Journal of the American Heart Association · 2026
External Validation of Published Scoring Systems to Predict Incident Atrial Fibrillation After Cryptogenic Stroke/Embolic Stroke of Undetermined Source.
СтатьяВалидационное исследованиеНаблюдательное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Pinnacchio Gaetano, Scacciavillani Roberto, Narducci Maria Lucia, Perna Francesco, Bencardino Gianluigi, Frisullo Giovanni, Broccolini Aldobrando, Marca Giacomo Della, Caliandro Pietro, Di Giorgi Nicoletta, Lenkowicz Jacopo, Carcagnì Antonella, Calabresi Paolo, Sanna Tommaso, Pelargonio Gemma
Аннотация
BACKGROUND: Atrial fibrillation (AF) is a common but often undetected cause of cryptogenic stroke (CS) and embolic stroke of undetermined source (ESUS). Implantable cardiac monitors aid in detecting subclinical AF, but their widespread use is limited by cost and logistical challenges. Several scoring systems exist to predict AF in patients with CS/ESUS, though few have been externally validated. This study was designed to externally validate 7 scoring systems for predicting incident AF in patients with CS/ESUS.
METHODS: In this observational study, we analyzed 132 patients with CS or transient ischemic attack who received implantable cardiac monitors. After a comprehensive literature search, 7 prediction models (Coronary Artery Disease or Chronic Obstructive Pulmonary Disease, Hypertension, Elderly, Systolic Heart Failure, Thyroid Disease [C2HEST], Brown ESUS-AF, ESUS-AF, Hypertension, Age, Valvular Heart Disease, Peripheral Vascular Disease, Obesity, Congestive Heart Failure, Coronary Artery Disease [HAVOC], Age, Hypercholesterolemia, Tricuspid Regurgitation, Left Ventricular End-Diastolic Volume, Left Atrium [ACTEL], Age, Stroke Severity, National Institutes of Health Stroke Scale >5 to Find Atrial Fibrillation [AS5F], and Coronary, Heart Failure, Age, Stroke Severity, Lipidemia, Sugar, Prior Stroke [CHASE-LESS]) were applied. The primary outcome was the discriminatory ability to detect AF. Secondary outcomes included evaluating the threshold scores and the performance of such threshold-based criteria for predicting nonoccurrence of AF with high specificity.
RESULTS: AF was detected in 40 patients (30.3%) over a mean follow-up of 13±12 months. Area under the receiver operating characteristic curves were C2HEST (0.704), Brown ESUS-AF (0.755), AS5F (0.726), ESUS-AF (0.607), HAVOC (0.661), ACTEL (0.650), and CHASE-LESS (0.671). C2HEST, Brown ESUS-AF, and AS5F showed acceptable predictive performance (area under the receiver operating characteristic curve >0.7). Threshold-based criteria using these scores achieved high specificity and positive predictive value. Combining thresholds across scores improved identification of patients unlikely to develop AF.
CONCLUSIONS: C2HEST, Brown ESUS-AF, and AS5F scores reliably predicted AF in patients with CS/ESUS and may help identify low-risk individuals, supporting more targeted use of diagnostic tools and guiding stroke prevention strategies.
METHODS: In this observational study, we analyzed 132 patients with CS or transient ischemic attack who received implantable cardiac monitors. After a comprehensive literature search, 7 prediction models (Coronary Artery Disease or Chronic Obstructive Pulmonary Disease, Hypertension, Elderly, Systolic Heart Failure, Thyroid Disease [C2HEST], Brown ESUS-AF, ESUS-AF, Hypertension, Age, Valvular Heart Disease, Peripheral Vascular Disease, Obesity, Congestive Heart Failure, Coronary Artery Disease [HAVOC], Age, Hypercholesterolemia, Tricuspid Regurgitation, Left Ventricular End-Diastolic Volume, Left Atrium [ACTEL], Age, Stroke Severity, National Institutes of Health Stroke Scale >5 to Find Atrial Fibrillation [AS5F], and Coronary, Heart Failure, Age, Stroke Severity, Lipidemia, Sugar, Prior Stroke [CHASE-LESS]) were applied. The primary outcome was the discriminatory ability to detect AF. Secondary outcomes included evaluating the threshold scores and the performance of such threshold-based criteria for predicting nonoccurrence of AF with high specificity.
RESULTS: AF was detected in 40 patients (30.3%) over a mean follow-up of 13±12 months. Area under the receiver operating characteristic curves were C2HEST (0.704), Brown ESUS-AF (0.755), AS5F (0.726), ESUS-AF (0.607), HAVOC (0.661), ACTEL (0.650), and CHASE-LESS (0.671). C2HEST, Brown ESUS-AF, and AS5F showed acceptable predictive performance (area under the receiver operating characteristic curve >0.7). Threshold-based criteria using these scores achieved high specificity and positive predictive value. Combining thresholds across scores improved identification of patients unlikely to develop AF.
CONCLUSIONS: C2HEST, Brown ESUS-AF, and AS5F scores reliably predicted AF in patients with CS/ESUS and may help identify low-risk individuals, supporting more targeted use of diagnostic tools and guiding stroke prevention strategies.