Evaluation of a Point-of-Care N-Terminal Pro-Brain Natriuretic Peptide Assay for Heart Failure Management
Bibliographic record
Abstract
Background: N-terminal pro-brain natriuretic peptide (NT-proBNP) is a key biomarker in heart failure (HF) diagnosis and management. This study aimed to evaluate performances of the LumiraDx® NT-proBNP, a point-of-care testing (POCT) device, focusing on imprecision, method comparison, and clinical practicability. Methods: The LumiraDx® NT-proBNP test was assessed for imprecision across two reagent lots and compared with a reference laboratory method (Cobas e601) using 81 plasma samples. Method concordance was analyzed using Bland-Altman and Passing-Bablok regression. A user satisfaction survey evaluated its practicality in a clinical setting. Results: For the first reagent lot, a coefficient of variation (CV) of 2.81% was observed, while for the second reagent lot, the CV was 5.4%. Method comparison revealed strong concordance with the reference method for NT-proBNP values < 1,000 ng/L. However, a significant bias was observed for values > 1,000 ng/L in the first lot, resolved in the second. User satisfaction surveys highlighted ease of use. Additionally, implementing the LumiraDx® NT-proBNP Platform resulted in a significant reduction in turnaround time, with an estimated 49 min saved in result reporting. Conclusion: The LumiraDx® NT-proBNP POCT device demonstrates strong potential for HF management by combining rapid results, user-friendly operation, and sampling versatility. While biases at higher NT-proBNP levels warrant further standardization, this system represents a practical tool for decentralized HF care.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".