International journal of cardiology. Heart & vasculature · 2021
Novel model-based point scoring system for predicting stroke risk in atrial fibrillation patients: Results from a nationwide cohort study with validation.
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Chen Yun-Yu, Lin Yenn-Jiang, Chien Kuo-Liong, Chao Tze-Fan, Lo Li-Wei, Chang Shih-Lin, Chung Fa-Po, Lin Chin-Yu, Chang Ting-Yung, Kuo Ling, Hsieh Yu-Cheng, Li Cheng-Hung, Chen Shih-Ann
Аннотация
BACKGROUND: The stroke risk scoring system for atrial fibrillation (AF) patients can vary considerably based on patients' status while receiving ablation. This study aimed to demonstrate a novel scoring system for stroke risk stratification based on the status of catheter ablation.
METHODS: First, 787 patients with AF undergoing ablation were matched according to age, sex, and underlying diseases with the same number of patients not undergoing ablation using the propensity-score (PS)-matched cohort. Multivariate Cox model-derived coefficients were used to construct a simple point-based clinical model using the PS-matched cohort. Thereafter, the novel model (AF-CA-Stroke score) was validated in a nationwide AF cohort.
RESULTS: The AF-CA-Stroke score was calculated based on age (point = 5), ablation status (point = 4), prior history of stroke (point = 4), chronic kidney disease (point = 2), diabetes mellitus (point = 1), and congestive heart failure (point = 1). Risk function to predict the 1-, 5-, 10-year absolute stroke risks was reported. The estimated area under the receive operating characteristic curve of the AF-CA-Stroke score in the PS-matched cohort was 0.845 (95% confidence interval: 0.824-0.865) to predict long-term stroke. A validation study showed that discrimination abilities in the AF-CA-Stroke scores were significantly higher than those in the CHADS2/CHA2DS2-VASc scores. The best cut-off value of the AF-CA-Stroke score to predict future strokes was ≥ 5.
CONCLUSIONS: This novel model-based point scoring system effectively identifies stroke risk using clinical factors and AF ablation status of patients with AF. Various age stratifications and AF ablation should be considered in AF management.
METHODS: First, 787 patients with AF undergoing ablation were matched according to age, sex, and underlying diseases with the same number of patients not undergoing ablation using the propensity-score (PS)-matched cohort. Multivariate Cox model-derived coefficients were used to construct a simple point-based clinical model using the PS-matched cohort. Thereafter, the novel model (AF-CA-Stroke score) was validated in a nationwide AF cohort.
RESULTS: The AF-CA-Stroke score was calculated based on age (point = 5), ablation status (point = 4), prior history of stroke (point = 4), chronic kidney disease (point = 2), diabetes mellitus (point = 1), and congestive heart failure (point = 1). Risk function to predict the 1-, 5-, 10-year absolute stroke risks was reported. The estimated area under the receive operating characteristic curve of the AF-CA-Stroke score in the PS-matched cohort was 0.845 (95% confidence interval: 0.824-0.865) to predict long-term stroke. A validation study showed that discrimination abilities in the AF-CA-Stroke scores were significantly higher than those in the CHADS2/CHA2DS2-VASc scores. The best cut-off value of the AF-CA-Stroke score to predict future strokes was ≥ 5.
CONCLUSIONS: This novel model-based point scoring system effectively identifies stroke risk using clinical factors and AF ablation status of patients with AF. Various age stratifications and AF ablation should be considered in AF management.