Key result
The Framingham Risk Score and Pooled Cohort Equations demonstrated similar discrimination (C-index 0.740 vs 0.747) for predicting cardiovascular events, and refitting models did not improve accuracy.
Why the study?
Do published cardiovascular risk scores (FRS and PCE) applied to electronic health data yield accurate estimates of cardiovascular risk, and does refitting improve accuracy?
Cohort (n=84,116)
Do published cardiovascular risk scores (FRS and PCE) applied to electronic health data yield accurate estimates of cardiovascular risk, and does refitting improve accuracy?
Published cardiovascular risk models, particularly the Framingham Risk Score, can be successfully applied to electronic health data without the need for model refitting.
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Supports the direct application of existing risk calculators to electronic health records without recalibration; extends their.
Wolfson et al. (2017) conducted a cohort in Cardiovascular risk (n=84,116). Framingham Risk Score and Pooled Cohort Equations vs. Refitted models was evaluated on Calibration and discrimination of risk scores. The Framingham Risk Score and Pooled Cohort Equations demonstrated similar discrimination (C-index 0.740 vs 0.747) for predicting cardiovascular events, and refitting models did not improve accuracy.
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