Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2018
A Simple Score That Predicts Paroxysmal Atrial Fibrillation on Outpatient Cardiac Monitoring after Embolic Stroke of Unknown Source.
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Ricci Brittany, Chang Andrew D, Hemendinger Morgan, Dakay Katarina, Cutting Shawna, Burton Tina, Mac Grory Brian, Narwal Priya, Song Christopher, Chu Antony, Mehanna Emile, McTaggart Ryan, Jayaraman Mahesh, Furie Karen, Yaghi Shadi
Аннотация
BACKGROUND: Occult paroxysmal atrial fibrillation (AF) is detected in 16%-30% of patients with embolic stroke of unknown source (ESUS). The identification of AF predictors on outpatient cardiac monitoring can help guide clinicians decide on a duration or method of cardiac monitoring after ESUS.
METHODS: We included all patients with ESUS who underwent an inpatient diagnostic evaluation and outpatient cardiac monitoring between January 1, 2013, and December 31, 2016. Patients were divided into 2 groups based on detection of AF or atrial flutter during monitoring. We compared demographic data, clinical risk factors, and cardiac biomarkers between the 2 groups. Multivariable logistic regression was used to determine predictors of AF.
RESULTS: We identified 296 consecutive patients during the study period; 38 (12.8%) patients had AF detected on outpatient cardiac monitoring. In a multivariable regression analysis, advanced age (ages 65-74: odds ratio [OR] 2.36, 95% confidence interval [CI] .85-6.52; ages 75 or older: OR 4.08, 95% CI 1.58-10.52) and moderate-to-severe left atrial enlargement (OR 4.66, 95% CI 1.79-12.12) were predictors of AF on outpatient monitoring. We developed the Brown ESUS-AF score: age (65-74 years: 1 point, 75 years or older: 2 points) and left atrial enlargement (moderate or severe: 2 points) with good prediction of AF (area under the curve .725) and was internally validated using bootstrapping. The percentage of patients with AF detected in each score category were as follows: 0: 4.2%; 1: 14.8%; 2: 20.8%; 3: 22.2%; 4: 55.6%.
CONCLUSIONS: The Brown ESUS-AF score predicts AF on prolonged outpatient monitoring after ESUS. More studies are needed to externally validate our findings.
METHODS: We included all patients with ESUS who underwent an inpatient diagnostic evaluation and outpatient cardiac monitoring between January 1, 2013, and December 31, 2016. Patients were divided into 2 groups based on detection of AF or atrial flutter during monitoring. We compared demographic data, clinical risk factors, and cardiac biomarkers between the 2 groups. Multivariable logistic regression was used to determine predictors of AF.
RESULTS: We identified 296 consecutive patients during the study period; 38 (12.8%) patients had AF detected on outpatient cardiac monitoring. In a multivariable regression analysis, advanced age (ages 65-74: odds ratio [OR] 2.36, 95% confidence interval [CI] .85-6.52; ages 75 or older: OR 4.08, 95% CI 1.58-10.52) and moderate-to-severe left atrial enlargement (OR 4.66, 95% CI 1.79-12.12) were predictors of AF on outpatient monitoring. We developed the Brown ESUS-AF score: age (65-74 years: 1 point, 75 years or older: 2 points) and left atrial enlargement (moderate or severe: 2 points) with good prediction of AF (area under the curve .725) and was internally validated using bootstrapping. The percentage of patients with AF detected in each score category were as follows: 0: 4.2%; 1: 14.8%; 2: 20.8%; 3: 22.2%; 4: 55.6%.
CONCLUSIONS: The Brown ESUS-AF score predicts AF on prolonged outpatient monitoring after ESUS. More studies are needed to externally validate our findings.
Предметные рубрики MeSH
Age FactorsAgedAged, 80 and overAmbulatory CareArea Under CurveAtrial FibrillationAtrial FlutterCardiomegalyChi-Square DistributionClinical Decision-MakingDecision Support TechniquesEchocardiographyElectrocardiographyFemaleHumansIntracranial EmbolismLogistic ModelsMaleMiddle AgedMonitoring, AmbulatoryMultivariate AnalysisOdds RatioPredictive Value of TestsROC CurveRegistriesReproducibility of ResultsRetrospective StudiesRisk FactorsStrokeTime Factors