SwePub - sökning: WFRF:(Gulsvik Anne Kristine)

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1.	Tancin Lambert, Anna, et al. (författare) Biomarkers Predictive of Atrial Fibrillation in Patients with Cryptogenic Stroke. Insights from The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study 2023 Ingår i: European Journal of Neurology. - : Wiley. - 1351-5101 .- 1468-1331. ; 30:5, s. 1352-1363 Tidskriftsartikel (refereegranskat)abstract BACKGROUND: There are currently no biomarkers used to select cryptogenic stroke (CS) patients for monitoring with insertable cardiac monitors (ICMs), the most effective tool for diagnosing atrial fibrillation (AF) in CS. The purpose of this study was to assess clinically available biomarkers as predictors of AF.METHODS: Eligible CS and cryptogenic transient ischemic attack (TIA) patients underwent 12-month monitoring with ICMs, clinical follow-up, and biomarker sampling. Levels of cardiac and thromboembolic biomarkers, taken within 14 days from symptom onset, were compared between patients diagnosed with AF (n=74) during monitoring and those without AF (n=185). Receiver operating characteristic (ROC) curves were created. Biomarkers reaching area under ROC curve (AUC) ≥ 0.7 were dichotomized by finding optimal cut-off values and used in logistic regression establishing their predictive value for increased risk of AF in unadjusted and adjusted models.RESULTS: B-type natriuretic peptide (BNP), N-terminal pro-brain natriuretic peptide (NT-proBNP), creatine kinase, D-dimer, high-sensitivity cardiac Troponin I and T were significantly higher in the AF than non-AF group. BNP and NT-proBNP reached predefined AUC level, 0.755 and 0.725 respectively. Optimal cut-off values were 33.5 ng/L for BNP, and 87 ng/L for NT-proBNP. Regression analysis showed that NT-proBNP was a predictor of AF in both unadjusted, odds ratio (OR) 7.72 (95% confidence interval [CI] 3.16-18.87), and age and sex adjusted models, OR 4.82 (95% CI 1.79-12.96).CONCLUSION: Several clinically established biomarkers were associated with AF. NT-proBNP performed best as AF predictor and could be used for selecting patients for long-term monitoring with ICMs.

Tancin Lambert, Anna, et al. (författare)
Biomarkers Predictive of Atrial Fibrillation in Patients with Cryptogenic Stroke. Insights from The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study
2023
Ingår i: European Journal of Neurology. - : Wiley. - 1351-5101 .- 1468-1331. ; 30:5, s. 1352-1363
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: There are currently no biomarkers used to select cryptogenic stroke (CS) patients for monitoring with insertable cardiac monitors (ICMs), the most effective tool for diagnosing atrial fibrillation (AF) in CS. The purpose of this study was to assess clinically available biomarkers as predictors of AF.METHODS: Eligible CS and cryptogenic transient ischemic attack (TIA) patients underwent 12-month monitoring with ICMs, clinical follow-up, and biomarker sampling. Levels of cardiac and thromboembolic biomarkers, taken within 14 days from symptom onset, were compared between patients diagnosed with AF (n=74) during monitoring and those without AF (n=185). Receiver operating characteristic (ROC) curves were created. Biomarkers reaching area under ROC curve (AUC) ≥ 0.7 were dichotomized by finding optimal cut-off values and used in logistic regression establishing their predictive value for increased risk of AF in unadjusted and adjusted models.RESULTS: B-type natriuretic peptide (BNP), N-terminal pro-brain natriuretic peptide (NT-proBNP), creatine kinase, D-dimer, high-sensitivity cardiac Troponin I and T were significantly higher in the AF than non-AF group. BNP and NT-proBNP reached predefined AUC level, 0.755 and 0.725 respectively. Optimal cut-off values were 33.5 ng/L for BNP, and 87 ng/L for NT-proBNP. Regression analysis showed that NT-proBNP was a predictor of AF in both unadjusted, odds ratio (OR) 7.72 (95% confidence interval [CI] 3.16-18.87), and age and sex adjusted models, OR 4.82 (95% CI 1.79-12.96).CONCLUSION: Several clinically established biomarkers were associated with AF. NT-proBNP performed best as AF predictor and could be used for selecting patients for long-term monitoring with ICMs.

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