Integrating quality assurance in autoimmunity: the changing face of the automated ANA IIF test.

OBJECTIVES: Currently available computer-aided diagnosis (CAD) systems for the detection of anti-nuclear antibodies (ANA) by indirect immunofluorescence (IIF) assay enable a standardized measurement of system-specific fluorescent intensity (FI) measures. We aimed to evaluate an internal quality control (iQC) program that controls the total ANA IIF process in routine practice. METHODS: In addition to the kit iQC materials, supplemental quality indicators were integrated in a total quality assurance (QA) program: patient-derived iQC's samples (negative, 1/160 fine speckled and 1/160 homogeneous), median sample FI per run and percentage of ANA IIF positive samples per run. Analytical rejection criteria were based on the imprecision of the positivity index (PI) measure of the Zenit PRO system (Menarini). Clinical rejection criteria were based on changes in FI that correspond to a change in ANA IIF titer of ≥2. To evaluate the QA program, different artificial errors were introduced during the ANA IIF process. After every run, quality indicators were evaluated and compared to the pre-set target values. RESULTS: Rescanning the ANA IIF slides five times, using an old conjugate and a needle obstruction resulted in analytically and even clinically relevant errors in ANA IIF results. All errors were correctly detected by the different defined quality indicators. Traditional Westgard rules, including analytically (and clinically) defined rejection limits were useful in monitoring quality indicators. CONCLUSIONS: The integration of a total process iQC program in CAD systems, based on the specific FI measurands and performance criteria of the system, adds value to QA.

Clinical autoantibody detection by microarray.

BACKGROUND: AMiDot is a microdot array-based immunoassay that allows simultaneous detection of multiple autoantibodies on a single patient. We evaluated the AMiDot "Systemic Autoimmune Disease" (SAD) panel, which detects antibodies to 17 different antigens. METHODS: AMiDot was performed on 184 samples from blood donors and on 280 randomly selected clinical samples containing antibodies to extractable nuclear antigens or to dsDNA. The results obtained by AMiDot on the clinical samples were compared to results obtained by EliA (Thermo Fisher) for anti-Ro60, anti-La, anti-RNP, anti-Scl-70, anti-CENPB, anti-Sm, and anti-Jo-1 and by Farr assay for anti-dsDNA. Discordant results were further analyzed by immunodot (D-tek). RESULTS: Concordance between AMiDot and EliA was ≥87% and κ agreement ≥0.44. When compared to EliA and immunodot (in case of discordance between AMiDot and EliA), concordance improved to ≥91% and κ agreement to ≥0.77. The sensitivity of AMiDot (compared to EliA and immunodot, in case of discordance between AMiDot and EliA) was ≥93%, except for anti-Ro60 (84%). The concordance and κ agreement of AMiDot with the Farr assay (for dsDNA antibodies) was, respectively, 84% and 0.33. The sensitivity of AMiDot for dsDNA (compared to Farr assay) was 25%. The specificity was ≥97% (in blood donors as well as in clinical samples). The within-run imprecision was 9%-27% and the between-run imprecision 29%-39%. CONCLUSIONS: AMiDot offers an alternative to line immunodot assay. Individual antibody assays might suffer from low sensitivity.

Detection of antinuclear antibodies by automated indirect immunofluorescence analysis.

BACKGROUND: Testing for antinuclear antibodies is useful for the diagnosis of systemic rheumatic diseases. Automated systems for image acquisition and interpretation of indirect immunofluorescence-based tests are increasingly used. The diagnostic performance of such automated approach in untreated patients has not been reported. METHODS: Antinuclear antibodies were measured by automated indirect immunofluorescence using Zenit G. Sight on HEp2 and HEp2000 substrate in 268 consecutive samples submitted to the laboratory for antinuclear antibody testing, and in 231 patients with a systemic rheumatic disease at the time of diagnosis, 143 blood donors, 134 patients with chronic fatigue syndrome, and 133 diseased controls. RESULTS: Image acquisition by G-Sight was of high quality. The accuracy of pattern assignment was limited. There was a significant correlation between automated estimation of fluorescence intensity (probability index of positivity) and end-point titer. Probability index interval specific likelihood ratios for systemic rheumatic disease increased with increasing level of positivity probability. With the HEp-2 substrate, the likelihood ratio for systemic lupus erythematosus was 0.06, 0.4, 6.8, 12.1, and 43.9 for a probability measure of positivity of ≤10, 11-≤30, 31-≤50, 51-≤85, and >85, respectively. CONCLUSION: Quantitative data generated by automated image acquisition facilitates standardized interpretation.