Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework
Pith reviewed 2026-05-14 22:04 UTC · model grok-4.3
The pith
An interpretable predictor-driven framework detects low left ventricular ejection fraction from ECG more accurately than black-box models.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The ECGPD-LEF framework integrates foundation model-derived diagnostic probabilities with interpretable modeling for detecting low left ventricular ejection fraction from ECG. Trained on the EchoNext dataset of 72,475 ECG-echocardiogram pairs, it achieved an internal AUROC of 88.4% and F1 score of 64.5% for moderate LEF in a cohort of 5,442 cases, and external AUROC of 86.8% and F1 of 53.6% in 16,017 cases. It consistently outperformed the benchmark's official end-to-end baseline across subgroups. Interpretability analysis highlighted predictors such as normal ECG, incomplete left bundle branch block, and subendocardial injury, which alone enabled zero-shot-like inference with AUROCs of 75.3
What carries the argument
The ECGPD-LEF framework that uses foundation model-derived diagnostic probabilities as inputs to an interpretable model for LEF risk estimation from ECG.
If this is right
- Outperforms the official end-to-end baseline provided with the EchoNext benchmark across demographic and clinical subgroups.
- High-impact predictors such as normal ECG and incomplete left bundle branch block independently enable zero-shot-like inference without task-specific retraining.
- Supports scalable enhancement through addition of further predictors and seamless integration with existing AI-ECG systems.
- Reconciles high predictive performance with mechanistic transparency for clinical use.
Where Pith is reading between the lines
- Applying this approach in routine ECG screening programs could reduce the number of undetected LEF cases progressing to heart failure.
- The zero-shot performance suggests foundation model probabilities capture intrinsic patterns of ventricular dysfunction that transfer across datasets.
- Adding predictors from new foundation models or clinical variables might further close the gap between internal and external performance.
Load-bearing premise
The diagnostic probabilities produced by the foundation model are reliable across different patient populations and contain enough information about left ventricular function to drive accurate predictions without needing task-specific retraining on echo data.
What would settle it
Testing the framework on a fresh external cohort of ECG-echocardiogram pairs where the AUROC for moderate LEF drops below 80% or where the high-impact predictors show no statistical association with measured ejection fraction values.
read the original abstract
Low left ventricular ejection fraction (LEF) frequently remains undetected until progression to symptomatic heart failure, underscoring the need for scalable screening strategies. Although artificial intelligence-enabled electrocardiography (AI-ECG) has shown promise, existing approaches rely solely on end-to-end black-box models with limited interpretability or on tabular systems dependent on commercial ECG measurement algorithms with suboptimal performance. We introduced ECG-based Predictor-Driven LEF (ECGPD-LEF), a structured framework that integrates foundation model-derived diagnostic probabilities with interpretable modeling for detecting LEF from ECG. Trained on the benchmark EchoNext dataset comprising 72,475 ECG-echocardiogram pairs and evaluated in predefined independent internal (n=5,442) and external (n=16,017) cohorts, our framework achieved robust discrimination for moderate LEF (internal AUROC 88.4%, F1 64.5%; external AUROC 86.8%, F1 53.6%), consistently outperforming the official end-to-end baseline provided with the benchmark across demographic and clinical subgroups. Interpretability analyses identified high-impact predictors, including normal ECG, incomplete left bundle branch block, and subendocardial injury in anterolateral leads, driving LEF risk estimation. Notably, these predictors independently enabled zero-shot-like inference without task-specific retraining (internal AUROC 75.3-81.0%; external AUROC 71.6-78.6%), indicating that ventricular dysfunction is intrinsically encoded within structured diagnostic probability representations. This framework reconciles predictive performance with mechanistic transparency, supporting scalable enhancement through additional predictors and seamless integration with existing AI-ECG systems.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript introduces ECGPD-LEF, a structured framework that fuses pre-computed diagnostic probabilities from an unspecified foundation model with an interpretable linear or threshold-based predictor to detect low left ventricular ejection fraction (LEF) from ECG. On the EchoNext benchmark (72,475 training pairs) it reports internal AUROC 88.4 % / F1 64.5 % and external AUROC 86.8 % / F1 53.6 % for moderate LEF, outperforming the official end-to-end baseline across subgroups; the same predictor coefficients are also shown to support zero-shot inference (internal AUROC 75.3–81.0 %) without task-specific retraining.
Significance. If the foundation-model probabilities prove independent of the EchoNext cohorts, the work supplies a concrete route to interpretable, scalable LEF screening that preserves competitive discrimination while exposing the ECG features driving risk. The zero-shot result, if reproducible, would be a notable demonstration that ventricular-dysfunction information is already linearly separable inside existing diagnostic-probability embeddings.
major comments (2)
- [Methods] Methods (foundation-model paragraph): the identity, pre-training corpus, and training cutoff of the foundation model that supplies the diagnostic probabilities are never stated, nor is any explicit confirmation given that the 72,475 EchoNext ECG-echo pairs were excluded from its pre-training. Because both the headline AUROC numbers and the zero-shot claim rest entirely on these probabilities being uncontaminated and informative, this omission is load-bearing for the central contribution.
- [Results] Results (performance tables and text): AUROC and F1 values are reported without confidence intervals, bootstrap standard errors, or p-values for the comparison against the official end-to-end baseline. Consequently the claim of “consistent outperformance across demographic and clinical subgroups” cannot be statistically evaluated from the supplied numbers.
minor comments (2)
- [Abstract] Abstract: the phrase “zero-shot-like inference” is used without a precise definition; a short clause clarifying that the probabilities themselves are still pre-computed would avoid reader confusion.
- [Figures] Figure legends: axis labels and color keys for the interpretability plots (e.g., coefficient magnitudes for “normal ECG”, “incomplete LBBB”) are not fully legible at print size; adding explicit numeric values or a supplementary table would improve clarity.
Simulated Author's Rebuttal
We thank the referee for the constructive and detailed review. We address each major comment point by point below and have revised the manuscript to incorporate the requested clarifications and statistical reporting.
read point-by-point responses
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Referee: [Methods] Methods (foundation-model paragraph): the identity, pre-training corpus, and training cutoff of the foundation model that supplies the diagnostic probabilities are never stated, nor is any explicit confirmation given that the 72,475 EchoNext ECG-echo pairs were excluded from its pre-training. Because both the headline AUROC numbers and the zero-shot claim rest entirely on these probabilities being uncontaminated and informative, this omission is load-bearing for the central contribution.
Authors: We agree that explicit details on the foundation model are essential for reproducibility and to substantiate the claims. In the revised manuscript we will expand the Methods section to name the specific foundation model, describe its pre-training corpus and training cutoff date, and add an explicit statement confirming that the EchoNext ECG-echo pairs were excluded from pre-training. These additions directly address the load-bearing nature of the omission. revision: yes
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Referee: [Results] Results (performance tables and text): AUROC and F1 values are reported without confidence intervals, bootstrap standard errors, or p-values for the comparison against the official end-to-end baseline. Consequently the claim of “consistent outperformance across demographic and clinical subgroups” cannot be statistically evaluated from the supplied numbers.
Authors: We acknowledge that the lack of uncertainty estimates and formal statistical comparisons limits evaluation of the performance claims. In the revised manuscript we will add 95% bootstrap confidence intervals for all AUROC and F1 values, report bootstrap standard errors, and include p-values for baseline comparisons (using DeLong’s test for AUROCs). These will be incorporated into the Results text, tables, and subgroup analyses. revision: yes
Circularity Check
No significant circularity; derivation uses external probabilities as fixed inputs
full rationale
The paper trains an interpretable model on foundation-model diagnostic probabilities as features and reports AUROCs on held-out internal/external cohorts against an official baseline. The zero-shot claim is the direct use of those fixed probabilities for LEF without retraining, which is a standard transfer step and does not reduce the reported performance to the inputs by construction. No equations, self-citations, ansatzes, or fitted-parameter renamings are shown that would make any prediction equivalent to its own inputs. The framework remains self-contained against the provided benchmarks.
Axiom & Free-Parameter Ledger
free parameters (1)
- interpretable model coefficients and thresholds
axioms (1)
- domain assumption Foundation model diagnostic probabilities capture intrinsic ECG features relevant to ventricular dysfunction
discussion (0)
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