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

Abstract Background Current dementia prediction models, such as the UK Biobank Dementia Risk Score (UKBDRS, 12 variables), are limited by having variables that are difficult to obtain, subjectively measured, and self-reported. Although individual ECG parameters are linked to higher dementia risk, no study has combined multiple ECG parameters to create an ECG model for dementia prediction. Purpose Using data from the ARIC study, a prospective community-based cohort of Black and White participants in the USA that started in 1987-89, we created an ECG model (based on 7 pre-defined ECG parameters linked to dementia) and compared its model discrimination with the UKBDRS. Methods Participants without prevalent dementia from 2 baselines were included: Visit 4 (V4, 1996-98) and V5 (2011-13). Seven ECG parameters were examined: heart rate, abnormal P-wave terminal force in lead V1 (PTFV1), prolonged P wave duration, abnormal P wave axis, advanced interatrial block, left ventricular hypertrophy (LVH, Cornell voltage criteria), and corrected QT interval (QTc). Dementia cases were identified through 2019 using in-person and phone cognitive assessments, hospitalisation codes, and death certificates. Using a backward selection method (keeping variables with P<0.10 and age, since it is an important dementia risk factor), parsimonious Cox regression models were constructed for prediction of dementia over the longest available follow-up (V4: 23 years, V5: 8 years). Model discrimination of the ECG models and UKBDRS variables was evaluated by the Harrell’s C-statistic (95% CI). Results At V4, 11,520 participants (mean age, 62.8 ± 5.7 years; 56% women; 22% Black) were included; 2,427 developed dementia. At V5, 5,667 participants (mean age, 75.4 ± 5.1 years; 59% women; 22% Black) were included; 830 developed dementia. Figures 1 and 2 show the C-statistic (95%) and area under the ROC curve for the ECG model and UKBDRS variables at V4 and V5, respectively. The C-statistic (95% CI) for both ECG models were >0.70 and comparable with the corresponding UKBDRS models. Conclusion For middle-aged and older adults, a dementia prediction model comprising age and 4 ECG variables has good discrimination for dementia that is comparable with the 12-variable UKBDRS model. Since ECG variables are easily obtainable and objectively measured, these ECG models will be easier to adopt than the UKBDRS model. Further work to validate this model in an external cohort and develop a risk score will facilitate clinical translation.