What type of study is this?

This is a Meta-Analysis study (also classified as: Systematic Review).

What does this research mean for the field?

Artificial Intelligence models based on electrocardiograms demonstrate moderate-to-good discriminatory ability for predicting heart failure, with a pooled AUROC of 0.76. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.SUPPORTS_CONSENSUS.

What question did this study set out to answer?

This research aims to systematically evaluate how well artificial intelligence models using electrocardiograms predict heart failure.

January 8, 2026Frontiers in Cardiovascular MedicineOpen Access

Artificial Intelligence models show moderate-to-good discriminatory ability for predicting heart failure with pooled AUROC of 0.76.

Q: What is the clinical evidence from this study?

Study design: Systematic Review. Population: Heart Failure (n=1728134). Intervention: Artificial Intelligence models based on ECG vs. Traditional risk models (FHS-HF/PCP-HF). Primary outcome: AUROC for predicting heart failure (AUROC 0.76, 95% CI 0.74–0.78, p=<0.001).

View Full Paper

Ask AI

Bookmark

Key result

Artificial Intelligence models show moderate-to-good discriminatory ability for predicting heart failure with pooled AUROC of 0.76.

Authors

SZShunhong Zhang JJJun Jiang YLYi Luo

Discussion

Member takes

Overview

Systematic review evaluates AI's ability to predict heart failure via ECGs, indicating potential yet uncertain effectiveness.

Key Points

This research aims to systematically evaluate how well artificial intelligence models using electrocardiograms predict heart failure.
Conducted a systematic literature search across multiple databases from 2005 to 2025.
Included studies reporting performance metrics like AUROC from AI models based on ECGs.
Performed meta-analysis using a random-effects model to evaluate efficacy.
Analyzed heterogeneity and conducted subgroup comparisons across ethnicities.
Assessed risk of bias with the PROBAST + AI tool.
Included five studies with 11 cohorts and 1,728,134 participants.
Pooled AUROC/C-statistic found to be 0.76, indicating moderate-to-good discrimination.
Subgroup analyses showed consistent AUROC values across ethnic groups, ranging from 0.77 to 0.79.
Traditional models had slightly lower AUROC values with high heterogeneity among studies.

Study Design

Type

Systematic Review (n=1,728,134)

Multicenter

Yes

PICO

Population

Heart Failure (n=1,728,134)

Intervention / Comparator

Artificial Intelligence models based on ECG vs Traditional risk models (FHS-HF/PCP-HF)

Primary Outcome

AUROC for predicting heart failure — AUROC 0.76 (0.74–0.78), p=<0.001

Main Result

Effect estimate: AUROC 0.76 (95% CI 0.74–0.78)

Absolute Event Rate: 76% vs 74.2%

p-value: p=<0.001

Limitations

Significant heterogeneity observed among studies (I2 = 89%) due to differences in population characteristics and study designs.
Lack of external validation in geographically diverse settings.

Cite This Study

Zhang et al. (2026) conducted a systematic review in Heart Failure (n=1,728,134). Artificial Intelligence models based on ECG vs. Traditional risk models (FHS-HF/PCP-HF) was evaluated on AUROC for predicting heart failure (AUROC 0.76, 95% CI 0.74–0.78, p=<0.001). Artificial Intelligence models show moderate-to-good discriminatory ability for predicting heart failure with pooled AUROC of 0.76.

synapsesocial.com/papers/69608779fa51ca23bb98098a https://doi.org/10.3389/fcvm.2025.1659298

View Full Paper

Ask AI

Bookmark

January 8, 2026Frontiers in Cardiovascular MedicineOpen Access

Artificial Intelligence models show moderate-to-good discriminatory ability for predicting heart failure with pooled AUROC of 0.76.

View Full Paper

Ask AI

Bookmark

Key result

Artificial Intelligence models show moderate-to-good discriminatory ability for predicting heart failure with pooled AUROC of 0.76.

Authors

SZShunhong Zhang JJJun Jiang YLYi Luo

Discussion

Member takes

Overview

Systematic review evaluates AI's ability to predict heart failure via ECGs, indicating potential yet uncertain effectiveness.

Key Points

This research aims to systematically evaluate how well artificial intelligence models using electrocardiograms predict heart failure.
Conducted a systematic literature search across multiple databases from 2005 to 2025.
Included studies reporting performance metrics like AUROC from AI models based on ECGs.
Performed meta-analysis using a random-effects model to evaluate efficacy.
Analyzed heterogeneity and conducted subgroup comparisons across ethnicities.
Assessed risk of bias with the PROBAST + AI tool.
Included five studies with 11 cohorts and 1,728,134 participants.
Pooled AUROC/C-statistic found to be 0.76, indicating moderate-to-good discrimination.
Subgroup analyses showed consistent AUROC values across ethnic groups, ranging from 0.77 to 0.79.
Traditional models had slightly lower AUROC values with high heterogeneity among studies.

Study Design

Type

Systematic Review (n=1,728,134)

Multicenter

Yes

PICO

Population

Heart Failure (n=1,728,134)

Intervention / Comparator

Artificial Intelligence models based on ECG vs Traditional risk models (FHS-HF/PCP-HF)

Primary Outcome

AUROC for predicting heart failure — AUROC 0.76 (0.74–0.78), p=<0.001

Main Result

Effect estimate: AUROC 0.76 (95% CI 0.74–0.78)

Absolute Event Rate: 76% vs 74.2%

p-value: p=<0.001

Limitations

Significant heterogeneity observed among studies (I2 = 89%) due to differences in population characteristics and study designs.
Lack of external validation in geographically diverse settings.

Cite This Study

synapsesocial.com/papers/69608779fa51ca23bb98098a https://doi.org/10.3389/fcvm.2025.1659298

View Full Paper

Ask AI

Bookmark