Natriuretic Peptides to Classify Risk of Atrial Fibrillation Detection After Stroke: Analysis of the BIOSIGNAL and PRECISE Cohort Studies.

Background And Objectives: Prolonged cardiac monitoring (PCM) increases atrial fibrillation (AF) detection after ischemic stroke, but access is limited, and it is burdensome for patients. Our objective was to assess whether midregional proatrial natriuretic peptide (MR-proANP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) could classify people who are unlikely to have AF after ischemic stroke and allow better targeting of PCM.

Methods: We analyzed people from the Biomarker Signature of Stroke Aetiology (BIOSIGNAL) study with ischemic stroke, no known AF, and ≥3 days cardiac monitoring. External validation was performed in the Preventing Recurrent Cardioembolic Stroke: Right Approach, Right Patient (PRECISE) study of 28 days of cardiac monitoring in people with ischemic stroke or transient ischemic attack and no known AF. The main outcome is no AF detection. We assessed the discriminatory value of MR-proANP and NT-proBNP combined with clinical variables to identify people with no AF. A decision curve analysis was performed with combined data to determine the net reduction in people who would undergo PCM using the models based on a 15% threshold probability for AF detection.

Results: We included 621 people from the BIOSIGNAL study. The clinical multivariable prediction model included age, NIH Stroke Scale score, lipid-lowering therapy, creatinine, and smoking status. The area under the receiver-operating characteristic curve (AUROC) for clinical variables was 0.68 (95% CI 0.62-0.74), which improved with the addition of logMR-proANP (0.72, 0.66-0.78; = 0.001) or logNT-proBNP (0.71, 0.65-0.77; = 0.009). Performance was similar for the models with logMR-proANP vs logNT-proBNP ( = 0.28). In 239 people from the PRECISE study, the AUROC for clinical variables was 0.68 (0.59-0.76), which improved with the addition of logNT-proBNP (0.73, 0.65-0.82; < 0.001) or logMR-proANP (0.79, 0.72-0.86; < 0.001). Performance was better for the model with logMR-proANP vs logNT-proBNP ( = 0.03). The models could reduce the number of people who would undergo PCM by 30% (clinical and logMR-proANP), 27% (clinical and logNT-proBNP), or 20% (clinical only).

Discussion: MR-proANP and NT-proBNP help classify people who are unlikely to have AF after ischemic stroke. Measuring MR-proANP or NT-proBNP could reduce the number of people who need PCM by 30%, without reducing the amount of AF detected.

Trial Registration Information: NCT02274727; clinicaltrials.gov/study/NCT02274727.