A novel electrocardiogram-based model for prediction of dementia—The Atherosclerosis Risk in Communities (ARIC) study

Aim

Create an ECG-based model to predict dementia and compare its performance with the existing Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) model.

Methods and Results

Participants without prevalent dementia in the Atherosclerosis Risk in Communities study were studied. Visit 4 (V4) (1996–98, mean age, 62 years) and V5 (2011–13, mean age, 75 years) were used as baselines. Incident dementia cases were adjudicated through 2019. We created parsimonious ECG models by using Cox regression with a backward selection method. C-statistic (95 % CI) of the ECG-based model (two or three ECG variables and age) was higher than the CAIDE model (seven variables) at V4 (0.72 [0.71–0.74] vs. 0.67 [0.66–0.68]) and V5 (0.70 [0.68–0.72] vs. 0.64 [0.62–0.66]). The ECG-based model was well calibrated, but the CAIDE model was poorly calibrated at V4 (P < 0.001).

Conclusion

For middle-aged and older adults, a novel ECG-based model has good discrimination that is superior to the CAIDE model in predicting dementia. Since ECG variables are readily obtainable, the ECG-based model will be easy to adopt clinically.