Derivation | Validation | Combined | Reduced | |

Male model | ||||

Discrimination | ||||

C-statistic (95% CI) | 0.82 (0.80 to 0.83) | 0.83 (0.81 to 0.85) | 0.82 (0.81 to 0.84) | 0.82 (0.81 to 0.84) |

Ratio of 95 to 5 risk percentile | 28.6 (0.128/0.004) | 26.1 (0.119/0.005) | 36.5 (0.134/0.004) | 36.2 (0.134/0.004) |

Calibration | ||||

Observed vs predicted1 | −0.76% | 4.21% | −0.61% | −0.61% |

5-year cumulative incidence (observed) (95% CI) | 0.044 (0.041 to 0.047) | 0.045 (0.041 to 0.048) | 0.044 (0.042 0.047) | 0.044 (0.042 to 0.047) |

5-year risk (predicted) | 0.045 | 0.043 | 0.045 | 0.045 |

Calibration slope and intercept | 0.7859 to 0.0098 | 0.7799 to 0.0080 | 0.8240 to 0.0081 | 0.8285 to 0.0079 |

Overall performance | ||||

Brier Score (scaled) | 0.081 | 0.068 | 0.086 | 0.086 |

Nagelkerke R^{2} | 0.105 | 0.123 | 0.100 | 0.100 |

Female model | ||||

Discrimination | ||||

C-statistic (95% CI) | 0.82 (0.81 to 0.83) | 0.83 (0.81 to 0.85) | 0.83 (0.82 to 0.83) | 0.83 (0.82 to 0.83) |

Ratio of 95 to 5 risk percentile | 54.3 (0.171/0.003) | 50.6 (0.167/0.003) | 64.8 (0.178/0.003) | 64.6 (0.178/0.003) |

Calibration | ||||

Observed vs predicted* | −1.07% | −10.58% | −0.78% | −0.78% |

5-year cumulative incidence (observed) (95% CI) | 0.060 (0.057 to 0.062) | 0.053 (0.050 to 0.057) | 0.057 (0.055 to 0.060) | 0.057 (0.055 to 0.060) |

5-year risk (predicted) | 0.060 | 0.059 | 0.058 | 0.058 |

Calibration slope and intercept | 0.7671 to 0.0145 | 0.8666 to 0.0128 | 0.8320 to 0.0102 | 0.8335 to 0.0101 |

Overall performance | ||||

Brier Score (scaled) | 0.107 | 0.102 | 0.111 | 0.111 |

Nagelkerke R^{2} | 0.147 | 0.132 | 0.133 | 0.133 |

*Three types of performance tests were examined.22

**(1) Discrimination**is the ability of a predictive model to differentiate between those who experience the outcome from those who do not.*C-statistic*is a rank order statistic for predictions against true outcomes.24 The statistic ranges from 0 to 1; a value of 0.5 indicates the model is no better than random prediction, while a value of 1 indicates the model perfectly predicts whose who will develop the outcome of interest and who will not.*Ratio of 95 to 5 risk percentile*is a measure indicating the spread of the predicted risks, where a higher ratio indicates a more discriminating algorithm. For example, a ratio of 20 indicates that the absolute risk of the event of interest is 20 times higher for a person in the 95^{th}percentile of risk than for a person in the 5^{th}percentile of risk.**(2) Calibration**(or accuracy) describes how well the predicted probability of disease agrees with the observed outcomes.*Observed versus predicted (O vs P*) is the relative difference between the observed incidence and the predicted risk, calculated as (Observed – Predicted)/Observed × 100. The absolute values for this calculation are the*observed 5-year cumulative incidence*and the*predicted 5-year risk*. A 1% difference indicates that 1% more events were observed than were predicted. This tables show overall O vs P. Online supplemental digital content 6 and 7 show O vs P for specific subgroups. The*calibration slope and intercept*indicates the slope and intercept of the calibration plots. Figure 1 displays the calibration plots for validation data.**(3) Overall performance measures**.*Brier score (scaled*) is a measure of overall agreement between observed and predicted risk with values between 0 and 1.33*Nagelkerke R*is a measure of the amount of variation in risk between individuals in the data that is explained by the model, with values from 0 to 132. Larger values indicate that more variation is explained.^{2}