Ingrid E Christophersen1, Xiaoyan Yin2, Martin G Larson3, Steven A Lubitz4, Jared W Magnani5, David D McManus6, Patrick T Ellinor4, Emelia J Benjamin7. 1. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA; Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, MA; Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Norway. 2. NHLBI and Boston University's Framingham Heart Study, Framingham, MA; Department of Biostatistics, Boston University School of Public Health, Boston, MA. 3. NHLBI and Boston University's Framingham Heart Study, Framingham, MA; Department of Biostatistics, Boston University School of Public Health, Boston, MA; Mathematics and Statistics Department, Boston University, Boston, MA. 4. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA; Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, MA; Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA. 5. NHLBI and Boston University's Framingham Heart Study, Framingham, MA; Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA. 6. Department of Medicine, Cardiovascular Medicine Division, University of Massachusetts Medical School, Worcester, MA. 7. NHLBI and Boston University's Framingham Heart Study, Framingham, MA; Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA; Boston University School of Public Health, Boston, MA. Electronic address: emelia@bu.edu.
Abstract
BACKGROUND: Atrial fibrillation (AF) affects more than 33 million individuals worldwide and increases risks of stroke, heart failure, and death. The CHARGE-AF risk score was developed to predict incident AF in three American cohorts and it was validated in two European cohorts. The CHA2DS2-VASc risk score was derived to predict risk of stroke, peripheral embolism, and pulmonary embolism in individuals with AF, but it has been increasingly used for AF risk prediction. We compared CHARGE-AF risk score versus CHA2DS2-VASc risk score for incident AF risk in a community-based cohort. METHODS AND RESULTS: We studied Framingham Heart Study participants aged 46 to 94 years without prevalent AF and with complete covariates. We predicted AF risk using Fine-Gray proportional sub-distribution hazards regression. We used the Wald χ(2) statistic for model fit, C-statistic for discrimination, and Hosmer-Lemeshow (HL) χ(2) statistic for calibration. We included 9722 observations (mean age 63.9 ± 10.6 years, 56% women) from 4548 unique individuals: 752 (16.5%) developed incident AF and 793 (17.4%) died. The mean CHARGE-AF score was 12.0 ± 1.2 and the sub-distribution hazard ratio (sHR) for AF per unit increment was 2.15 (95% CI, 99-131%; P < .0001). The mean CHA2DS2-VASc score was 2.0 ± 1.5 and the sHR for AF per unit increment was 1.43 (95% CI, 37%-51%; P < .0001). The CHARGE-AF model had better fit than CHA2DS2-VASc (Wald χ(2) = 403 vs 209, both with 1 df), improved discrimination (C-statistic = 0.75, 95% CI, 0.73-0.76 vs C-statistic = 0.71, 95% CI, 0.69-0.73), and better calibration (HL χ(2) = 5.6, P = .69 vs HL χ(2) = 28.5, P < .0001). CONCLUSION: The CHARGE-AF risk score performed better than the CHA2DS2-VASc risk score at predicting AF in a community-based cohort.
BACKGROUND: Atrial fibrillation (AF) affects more than 33 million individuals worldwide and increases risks of stroke, heart failure, and death. The CHARGE-AF risk score was developed to predict incident AF in three American cohorts and it was validated in two European cohorts. The CHA2DS2-VASc risk score was derived to predict risk of stroke, peripheral embolism, and pulmonary embolism in individuals with AF, but it has been increasingly used for AF risk prediction. We compared CHARGE-AF risk score versus CHA2DS2-VASc risk score for incident AF risk in a community-based cohort. METHODS AND RESULTS: We studied Framingham Heart Study participants aged 46 to 94 years without prevalent AF and with complete covariates. We predicted AF risk using Fine-Gray proportional sub-distribution hazards regression. We used the Wald χ(2) statistic for model fit, C-statistic for discrimination, and Hosmer-Lemeshow (HL) χ(2) statistic for calibration. We included 9722 observations (mean age 63.9 ± 10.6 years, 56% women) from 4548 unique individuals: 752 (16.5%) developed incident AF and 793 (17.4%) died. The mean CHARGE-AF score was 12.0 ± 1.2 and the sub-distribution hazard ratio (sHR) for AF per unit increment was 2.15 (95% CI, 99-131%; P < .0001). The mean CHA2DS2-VASc score was 2.0 ± 1.5 and the sHR for AF per unit increment was 1.43 (95% CI, 37%-51%; P < .0001). The CHARGE-AF model had better fit than CHA2DS2-VASc (Wald χ(2) = 403 vs 209, both with 1 df), improved discrimination (C-statistic = 0.75, 95% CI, 0.73-0.76 vs C-statistic = 0.71, 95% CI, 0.69-0.73), and better calibration (HL χ(2) = 5.6, P = .69 vs HL χ(2) = 28.5, P < .0001). CONCLUSION: The CHARGE-AF risk score performed better than the CHA2DS2-VASc risk score at predicting AF in a community-based cohort.
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