Development and Internal Validation of a Vectorcardiography-Augmented Model for 12-Month Major Adverse Cardiovascular...

Area of Science:

• Cardiology
• Biomedical Engineering
• Predictive Analytics

Background:

• Chronic heart failure (CHF) patients face high risks of major adverse cardiovascular events (MACE).
• Accurate 12-month MACE risk prediction is crucial for hospitalized CHF patients.
• Current prediction models may lack sufficient accuracy for this high-risk group.

Purpose of the Study:

• To develop and internally validate a novel vectorcardiography (VCG)-augmented model for estimating 12-month MACE risk.
• To assess the incremental value of VCG features over conventional clinical and echocardiographic parameters.
• To provide a more precise risk stratification tool for hospitalized CHF patients.

Main Methods:

• Retrospective cohort study of 160 hospitalized CHF patients.
• ECG-to-VCG transformation using the Kors method.
• Developed a six-predictor model including LVEDD, NYHA class, spatial QRS-T angles, and QRS-loop reversal sign.
• Internal validation using 1000 bootstrap resamples.

Main Results:

• The VCG-augmented model demonstrated strong predictive performance with an optimism-corrected AUC of 0.934.
• Adding VCG features improved AUC from 0.890 to 0.955 (ΔAUC 0.065, P=0.001) compared to a conventional model.
• The model showed good calibration and discrimination, with a corrected Brier score of 0.106.

Conclusions:

• A VCG-augmented model incorporating specific clinical and VCG parameters shows promising internal validation for 12-month MACE risk estimation in hospitalized CHF patients.
• This VCG-based approach offers enhanced predictive accuracy over conventional methods.
• External multicenter validation is recommended prior to clinical implementation.