Cardiac natriuretic peptides for diagnosis of covert atrial fibrillation after acute ischaemic stroke: a meta-analysis of diagnostic accuracy studies
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Detection of atrial fibrillation (AF) after acute ischaemic stroke is pivotal for the timely initiation of anticoagulation to prevent recurrence. Besides heart rhythm monitoring, various blood biomarkers have been suggested as complimentary diagnostic tools for AF. We aimed to summarise data on the performance of cardiac natriuretic peptides for the diagnosis of covert AF after acute ischaemic stroke and to assess their potential clinical utility. METHODS: We searched PubMed and Embase for prospective studies reporting the performance of B-type natriuretic peptide (BNP) or N-terminal pro-BNP (NT-proBNP) for the diagnosis of covert AF after acute ischaemic stroke. Summary diagnostic performance measures were pooled using bivariate meta-analysis with a random-effect model. RESULTS: We included six studies focusing on BNP (n=1930) and three studies focusing on NT-proBNP (n=623). BNP had a sensitivity of 0.83 (95% CI 0.64 to 0.93), a specificity of 0.74 (0.67 to 0.81), a positive likelihood ratio of 3.2 (2.6 to 4.0) and a negative likelihood ratio of 0.23 (0.11 to 0.49). NT-proBNP had a sensitivity of 0.91 (0.65 to 0.98), a specificity of 0.77 (0.52 to 0.91), a positive likelihood ratio of 3.9 (1.8 to 8.7) and a negative likelihood ratio of 0.12 (0.03 to 0.48). Considering a pretest probability of 20%, BNP and NT-proBNP had post-test probabilities of 45% and 50%. CONCLUSIONS: NT-proBNP has a better performance than BNP for the diagnosis of covert AF after acute ischaemic stroke. Both biomarkers have low post-test probabilities and may not be used as a stand-alone decision-making tool for the diagnosis of covert AF in patients with acute ischaemic stroke. However, they may be useful for a screening strategy aiming to select patients for long-term monitoring of the heart rhythm.
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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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.066 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".