Table 2.
Discriminatory ability of several modeling strategies to predict incident atrial fibrillation compared with the published Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation (CHARGE-AF) model
| Predictive Modeling Strategy | C-Index (95% Confidence Interval) | Difference from CHARGE-AF C Index (95% Confidence Interval) | Net Reclassification Index (95% Confidence Interval)a |
|---|---|---|---|
| CHARGE-AF | 0.674 (0.64 to 0.71) | NA | NA |
| CHARGE-AF (re-estimated) | 0.667 (0.64 to 0.70) | −0.007 (–0.02 to 0.01) | NA |
| Stepwise regression | |||
| Clinical variables only | 0.649 (0.62 to 0.68) | −0.025 (–0.04 to −0.01) | −23 (−35 to −11) |
| Clinical variables + NT-proBNP | 0.704 (0.67 to 0.73) | 0.030 (0.00 to 0.06)b | 15 (2 to 28)b |
| Clinical variables + hsTnT | 0.668 (0.64 to 0.70) | −0.006 (–0.03 to 0.02) | −9 (−21 to 4) |
| Clinical variables + NT-proBNP + hsTnT | 0.707 (0.68 to 0.74) | 0.033 (0.01 to 0.06)b | 13 (1 to 26)b |
| LASSO | |||
| Clinical variables only | 0.665 (0.63 to 0.70) | −0.009 (–0.03 to 0.01) | −22 (−34 to −10) |
| Clinical variables + NT-proBNP | 0.714 (0.68 to 0.75) | 0.040 (0.02 to 0.06)b | 9 (−4 to 22) |
| Clinical variables + hsTnT | 0.681 (0.65 to 0.71) | 0.007 (–0.01 to 0.03) | −1 (−13 to 12) |
| Clinical variables + NT-proBNP + hsTnT | 0.716 (0.69 to 0.75) | 0.041 (0.02 to 0.07)b | 16 (3 to 29)b |
| Ridge regression | |||
| Clinical variables only | 0.669 (0.64 to 0.70) | −0.005 (–0.03 to 0.02) | −24 (−36 to −12) |
| Clinical variables + NT-proBNP | 0.701 (0.67 to 0.73) | 0.027 (0.00 to 0.05)b | 11 (−2 to 24) |
| Clinical variables + hsTnT | 0.679 (0.65 to 0.71) | 0.005 (–0.02 to 0.03) | −14 (−26 to −2) |
| Clinical variables + NT-proBNP + hsTnT | 0.706 (0.66 to 0.74) | 0.032 (0.01 to 0.06)b | 15 (2 to 28)b |
| Likelihood-based boosting model | |||
| Clinical variables only | 0.668 (0.64 to 0.70) | −0.006 (–0.03 to 0.01) | −11 (−24 to 1) |
| Clinical variables + NT-proBNP | 0.717 (0.69 to 0.75) | 0.042 (0.02 to 0.07)b | 18 (5 to 31)b |
| Clinical variables + hsTnT | 0.683 (0.65 to 0.72) | 0.009 (–0.01 to 0.03) | 2 (−10 to 14) |
| Clinical variables + NT-proBNP + hsTnT | 0.718 (0.69 to 0.75) | 0.044 (0.02 to 0.07)b | 21 (8 to 34)b |
| Generalized boosting regression model | |||
| Clinical variables only | 0.663 (0.63 to 0.70) | −0.011 (–0.04 to 0.01) | −28 (−41 to −16) |
| Clinical variables + NT-proBNP | 0.701 (0.67 to 0.74) | 0.027 (–0.00 to 0.06)b | 4 (−9 to 17) |
| Clinical variables + hsTnT | 0.676 (0.64 to 0.71) | 0.002 (–0.03 to 0.03) | −17 (−30 to −5) |
| Clinical variables + NT-proBNP + hsTnT | 0.711 (0.68 to 0.74) | 0.037 (0.01 to 0.07)b | 10 (−3 to 24) |
| Super learner algorithm | 0.720 (0.69 to 0.75) | 0.046 (0.02 to 0.07)b | 15 (2 to 28)b |
The entry for CHARGE-AF is C index and the associated 95% bootstrap confidence interval; all other entries are ten-fold cross-validated C indices or difference in C indices and associated 95% bootstrap confidence intervals compared with the CHARGE-AF model with the original coefficients. CHARGE-AF models predict atrial fibrillation from age, White race/ethnicity, height, weight, systolic BP, diastolic BP, smoking, use of antihypertensives, diabetes, congestive heart failure, and myocardial infarction. Predictions from the super learner algorithm are derived from individual algorithm predictions; weights are shown in Supplemental Table 2. CHARGE-AF, Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation; NA, not applicable; NT-proBNP, N-terminal pro–B-type natriuretic peptide; hsTnT, high-sensitivity troponin T; LASSO, least absolute shrinkage and selection operator.
Net reclassification index is compared with the CHARGE-AF model with re-estimated coefficients.
Indicates statistical significance at the α=0.05 level.