are expected to transform the practice of the clinical chemistry laboratory in the near future.

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review

BACKGROUND

Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine.

CONTENT

In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine.

SUMMARY

AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.