Key result
Adding MR-proANP to clinical variables improved the identification of patients unlikely to have atrial fibrillation after stroke (AUROC 0.72), potentially reducing the need for prolonged cardiac monitoring by 30%.
Why the study?
Prolonged cardiac monitoring increases atrial fibrillation detection after stroke but access is limited, prompting the evaluation of MR-proANP and NT-proBNP to identify patients unlikely to have AFDAS.
Does the addition of MR-proANP or NT-proBNP to clinical variables improve the identification of patients unlikely to have atrial fibrillation after ischaemic stroke?
Cohort (n=621)
Outcome evaluators blinded to biomarker levels
Yes
Does the addition of MR-proANP or NT-proBNP to clinical variables improve the identification of patients unlikely to have atrial fibrillation after ischaemic stroke?
Effect estimate: AUROC 0.72 (95% CI 0.66-0.78)
p-value: p=0.001
Measuring MR-proANP or NT-proBNP alongside clinical variables improves the identification of patients unlikely to have atrial fibrillation after ischemic stroke, potentially reducing the need for prolonged cardiac monitoring by up to 30%.
May aid PCM triage after stroke; leaves open whether biomarker-guided allocation improves detection or outcomes.
Background Prolonged cardiac monitoring (PCM) increases atrial fibrillation detection after stroke (AFDAS) but access is limited. We aimed to assess the utility of midregional pro-atrial natriuretic peptide (MR-proANP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) to identify people who are unlikely to have AFDAS and improve healthcare resource allocation for PCM.. Methods We analysed people from the BIOSIGNAL (Biomarker Signature of Stroke Aetiology) study with ischaemic stroke, no known AF and ≥3 days cardiac monitoring. External validation was in the PRECISE (Preventing Recurrent Cardioembolic Stroke: Right Approach, Right Patient) study of 28-days cardiac monitoring after stroke. The main outcome is no AFDAS. We assessed the discriminatory value of MR-proANP and NT-proBNP combined with clinical variables to identify people with no AFDAS. We determined the net reduction in people who would undergo PCM using the models with 15% AFDAS threshold probability. Results We included 621 people from BIOSIGNAL. The clinical model included age, National Institutes of Health Stroke Scale score, lipid-lowering therapy, creatinine and smoking status. The AUROC was 0.68 (95%CI 0.62-0.74) with clinical variables, which improved with log 10 MR-proANP (0.72,0.66-0.78;p=0.001) or log 10 NT-proBNP (0.71,0.65-0.77;p=0.009). Performance was similar for log 10 MR-proANP versus log 10 NT-proBNP (p=0.28). In 239 people from PRECISE, the AUROC for clinical variables was 0.68 (0.59-0.76), which improved with log 10 NT-proBNP (0.73,0.65-0.82;p<0.001) or log 10 MR-proANP (0.79,0.72-0.86;p<0.001). Performance was better with log 10 MR-proANP versus log 10 NT-proBNP (p=0.03). The models could reduce the number who would undergo PCM by 30% (clinical+log 10 MR-proANP), 27% (clinical+log 10 NT-proBNP) or 20% (clinical). Conclusions MR-proANP and NT-proBNP help classify people who are unlikely to have AFDAS and could reduce the number who need PCM by 30%.
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Cameron et al. (2023) conducted a cohort in Ischaemic stroke without known atrial fibrillation (n=621). MR-proANP and NT-proBNP vs. Clinical variables alone was evaluated on No atrial fibrillation detected after stroke (AFDAS) (AUROC 0.72, 95% CI 0.66-0.78, p=0.001). Adding MR-proANP to clinical variables improved the identification of patients unlikely to have atrial fibrillation after stroke (AUROC 0.72), potentially reducing the need for prolonged cardiac monitoring by 30%.
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