A comparison of the CHARGE–AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study
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 d...
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| Published in | The American heart journal Vol. 178; pp. 45 - 54 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Inc
01.08.2016
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0002-8703 1097-6744 |
| DOI | 10.1016/j.ahj.2016.05.004 |
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| Abstract | 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.
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 χ2 statistic for model fit, C-statistic for discrimination, and Hosmer-Lemeshow (HL) χ2 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 χ2 = 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 χ2 = 5.6, P = .69 vs HL χ2 = 28.5, P < .0001).
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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| AbstractList | 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.
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 χ2 statistic for model fit, C-statistic for discrimination, and Hosmer-Lemeshow (HL) χ2 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 χ2 = 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 χ2 = 5.6, P = .69 vs HL χ2 = 28.5, P < .0001).
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 chi 2 statistic for model fit, C-statistic for discrimination, and Hosmer-Lemeshow (HL) chi 2 statistic for calibration. We included 9722 observations (mean age 63.9 plus or minus 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 plus or minus 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 plus or minus 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 chi 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 chi 2 = 5.6, P = .69 vs HL chi 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 χ2statistic for model fit, C-statistic for discrimination, and Hosmer-Lemeshow (HL) χ2statistic 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 χ2= 403 vs 209, both with 1df), 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 χ2= 5.6,P= .69 vs HL χ2= 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. BACKGROUNDAtrial 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 RESULTSWe 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).CONCLUSIONThe CHARGE-AF risk score performed better than the CHA2DS2-VASc risk score at predicting AF in a community-based cohort. 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. 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). The CHARGE-AF risk score performed better than the CHA2DS2-VASc risk score at predicting AF in a community-based cohort. |
| Author | McManus, David D. Yin, Xiaoyan Larson, Martin G. Lubitz, Steven A. Christophersen, Ingrid E. Ellinor, Patrick T. Magnani, Jared W. Benjamin, Emelia J. |
| AuthorAffiliation | g Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA j Boston University School of Public Health, Boston, MA f Mathematics and Statistics Department, Boston University, Boston, MA e Department of Biostatistics, Boston University School of Public Health, Boston, MA d NHLBI and Boston University’s Framingham Heart Study, Framingham, MA a Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA i Department of Medicine, Cardiovascular Medicine Division, University of Massachusetts Medical School, Worcester, MA b Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, MA c Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Norway h Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA |
| AuthorAffiliation_xml | – name: i Department of Medicine, Cardiovascular Medicine Division, University of Massachusetts Medical School, Worcester, MA – name: d NHLBI and Boston University’s Framingham Heart Study, Framingham, MA – name: b Program in Medical and Population Genetics, The Broad Institute of Harvard and MIT, Cambridge, MA – name: c Department of Medical Research, Bærum Hospital, Vestre Viken Hospital Trust, Norway – name: j Boston University School of Public Health, Boston, MA – name: a Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA – name: e Department of Biostatistics, Boston University School of Public Health, Boston, MA – name: f Mathematics and Statistics Department, Boston University, Boston, MA – name: h Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA – name: g Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA |
| Author_xml | – sequence: 1 givenname: Ingrid E. surname: Christophersen fullname: Christophersen, Ingrid E. organization: Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA – sequence: 2 givenname: Xiaoyan surname: Yin fullname: Yin, Xiaoyan organization: NHLBI and Boston University’s Framingham Heart Study, Framingham, MA – sequence: 3 givenname: Martin G. surname: Larson fullname: Larson, Martin G. organization: NHLBI and Boston University’s Framingham Heart Study, Framingham, MA – sequence: 4 givenname: Steven A. surname: Lubitz fullname: Lubitz, Steven A. organization: Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA – sequence: 5 givenname: Jared W. surname: Magnani fullname: Magnani, Jared W. organization: NHLBI and Boston University’s Framingham Heart Study, Framingham, MA – sequence: 6 givenname: David D. surname: McManus fullname: McManus, David D. organization: Department of Medicine, Cardiovascular Medicine Division, University of Massachusetts Medical School, Worcester, MA – sequence: 7 givenname: Patrick T. surname: Ellinor fullname: Ellinor, Patrick T. organization: Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA – sequence: 8 givenname: Emelia J. surname: Benjamin fullname: Benjamin, Emelia J. email: emelia@bu.edu organization: NHLBI and Boston University’s Framingham Heart Study, Framingham, MA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27502851$$D View this record in MEDLINE/PubMed |
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| Copyright | 2016 Elsevier Inc. Copyright © 2016 Elsevier Inc. All rights reserved. Copyright Elsevier Limited Aug 01, 2016 |
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| Snippet | Atrial fibrillation (AF) affects more than 33 million individuals worldwide and increases risks of stroke, heart failure, and death. The CHARGE-AF risk score... Background Atrial fibrillation (AF) affects more than 33 million individuals worldwide and increases risks of stroke, heart failure, and death. The CHARGE-AF... BACKGROUNDAtrial fibrillation (AF) affects more than 33 million individuals worldwide and increases risks of stroke, heart failure, and death. The CHARGE-AF... |
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| SubjectTerms | Aged Aged, 80 and over Atrial Fibrillation - epidemiology Cardiac arrhythmia Cardiology Cohort Studies Comparative studies Female Health risk assessment Humans Incidence Male Middle Aged Proportional Hazards Models Risk Assessment - methods |
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| Title | A comparison of the CHARGE–AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study |
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