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Heart failure (HF) and atrial fibrillation (AF) are epidemic diseases, frequently coexisting, sharing risk factors and conferring poor prognosis. Identification of individuals at high risk of HF and AF may enable early treatment and improve the prognosis. Reliable prediction models for daily clinical practice are lacking. Early modification and treatment of risk factors may reduce the incidence of AF and HF. Because atrial structure and function abnormalities increase the risk of AF, ECG indices reflecting atrial pathology may prove useful in predicting AF and HF.

The main objectives were to evaluate whether:

• P-wave duration (Pdur) and PR-interval in V1 predicted incident HF and incident AF (Paper I-II)
• low frequency/high frequency (L-F/H-F) ratio, a marker of autonomic balance, predicted incident HF (Paper IV)
• combining selected ECG variables or the L-F/H-F ratio with traditional risk factors improved the performance of the traditional HF prediction model (Paper III-IV).

The Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) with 15 years of follow-up was used for all four studies. After applying the exclusion criteria, 836 subjects were evaluated for incident HF (Paper I, III-IV) and 877 subjects for incident AF (Paper II). Cox proportional hazard analysis related ECG-derived variables to incident HF and incident AF. Study III used machine learning to determine which ECG variables correlated to incident HF. C-statistic was used to test whether adding selected ECG variables to traditional HF risk factors improved the performance of the HF prediction model.

Short Pdur was significantly associated with incident HF (Paper I) and incident AF (Paper II). Of 134 ECG variables, high R-wave amplitude variation (SD Ramp) had the highest predictive value for HF (Paper III). A decreased L-F/H-F ratio significantly predicted HF (Paper IV). Adding eight selected ECG variables (Paper III) and the L-F/H-F ratio (Paper IV) to the traditional risk factors significantly improved HF predictive performance by 11.7% and 3.3%, respectively.

In conclusion, the ECG may prove useful for predicting incident HF and AF beyond the traditional risk factors. An autonomic imbalance may precede the development of HF.