BACKGROUND: Early identification of patients at risk of congestive heart failure (HF) may alter their poor prognosis. The aim was therefore to test whether simple electrocardiographic variables, the P-wave and PR-interval, could predict incident HF.

METHODS: The PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study (1016 individuals all aged 70 years, 50% women) was used to identify predictors of HF. Subjects with prevalent HF, QRS duration z >= 130 ms, atrial tachyarrhythmias, implanted pacemaker/defibrillator, second- and third-degree atrioventricular block or delta waves at baseline were excluded. Cox proportional hazard analysis was used to relate the PR interval, P-wave duration (Pdur) and amplitude (Pamp), measured in lead V1, to incident HF. Adjustment was performed for gender, RR-interval, beta-blocking agents, systolic blood pressure, body mass index and smoking.

RESULTS: Out of 836 subjects at risk, 107 subjects were diagnosed with HF during a follow-up of 15 years. In the multivariate analysis, there was a strong U-shaped correlation between Pdur in lead V1 and incident HF (P= 0.0001) which was significant for a Pdur < 60 ms [HR = 2.75; 95% CI: 1.87-4.06, at Pdur 40 ms] but not for prolonged Pdur. There was no significant relationship between incident HF and the PR-interval or the Pamp. A Pdur < 60 ms improved discrimination by 3.7% when added to the traditional risk factors including sex, RR-interval, beta-blocking agents, systolic blood pressure, BMI and smoking (P= 0.048).

CONCLUSIONS: A short Pdur, an easily measured parameter on the ECG, may potentially be a useful marker of future HF, enabling its early detection and prevention, thus improving outcomes.

Atrial fibrillation (AF) is common and increases the risk for stroke and heart failure (HF). The early identification of patients at risk may prevent the development of AF and improve prognosis. This study, therefore, aimed to test the effect of the association between P-wave and PR-interval on the ECG and incident AF. The PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study (1016 individuals all aged 70 years; 50% women) was used to identify whether the ECG variables P-wave duration (Pdur) and PR-duration in lead V1 were related to new-onset AF. Exclusion criteria were prevalent AF, QRS-duration >= 130 milliseconds (msec), atrial tachyarrhythmias and implanted pacemaker/defibrillator. Cox proportional-hazards models were used for analyses. Adjustments were made for gender, RR-interval, beta-blocking agents, systolic blood pressure, body mass index, and smoking. Of 877 subjects at risk, 189 individuals developed AF during a 15-year follow-up. There was a U-shaped relationship between the Pdur and incident AF (P = 0.017) following multiple adjustment. Values below 60 msec were significantly associated with incident AF, with a hazard ratio of 1.55 (95% confidence interval 1.15-2.09) for a Pdur <= 42 msec. There was no significant relationship between incident AF and the PR-interval. A short Pdur derived from the ECG in V1 may be a useful marker for new-onset AF, enabling the early identification of at-risk patients.

Aims: Early identification of individuals at risk for heart failure (HF) may improve their poor prognosis. The aim was to test if a prediction model of ECG variables added to traditional risk factors could improve the prediction of incident HF versus traditional risk factors alone.

Methods and Results: The PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study (1016 individuals aged 70 years) was used for analysis of 23 ECG variables measured in 6 precordial leads. Out of 6 machine learning models used in a training dataset, the one with the best accuracy was used for the testing dataset. 

During 15 years of follow-up, 107 of 836 included individuals at risk were diagnosed with HF. Adding the 8 best ECG variables, identified by random forest in the training dataset, to traditional risk factors resulted in an improvement of the area under the ROC curve by 11.7% (p=0.0043) compared to the traditional risk factor model alone. A high beat-to-beat variation of the R amplitude (SD Ramp) in V1 was the most powerful predictive ECG variable. A decreased low Frequency/high frequency ratio, a heart rate variability index, was correlated to a high SD Ramp in V1 (p=0.002). 

Conclusion: Adding ECG variables to traditional cardiovascular risk factors were valuable for prediction of incident HF in an elderly population. The improvement in C statistics by adding the 8 identified ECG variables was quite substantial, which if reproduced in other populations, might be used as a screening tool for HF risk in clinical practice.

  Background

Early identification of individuals at risk of developing heart failure (HF) may improve poor prognosis. A dominant sympathetic activity is common in HF and associated with worse outcomes; however, less is known about the autonomic balance before HF.

Hypothesis

A low frequency/high frequency (L-F/H-F) ratio, index of heart rate variability, and marker of the autonomic balance predict the development of HF and may improve the performance of the HF prediction model when added to traditional cardiovascular (CV) risk factors.

Methods

Individuals in the PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study (n = 1016, all aged 70 years) were included. Exclusion criteria were prevalent HF, electrocardiographic QRS duration ≥130 millisecond, major arrhythmias, or conduction blocks at baseline. The association between the L-F/H-F ratio and incident HF was assessed using Cox proportional hazard analysis. The C-statistic evaluated whether adding the L-F/H-F-ratio to traditional CV risk factors improved the discrimination of incident HF.

Results

HF developed in 107/836 study participants during 15 years of follow-up. A nonlinear, inverse association between the L-F/H-F ratio and incident HF was mainly driven by an L-F/H-F ratio of <30. The association curve was flat for higher values (hazard ratio, HR for the total curve = 0.78 [95% confidence interval, CI: 0.69−0.88, p < .001]; HR = 2 for L-F/H-F ratio = 10). The traditional prediction model improved by 3.3% (p < .03) when the L-F/H-F ratio was added.

Conclusions

An L-F/H-F ratio of <30 was related to incident HF and improved HF prediction when added to traditional CV risk factors.