Biological age estimated by Age-Gap predicted all-cause mortality (HR 1.07; 95% CI 1.04-1.10; P<0.001) and was superior to chronological age for predicting long-term cause-specific mortality.
Cohort (n=535)
Does biological age estimation improve the prediction of long-term cause-specific mortality compared to chronological age in patients undergoing percutaneous coronary interventions?
Biological age estimated from deficits and functional impairments is superior to chronological age for predicting long-term mortality after percutaneous coronary interventions.
Effect estimate: HR 1.07 (95% CI 1.04-1.10)
p-value: p=<0.001
Background We tested whether biologic age, as estimated by deficits, functional impairments, or Age‐Gap or their combination, provide improved estimation of cause‐specific death as compared with chronological age. Methods Cardiovascular and noncardiovascular deficits, functional impairments, and Age‐Gap were prospectively collected in 535 patients aged ≥55 years undergoing percutaneous coronary interventions between August 1, 2014, and March 31, 2018. Age‐Gap was calculated as the difference between chronological age and age estimated by artificial intelligence ECG using a convolutional neural network. The full biological age model included deficits, functional impairments, and Age‐Gap >2 SD. A multivariable reduced model with the least number of variables was also created to provide a comparable C index to the full model. Results The average chronological age was 72.1±9.5 years, and there were 68% of men. During a median follow‐up of 2.61 years, 124 (23%) patients died. There was a modest correlation between Age‐Gap and biological age ( r =0.28 95% CI, 0.20–0.35; P <0.001). When modeled with chronologic age as a covariate, Age‐Gap predicted all‐cause (hazard ratio HR, 1.07 95% CI, 1.04–1.10; P <0.001) and cardiovascular (HR, 1.07 95% CI, 1.04–1.11; P <0.001) mortality. As compared with chronological age, the full biological age model noted significant improvement in the prediction of long‐term overall (95% CI, 0.65–0.78), cardiovascular (95% CI, 0.69–0.77), and noncardiovascular (95% CI, 0.55–0.86) mortality. In the reduced models, most prognostic information for noncardiovascular mortality (C index: 0.79) was obtained by subjective difficulty in performing tasks, whereas the deficit‐based estimation predicted cardiovascular mortality (C index: 0.72). Conclusions Estimated biological age from deficits and functional impairments was superior to chronological age in predicting long‐term cause‐specific mortality following percutaneous coronary interventions.
Singh et al. (Wed,) conducted a cohort in percutaneous coronary interventions (n=535). Biological age estimation vs. Chronological age was evaluated on all-cause mortality (HR 1.07, 95% CI 1.04-1.10, p=<0.001). Biological age estimated by Age-Gap predicted all-cause mortality (HR 1.07; 95% CI 1.04-1.10; P<0.001) and was superior to chronological age for predicting long-term cause-specific mortality.