Key result
Vectorcardiography-augmented models improve 12-month major cardiovascular events prediction over conventional models to ~0.96 AUC.
Why the study?
To develop and internally validate a vectorcardiography-augmented model for estimating 12-month major adverse cardiovascular events in hospitalized patients with chronic heart failure.
Does a VCG-augmented model improve the prediction of 12-month MACE in hospitalized patients with chronic heart failure?
Cohort (n=160)
No
Does a VCG-augmented model improve the prediction of 12-month MACE in hospitalized patients with chronic heart failure?
A VCG-augmented model incorporating spatial QRS-T angles and loop signs showed excellent internally validated performance for predicting 12-month MACE in hospitalized heart failure patients.
Answers are grounded in this paper and cite their sources.
Should not yet change risk stratification; extends ECG models retrospectively but leaves open prospective validation.
To develop and internally validate a vectorcardiography (VCG)–augmented model for estimating 12-month major adverse cardiovascular events (MACE) in hospitalized patients with chronic heart failure (CHF). We conducted a single-centre retrospective cohort study of adults hospitalized with CHF between 31 May 2023 and 31 May 2024, with data lock on 31 May 2025. The prespecified endpoint was any MACE within 12 months after the index hospitalization. ECG-to-VCG transformation was performed using the Kors method. The prespecified primary six-predictor model included LVEDD, NYHA class, frontal, horizontal, and sagittal QRS–T angles, and the QRS-loop reversal/U-turn sign. BNP and LVEF were evaluated in full-model, comparator-model, and incremental-value analyses. Because the prediction target was fixed 12-month risk rather than time-to-event hazard, multivariable logistic regression was used as the primary modelling approach. Internal validation was performed using 1000 bootstrap resamples, with apparent, optimism-corrected, calibration, and shrinkage-adjusted performance reported. Of 201 screened, 160 were included; MACE occurred in 68/160 (42.5%). The six-predictor model showed an apparent AUC of 0.946 (95% CI 0.914–0.978). Bootstrap internal validation yielded an AUC optimism estimate of 0.012 and an optimism-corrected AUC of 0.934. The apparent Brier score was 0.091, the apparent calibration slope was 1.000, and the apparent calibration intercept was 0.000. After bootstrap correction, the Brier score was 0.106, calibration-in-the-large was − 0.009, calibration intercept was − 0.010, calibration slope was 0.852, and the applied uniform shrinkage factor was 0.852. In formal incremental analyses, adding VCG features to a conventional base model improved AUC from 0.890 to 0.955 (ΔAUC 0.065; DeLong P = 0.001), with a lower Brier score and favourable discrimination/reclassification indices. A VCG-augmented model incorporating LVEDD, NYHA class, spatial QRS–T angles, and the QRS-loop reversal/U-turn sign showed promising internally validated performance for estimating 12-month MACE risk in hospitalized patients with CHF. External multicentre validation is required before clinical implementation.
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Cen et al. (2026) conducted a cohort in Chronic heart failure (n=160). Vectorcardiography (VCG)-augmented model vs. Conventional base model was evaluated on Any major adverse cardiovascular events (MACE) within 12 months after the index hospitalization (ΔAUC 0.065, p=0.001). A vectorcardiography-augmented model improved the prediction of 12-month major adverse cardiovascular events compared to a conventional base model.
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