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Correlation analysis of cardiac electrophysiological characteristics and cardiovascular disease progression in
1Department of Cardiology, West China Hospital of Sichuan University Chengdu 610041, Sichuan, China.
Insights
Elevated body mass index (BMI), triglycerides (TG), high-sensitivity C-reactive protein (hs-CRP), and prolonged QT/QTc intervals are key indicators for cardiovascular disease (CVD) risk in arrhythmia patients. Combining these factors enhances CVD prediction accuracy.
Area of Science:
- Cardiology
- Clinical Electrophysiology
- Biochemistry
Background:
- Cardiovascular disease (CVD) poses a significant risk to patients with arrhythmia.
- Identifying predictive indicators for CVD onset in this population is crucial for timely intervention.
Purpose of the Study:
- To investigate the correlation between cardiac electrophysiological characteristics and CVD in arrhythmia patients.
- To identify specific indicators for predicting CVD onset.
Main Methods:
- 100 arrhythmia patients were analyzed for CVD incidence over a two-year period.
- Baseline characteristics, biochemical markers, and ECG parameters were compared between CVD and non-CVD groups.
- Spearman correlation, logistic regression, and ROC curve analyses were employed to identify risk factors and assess predictive power.
Main Results:
- 31% of patients developed CVD.
- Higher BMI, hypertension, triglycerides (TG), hs-CRP, BNP, and prolonged QT/QTc intervals were associated with CVD.
- Lower HDL-C levels were observed in the CVD group.
- Combined assessment of BMI, TG, hs-CRP, and QT/QTc intervals achieved an AUC of 0.951 for CVD prediction.
Conclusions:
- Elevated BMI, TG, hs-CRP, and prolonged QT/QTc intervals are significant risk factors for CVD in arrhythmia patients.
- These factors are strongly associated with the development of cardiovascular disease.
- A combined evaluation of these indicators significantly improves the accuracy of CVD risk prediction.
Objective:
To investigate a correlation between cardiac electrophysiological characteristics and cardiovascular disease (CVD) in patients with arrhythmia, and to identify indicators for predicting CVD onset.
Methods:
We enrolled 100 arrhythmia patients treated at West China Hospital of Sichuan University between May 2021 and May 2023. The incidence of CVD was assessed during hospitalization and within one year post-discharge. We compared baseline characteristics, biochemical markers, and electrocardiogram parameters between patients who developed CVD and those who did not. Spearman correlation analysis was conducted to explore linear relationships between indicators and CVD incidence. Multivariate logistic regression was applied to identify associations between variables and CVD. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive power of individual and combined risk factors.
Results:
Of the 100 patients, 31 (31%) developed CVD. The CVD group exhibited significantly higher BMI, hypertension prevalence, triglycerides (TG), high-sensitivity C-reactive protein (hs-CRP), B-type natriuretic peptide (BNP), and QT/QTc intervals compared to the non-CVD group. HDL-C levels were significantly lower in the CVD group (P < 0.05). Spearman correlation analysis revealed positive correlations between BMI, hypertension, TG, hs-CRP, BNP, QT, and QTc with CVD (r = 0.243, 0.563, 0.384, 0.514, 0.238, 0.355, 0.327, all P < 0.05). Conversely, HDL-C showed a negative correlation with CVD (r = -0.200, P < 0.05). QTc correlated significantly with BMI, hypertension, TG, and hs-CRP (r = 0.263, 0.221, 0.255, 0.200, all P < 0.05). Multi-factor logistic regression identified BMI, TG, hs-CRP, and QT/QTc intervals as significant risk factors for CVD (all P < 0.05). ROC curve analysis showed that the combined assessment of BMI, TG, hs-CRP, QT, and QTc yielded an AUC of 0.951, with sensitivity of 92.7% and specificity of 86.4%, outperforming individual tests.
Conclusion:
Elevated BMI, TG, hs-CRP, and prolonged QT/QTc intervals are significantly associated with the development of CVD in arrhythmia patients. Combined evaluation of these factors improves the accuracy of CVD risk prediction.
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