Harmonization of ANA testing challenge: quantification strategy to accurately predict end-point titers avoiding serial dilution.
Immunol Res
; 72(1): 96-102, 2024 Feb.
Article
em En
| MEDLINE
| ID: mdl-37792145
ABSTRACT
Despite the advantages of automated systems for antinuclear antibody (ANA) analysis, the prediction of end-point titers avoiding serial dilutions is still in progress. The aims of this study were to set a conversion table providing discriminant ranges of fluorescence signal intensity values (FI) corresponding to the end-point titers and validate this tool in a real-life laboratory setting. Eight hundred ninety-four serum samples were analyzed for ANA using Image Navigator System. In order to classify FI into non-overlapping groups corresponding to conventional end-point titers, statistical discriminant analysis was used. Validation study was performed calculating agreement and error rates between visual readings and conversion table of 1119 routine ANA positive samples. Setting of FI ranges corresponding to the end-point titers for different staining patterns was computed. For samples showing single pattern, the overall agreement between visual readings and conversion table was 98.4% for all titers ranging from 1160 to 12560, of which 68.0% had the same titer and 30.4% were within ± one titer difference. Concordance rates according to ANA patterns were as follows (1) nuclear 98.4%, of which 67.0% had the same titer and 31.4% ± one titer; (2) cytoplasmic 100%, of which 72.7% had the same titer and 27.3% than ± one titer; (3) mitotic 66.6%, of which 33.3% had more ± one titer. Our study developed a quantification method for autoantibodies titers assessment based on just one single sample dilution instead of traditional serial dilution approach, providing significant advantages in routine laboratory in terms of reduction in hand-on time and harmonization of results.
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Base de dados:
MEDLINE
Assunto principal:
Anticorpos Antinucleares
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article