1.Cardiac Blood-Based Biomarkers of Myocardial Stress as Predictors of Atrial Fibrillation Development in Patients With Embolic Stroke of Undetermined Source/Cryptogenic Stroke: A Systematic Review and Meta-Analysis
Ana Sofia da SILVA JUSTO ; Sandra Micaela Abreu NÓBREGA ; Ana Luísa AIRES SILVA
Journal of Clinical Neurology 2024;20(3):256-264
Background:
and Purpose Undiagnosed atrial fibrillation (AF) is a major risk factor for stroke that can go unnoticed in individuals with embolic stroke of undetermined source (ESUS) or cryptogenic stroke (CS). Early detection is critical for stroke prognosis and secondary prevention. This study aimed to determine if blood biomarkers of myocardial stress can accurately predict AF in patients with ESUS/CS, which would allow the identification of those who would benefit from closer monitoring.
Methods:
In February 2023 we performed a systematic date-unrestricted search of three databases for studies on patients with ESUS/CS who were subsequently diagnosed with AF. We examined the relationships between AF and serum myocardial stress markers such as brain natriuretic peptide (BNP), N-terminal-pro-BNP (NT-proBNP), midregional proatrial natriuretic peptide, and troponin.
Results:
Among the 1,527 studies reviewed, 23 eligible studies involving 6,212 participants, including 864 with AF, were analyzed. A meta-analysis of 9 studies indicated that they demonstrated a clear association between higher NT-proBNP levels and an increased risk of AF, with adjusted and raw data indicating 3.06- and 9.03-fold higher AF risks, respectively. Lower NT-proBNP levels had a pooled negative predictive value of 91.7%, indicating the potential to rule out AF with an 8% false-negative rate.
Conclusions
Further research is required to fully determine the potential of biomarkers for AF detection after stroke, as results from previous studies lack homogeneity. However, lower NTproBNP levels have potential in ruling out AF in patients with ESUS/CS. Combining them with other relevant biomarkers may enhance the precision of identifying patients who will not benefit from extended monitoring, which would optimize resource allocation and patient care.