Table 3.
Improvement in predictive performance (measured using c-index) when adding the deep learning predicted age (RetiAGE score) to the risk models in the UK Biobank study
| Model 0: RetiAGE | Model 1: CA | Model 2: CA + RetiAGE | Model 3: PhenoAGE | Model 4: PhenoAGE + RetiAGE | |
|---|---|---|---|---|---|
| Primary outcome | |||||
| All-cause mortality | 0.664 (0.653–0.675) | 0.706 (0.696–0.716) | 0.720 (0.709–0.730)a | 0.737 (0.727–0.747) | 0.750 (0.740–0.760)a |
| CVD mortality | 0.702 (0.684–0.720) | 0.742 (0.725–0.759) | 0.760 (0.744–0.777)a | 0.788 (0.773–0.802) | 0.804 (0.790–0.819)a |
| Cancer mortality | 0.657 (0.642–0.671) | 0.696 (0.682–0.709) | 0.709 (0.695–0.722)a | 0.718 (0.705–0.731) | 0.732 (0.718–0.745)a |
| Secondary outcome | |||||
| CVD event | 0.646 (0.631–0.661) | 0.691 (0.673–0.705) | 0.701 (0.687–0.716)a | 0.720 (0.706–0.733) | 0.730 (0.716–0.744)a |
| Cancer event | 0.601 (0.593–0.608) | 0.629 (0.622–0.636) | 0.637 (0.629–0.644)a | 0.646 (0.639–0.654) | 0.653 (0.646–0.661)a |
The values in the table corresponded to the expressed as c-index with their 95% confidence intervals
aSignificant difference between Model 1 and 2 (P < 0.001), and Model 3 and 4 (P < 0.001) based on DeLong’s method.
CVD = cardiovascular disease; RetiAGE = deep learning predicted biological age; PhenoAGE = phenotypic age calculated based on clinical biomarkers (CA, albumin, creatinine, glucose, C-reactive protein [log], lymphocyte percent, mean [red] cell volume, red cell distribution width, alkaline phosphatase, white blood cell count)