Evaluation of a New NT-proBNP Immunoassay on an Automated Core Laboratory System
Bibliographic record
Abstract
BACKGROUND: Heart failure remains a major cause of morbidity and mortality despite improvements in treatment. This study aimed to evaluate the Alere N-terminal pro B-type natriuretic peptide (NT-proBNP) immunoassay on the Abbott Alinity i platform. METHODS: The analytical performance including precision, linearity, limit of quantitation (LOQ), carryover, dilution-recovery, and stability was evaluated. A method comparison between the Abbott Alere NT-proBNP assay and Roche Elecsys proBNP II assay was performed using 70 residual plasma samples. RESULTS: Total imprecision was 4.1%, 3.5%, and 2.3% for low (120.9 ng/L), medium (333.9 ng/L), and high (4767.4 ng/L) QC levels, respectively. The manufacturer's claimed LOQ of 8.3 ng/L was verified. Method comparison between the Alere NT-proBNP assay and the Elecsys proBNP II assay showed good agreement between assays with an R value of 0.998, a slope of 1.05 (95% CI, 1.03-1.06), and an intercept of 45.81 (95% CI, -46.6.84 to 138.22). The Bland-Altman plot showed an absolute bias of 250 ng/L or 6.02%. Subrange analysis (NT-proBNP <2000 ng/L) showed good agreement with an R value of 0.998, a slope of 1.04 (95% CI, 1.02-1.06), and an intercept of -4.83 (95% CI, -26.95 to 17.28), with a mean bias of 26 ng/L or 3.2%. The stability of NT-proBNP was also verified in lithium heparin plasma samples stored at 4°C over a 7-day period. Hemolysis and lipemia interference thresholds were verified, but icterus impacted NT-proBNP recovery by >20% at low analyte concentrations. CONCLUSIONS: The Alere NT-proBNP assay demonstrated acceptable analytical performance and very good clinical concordance with the Elecsys proBNP II assay.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".