Organizations: Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China · The Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Institute of Statistics and Big Data, Renmin University of China, Beijing, China · Cardiac Arrhythmia Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China · Function Test Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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.
Figures & tables
Figure 1: Flowchart of model development and evaluation. Model development is based on a traditional AI-ECG interpretation model, where a large unlabeled ECG dataset is used for pre-training and a smaller labeled ECG dataset is used for enhanced post-training. The automatic ECG diagnosis model outputs diagnostic probabilities for each ECG finding and serves as the predictor extractor. Based on the predictor extractor, we developed both a single-predictor approach and a multi-predictor approach. The single-predictor approach enables zero-shot-like inference without further training to detect LEF and can serve as an important indicator. The multi-predictor approach further trains a tabular model using ECG-ECHO pairs and provides both local- and global-level explanations based on SHAP values. Model evaluation is conducted on the EchoNext test set and an independently collected ECHO-Note pairs dataset. Multidimensional model dissection is performed, including overall performance evaluation, model interpretability analysis, and subgroup analyses across diverse populations. LEF, low left ventricular ejection fraction; No-LEF, absence of low left ventricular ejection fraction; SHD, structural heart disease; VHD, valvular heart disease, SHAP, Shapley Additive exPlanations.
ECG–ECHO
ECG–Note
Training set
Validation set
Internal test set
External test set
Patients (n)
26,218
4,626
5,442
16,017
ECGs (n)
72,475
4,626
5,442
16,017
Age groups
18–59
29,783 (41.1%)
1,787 (38.6%)
2,124 (39.0%)
4,270 (26.7%)
60–69
18,745 (25.9%)
1,093 (23.6%)
1,318 (24.2%)
3,637 (22.7%)
Table 1: Baseline demographic and clinical characteristics in the ECG-ECHO and external ECG-Note cohorts.
Predictor
Traditional ECG Model
LEF Detection (Single-predictor)
AUROC
AUPRC
F1 Score
Thresh
AUROC
AUPRC
F1 Score
Thresh
NORM
94.9 (94.1–95.7)
92.8 (91.4–94.2)
85.1 (83.5–86.7)
0.370
81.0 (79.6–82.4)
47.4 (44.2–50.9)
51.4 (49.3–53.8)
0.003641
ILBBB
90.9 (65.3–99.7)
31.6 ( 8.1–70.3)
30.0 ( 0.0–55.6)
0.163
80.0 (78.4–81.5)
48.5 (45.3–52.0)
50.5 (48.0–53.1)
0.000349
INJAL
98.6 (97.3–99.6)
52.2 (27.0–79.8)
47.6 (19.0–72.0)
0.500
75.3 (73.7–76.8)
34.8 (32.4–37.6)
45.1 (42.8–47.4)
0.000386
ISCLA
92.3 (85.5–97.6)
19.0 ( 5.6–45.9)
12.5 ( 0.0–36.4)
0.248
75.9 (74.5–77.4)
38.9 (36.2–42.3)
43.9 (41.6–46.3)
0.001918
ANEUR
96.7 (91.7–99.2)
15.0 ( 6.0–37.3)
11.8 ( 0.0–33.3)
0.294
74.8 (73.1–76.6)
38.9 (35.9–42.3)
42.9 (40.4–45.4)
0.001924
Table 2: Performance of traditional AI-ECG predictors and LEF detection using a single-predictor approach.
Method
Tabular Model
Predictor Extractor
AUROC
AUPRC
F1 Score
Internal test set
Columbia mini model
–
–
85.2 (83.9–86.5)
59.9 (56.6–63.3)
57.9 (55.4–60.3)
ECGPD-LEF
Logistic Regression
Transformer
84.5 (83.1–85.9)
56.8 (53.4–60.3)
57.0 (54.6–59.7)
Transformer-PT
84.9 (83.6–86.3)
57.8 (54.6–61.2)
58.5 (56.2–60.8)
XGBoost
Transformer
84.9 (83.5–86.2)
58.5 (55.1–61.7)
57.6 (55.1–60.1)
Transformer-PT
88.4 (87.1–89.5)
69.2 (66.2–72.0)
64.5 (62.2–66.7)
Table 3: Performance comparison of the proposed ECGPD-LEF framework and baseline Columbia mini model for LEF detection.
Figure 2: Model performance across different evaluation settings. The first row shows receiver operating characteristic (ROC) curves (a) and precision–recall (PR) curves (b) for five methods using the full feature set (71 predictors). The second row presents the performance of four models evaluated with an increasing number of predictors (1–71), in terms of AUROC (c), AUPRC (d) and F1 score (e). The gray dashed line indicates the Columbia mini model and serves as a reference baseline.
Figure 3: Global-level explanation of ECG model predictions across all predictors. The first row shows (a) the cumulative absolute SHAP contributions of all 71 predictors, expressed as percentages of the total contribution, and (b) a SHAP beeswarm plot for the top 10 predictors ranked by F1 score obtained from the single-predictor method. The second and third rows show (c-h) the relationships between individual predictors (NORM, ILBBB, ISCLA, ANEUR, INJIL and ASMI) and their SHAP values. Point density was estimated using a Gaussian kernel density on the log-transformed values. Each plot includes two vertical reference lines: the first (LEF threshold) indicates the threshold that achieves the optimal F1 score using a single-predictor method, and the second (Diagnosis threshold) indicates the positivity threshold defined by an independent ECG diagnosis model.
Figure 4: Local-level explanation of model predictions for a positive and a negative case. The first row shows SHAP waterfall plots for a positive case (a) and a negative case (b), illustrating the top ten ECG predictors contributing to the prediction, with the remaining predictors aggregated as “62 other features”. Feature contributions are shown in the log-odds space and sum to the final model output. The second row presents the corresponding predictor–probability relationships for the same cases (c,d). Vertical reference lines indicate positivity thresholds derived from an independent ECG diagnosis model.
Columbia mini model
ECGPD-LEF (Multi-predictor)
Subgroup
n
Prevalence (%)
AUROC
AUPRC
F1 Score
AUROC
AUPRC
F1 Score
Age groups
18–59
2124
13.6
85.2 (82.8–87.5)
53.5 (48.1–60.0)
53.8 (49.0–58.3)
87.4 (85.0–89.8)
64.6 (59.4–69.9)
59.0 (54.7–63.6)
60–69
1318
16.6
83.8 (80.9–86.6)
57.6 (51.1–64.6)
54.7 (49.0–59.7)
87.2 (84.2–90.2)
70.6 (64.9–76.3)
63.3 (58.1–68.5)
70–79
1154
21.7
86.2 (83.7–88.7)
65.8 (59.5–72.2)
63.7 (58.9–67.9)
89.7 (87.2–91.8)
74.7 (69.5–80.0)
70.5 (65.8–74.8)
80+
846
24.1
83.5 (80.4–86.5)
65.4 (59.5–71.8)
59.5 (54.7–64.4)
87.3 (84.8–89.7)
67.7 (60.9–74.1)
66.1 (61.4–70.8)
Table 4: Subgroup performance of ECGPD-LEF and the Columbia mini model in the internal test set.
Appendix figures & tables14 assets
Supplementary material from the paper’s appendix.
Appendix
ECG-ECHO
ECG–Note
Training set
Validation set
Test set
External Set
Estimated predictor
NORM
19,259 (26.6%)
1,485 (32.1%)
1,806 (33.2%)
3,343 (20.9%)
ILBBB
161 (0.2%)
8 (0.2%)
8 (0.1%)
74 (0.5%)
INJAL
666 (0.9%)
48 (1.0%)
48 (0.9%)
108 (0.7%)
ISCLA
109 (0.2%)
3 (0.1%)
6 (0.1%)
24 (0.1%)
Appendix
Table A.1: Estimated patient characteristics across the training, validation, and test splits of the EchoNext dataset and the external cohort.
Figure A.1: Construction of the external test set. (a) Flowchart illustrating the data selection process, including exclusion criteria applied to the MIMIC-IV database to derive the final high-quality testing set. (b) The prompt template used for the Large Language Model (LLM) Clinical Note Extraction Module, designed to extract quantitative ejection fraction(EF) values from unstructured clinical notes into a structured JSON format.
Training set
Validation set
Test set
Echocardiographic findings
LVWT ≥ 1.3cm
17,667 (24.4%)
877 (19.0%)
1,061 (19.5%)
Aortic stenosis
2,919 (4.0%)
252 (5.4%)
286 (5.3%)
Aortic regurgitation
878 (1.2%)
62 (1.3%)
66 (1.2%)
Mitral regurgitation
6,137 (8.5%)
282 (6.1%)
337 (6.2%)
Tricuspid regurgitation
7,707 (10.6%)
305 (6.6%)
353 (6.5%)
Appendix
Table B.1: Baseline echocardiographic characteristics across the training, validation, and test splits of the ECG-ECHO dataset.
Figure B.1: Model performance stratified by other structural heart disease (SHD). ROC (a,c) and PR (b,d) curves for individuals with other SHD (a,b) and without other SHD (c,d). Other SHD was defined as the presence of at least one predefined echocardiographic abnormality independent of left ventricular ejection fraction (see Appendix B.1 ). Transformer-PT-XGBoost is compared with the Columbia mini model. AUROC and AUPRC are indicated in each panel.
Figure B.2: Model performance stratified by valvular heart disease (VHD). ROC (a,c) and PR (b,d) curves for individuals with moderate or greater VHD (a,b) and without moderate or greater VHD (c,d). VHD was defined as moderate or greater valvular heart disease on echocardiography (see Appendix B.1 ). Transformer-PT-XGBoost is compared with the Columbia mini model. AUROC and AUPRC are indicated in each panel.
Columbia mini model
ECGPD-LEF (Multi-predictor)
Subgroup
n
Prevalence (%)
AUROC
AUPRC
F1 Score
AUROC
AUPRC
F1 Score
Age groups
18–59
4270
12.2
83.0 (80.9–84.9)
46.0 (41.5–50.8)
49.1 (45.4–52.4)
89.5 (88.1–90.9)
62.1 (57.4–66.4)
57.3 (54.2–60.4)
60–69
3637
15.7
82.0 (80.2–83.9)
47.7 (43.7–52.6)
50.1 (46.9–53.1)
88.8 (87.3–90.3)
65.1 (61.4–69.2)
56.3 (53.4–59.3)
70–79
3761
18.0
81.1 (79.2–82.8)
51.6 (47.7–55.9)
50.6 (48.0–53.3)
85.5 (84.0–86.9)
56.6 (53.0–60.9)
54.4 (51.6–57.0)
80+
4349
17.2
77.9 (76.1–79.7)
42.9 (39.5–46.8)
43.5 (40.9–45.9)
83.5 (82.1–85.1)
49.2 (45.6–53.2)
49.6 (47.2–52.1)
Appendix
Table B.2: Subgroup performance of ECGPD-LEF and the Columbia mini model in the external test set.
Grouped
Original Categories
Asian
ASIAN, ASIAN - ASIAN INDIAN, ASIAN - CHINESE, ASIAN - KOREAN, ASIAN - SOUTH EAST ASIAN
Black
BLACK/AFRICAN, BLACK/AFRICAN AMERICAN, BLACK/CAPE VERDEAN, BLACK/CARIBBEAN ISLAND
Hispanic
HISPANIC OR LATINO, CENTRAL AMERICAN, COLUMBIAN, CUBAN, DOMINICAN, GUATEMALAN, HONDURAN, MEXICAN, PUERTO RICAN, SALVADORAN, SOUTH AMERICAN
White
WHITE, WHITE - BRAZILIAN, EASTERN EUROPEAN, OTHER EUROPEAN, RUSSIAN, PORTUGUESE
Other
OTHER, AMERICAN INDIAN/ALASKA NATIVE, MULTIPLE RACE/ETHNICITY, PACIFIC ISLANDER
Unknown
UNKNOWN, UNABLE TO OBTAIN, DECLINED
Appendix
Table B.3: Mapping of original race/ethnicity categories to aggregated subgroups.
Predictor
Traditional ECG Model
LEF Detection (One Predictor)
AUROC
AUPRC
F1 Score
Thresh
AUROC
AUPRC
F1 Score
Thresh
LAFB
98.8 (98.3–99.2)
87.0 (82.1–91.2)
78.9 (74.2–83.5)
0.274
70.0 (68.2–71.7)
29.1 (26.9–31.6)
39.0 (36.8–41.1)
0.001226
ALMI
97.3 (95.0–99.1)
61.3 (43.6–77.1)
58.3 (40.0–73.2)
0.257
68.6 (66.8–70.6)
35.0 (32.3–38.3)
38.6 (36.1–41.1)
0.002106
ABQRS
87.5 (85.5–89.5)
57.4 (51.7–63.5)
54.4 (50.5–58.2)
0.053
68.3 (66.5–70.1)
29.8 (27.5–32.5)
38.0 (35.9–40.1)
0.012123
CLBBB
99.8 (99.6–100.0)
93.0 (85.9–98.4)
89.3 (82.8–94.7)
0.053
67.9 (66.0–69.7)
35.4 (32.5–38.8)
38.0 (35.5–40.2)
0.000006
ILMI
95.7 (91.9–98.5)
63.4 (50.6–75.7)
61.1 (48.9–71.2)
0.157
67.6 (65.7–69.4)
31.7 (29.0–34.8)
37.9 (35.9–40.1)
0.000172
Appendix
Table C.1: Performance of traditional AI-ECG predictors and LEF detection using a single-predictor approach (remaining 61 predictors)
Predictor
LEF Detection (One Predictor)
AUROC
AUPRC
F1 Score
Thresh
NORM
78.8 (77.8-79.6)
36.2 (34.5-38.1)
42.3 (41.0-43.5)
0.003641
ILBBB
71.6 (70.5-72.6)
33.2 (31.5-35.0)
38.2 (36.8-39.6)
0.000349
INJAL
73.4 (72.4-74.3)
27.9 (26.6-29.2)
40.9 (39.6-42.1)
0.000386
ISCLA
67.7 (66.6-68.8)
26.7 (25.4-28.4)
34.0 (32.6-35.2)
0.001918
ANEUR
71.0 (70.0-72.0)
32.1 (30.4-33.8)
38.0 (36.7-39.4)
0.001924
Appendix
Table C.2: Performance of LEF detection using the single-predictor approach on the external test cohort (ECG-Note) for all 71 ECG predictors.
Figure C.1: Operating thresholds of individual predictors for LEF detection. a , Left column: thresholds for the first half of predictors (AUROC ≥ 60%). b , Right column: thresholds for the remaining predictors. For each predictor, three types of thresholds are shown: 1) Diagnosis threshold : threshold for determining whether the ECG shows a positive signal for that predictor; 2) F1-max threshold : threshold for predicting LEF positivity using that predictor, chosen to maximize F1 score on the validation set; 3) Recall-based threshold : threshold for predicting LEF positivity using that predictor, set to achieve recall ≥ 90%. Only predictors with AUROC ≥ 60% are shown for clarity; complete results for all predictors are provided in Tables 2 and C.1 .
NORM
ILBBB
Subgroup
n
Prevalence (%)
AUROC
AUPRC
F1 Score
AUROC
AUPRC
F1 Score
Age groups
18–59
2124
13.6
80.9 (78.2–83.8)
41.3 (36.0–48.3)
47.1 (42.6–51.6)
79.3 (76.2–82.1)
44.1 (38.7–49.9)
44.4 (39.6–49.3)
60–69
1318
16.6
81.6 (78.3–84.8)
49.3 (42.2–56.6)
50.4 (45.5–55.0)
78.1 (74.7–81.1)
44.8 (37.9–51.4)
45.8 (40.1–51.0)
70–79
1154
21.7
80.6 (77.6–83.5)
53.6 (47.7–60.5)
55.4 (51.0–59.9)
81.1 (78.0–84.0)
55.7 (49.5–61.8)
58.5 (53.4–62.9)
80+
846
24.1
77.0 (73.4–80.2)
47.9 (41.9–54.8)
52.8 (48.2–57.1)
77.8 (74.5–81.3)
50.7 (44.8–58.2)
53.8 (48.6–58.9)
Appendix
Table C.3: Subgroup performance of the single-predictor approach for the NORM and ILBBB predictors on the internal test set.
INJIL
ISCLA
Subgroup
n
Prevalence (%)
AUROC
AUPRC
F1 Score
AUROC
AUPRC
F1 Score
Age groups
18–59
2124
13.6
77.4 (74.6–80.2)
32.4 (28.4–37.9)
40.8 (36.6–45.1)
79.0 (76.4–81.8)
36.6 (31.8–43.0)
43.3 (39.3–47.4)
60–69
1318
16.6
75.4 (72.0–78.8)
34.0 (29.0–40.1)
45.5 (40.6–50.2)
74.4 (70.9–78.0)
37.0 (30.8–44.3)
43.0 (38.1–48.0)
70–79
1154
21.7
73.9 (70.6–77.1)
39.5 (34.5–45.6)
48.0 (43.1–52.7)
73.9 (70.6–77.2)
42.5 (36.9–49.3)
45.8 (40.9–50.1)
80+
846
24.1
69.2 (65.1–72.9)
36.3 (31.2–42.8)
47.5 (42.3–52.5)
70.8 (67.2–74.5)
43.3 (37.2–50.1)
43.8 (38.5–48.8)
Appendix
Table C.4: Subgroup performance of the single-predictor method for INJIL and ISCLA in the internal test set.
NORM
ILBBB
Subgroup
n
Prevalence (%)
AUROC
AUPRC
F1 Score
AUROC
AUPRC
F1 Score
Age groups
18–59
4270
12.2
81.5 (79.4–83.3)
37.1 (33.1–41.5)
44.6 (41.6–47.7)
73.7 (71.3–76.0)
33.7 (30.3–38.0)
40.4 (37.7–43.2)
60–69
3637
15.7
78.5 (76.5–80.3)
40.8 (36.9–45.3)
46.2 (43.4–48.9)
71.6 (69.3–73.9)
37.6 (33.9–41.8)
43.4 (40.5–46.1)
70–79
3761
18.0
77.7 (76.1–79.5)
39.3 (36.1–43.3)
43.7 (41.3–46.0)
70.5 (68.4–72.7)
36.0 (33.1–39.7)
39.9 (37.4–42.5)
80+
4349
17.2
74.9 (73.1–76.7)
34.2 (31.3–37.6)
38.4 (36.4–40.4)
67.0 (64.9–69.2)
29.3 (26.8–32.4)
36.1 (33.9–38.5)
Appendix
Table C.5: Subgroup performance of the single-predictor method for NORM and ILBBB in the external test set.
INJIL
ISCLA
Subgroup
n
Prevalence (%)
AUROC
AUPRC
F1 Score
AUROC
AUPRC
F1 Score
Age groups
18–59
4270
12.2
73.1 (71.0–75.1)
25.7 (22.4–29.6)
42.2 (39.1–45.5)
61.0 (58.6–63.4)
23.2 (20.3–26.6)
32.4 (30.0–34.8)
60–69
3637
15.7
72.4 (70.4–74.5)
28.9 (25.6–32.8)
45.0 (42.4–47.6)
61.3 (59.0–63.6)
26.2 (23.4–29.6)
35.6 (33.2–38.1)
70–79
3761
18.0
71.6 (69.8–73.5)
29.4 (26.9–32.4)
43.5 (41.1–45.9)
62.7 (60.6–64.9)
25.9 (23.6–29.1)
33.2 (30.7–35.5)
80+
4349
17.2
69.9 (68.0–71.7)
27.3 (25.1–30.2)
39.2 (36.9–41.3)
62.2 (60.2–64.4)
24.7 (22.7–27.4)
32.0 (29.8–34.3)
Appendix
Table C.6: Subgroup performance of the single-predictor method for INJIL and ISCLA in the internal test set.
Reduced left ventricular ejection fraction (LVEF) is frequently asymptomatic and often detected only after advanced heart failure develops. Electrocardiograms are recorded routinely yet underused for this condition, because reduced LVEF has no single diagnostic waveform. We trained an ensemble of vision transformers from scratch to detect reduced LVEF (≤40%) from 12-lead ECGs, analyzing each heartbeat individually, using 10,142 patients across seven sites in three US health systems. In a held-out external cohort of 4,092 patients from three geographically independent US clinical sites at a real-world reduced-LVEF prevalence of 8.72%, the model achieved an AUROC of 0.88 (95% CI 0.86-0.89), sensitivity 81.2%, specificity 81.0%, and negative predictive value 97.8%. Sensitivity remained high across sex, race, ethnicity, and comorbidity subgroups, while specificity was lower in older patients and those with atrial fibrillation or cardiomyopathy. Beat-level attention maps provided interpretability into the model's predictions, showing consistent focus on the QRS complex rather than the P wave. These findings support the potential of routine ECGs as a scalable first-pass triage step to identify patients who should undergo echocardiography for reduced ejection fraction across diverse patient populations.
Burcu Ozek, Aruna Mohan, David Vorchheimer +5
Walkky LLC, Boston, MA, USA · Northwell Cardiovascular Institute, Lenox Hill Hospital, New York, NY, USA · Weiss Consulting Groups, Boca Raton, FL, USA +2
Left ventricular ejection fraction (LVEF) assessment depends on echocardiography, limiting access in primary care and resource-constrained settings. We developed a multimodal machine-learning framework that combines engineered 12-lead ECG timeseries features with structured EHR variables to classify LVEF into four clinically used strata: normal (>50%), mildly reduced (40-50%), moderately reduced (30-40%), and severely reduced (<30%). To support model explainability, we identified the most influential ECG and EHR features via SHAP attributions. Using retrospective data from Hartford HealthCare, we trained XGBoost models on 36,784 ECG-echocardiogram pairs from 30,952 outpatients and evaluated temporal generalizability on 19,966 ECGs from a subsequent period. The multimodal model achieved one-vs-rest AUROCs of 0.95 (severe), 0.92 (moderate), 0.82 (mild), and 0.91 (normal), outperforming ECG-only and EHR-only baselines, and maintained performance under temporal validation. This work supports ECG-based, multimodal LVEF stratification as a practical screening and triage aid to prioritize confirmatory imaging where resources are limited.
Catherine Ning, Yu Ma, Cindy Beini Wang +4
Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA · Wisconsin School of Business, University of Wisconsin–Madison, Madison, WI, USA · Heart and Vascular Institute, Hartford Hospital, Hartford, CT, USA
Artificial intelligence-enabled electrocardiography (AI-ECG) can detect heart failure (HF), including disease not captured by left ventricular ejection fraction (LVEF), but the cardiac phenotypes underlying model predictions remain unclear. We therefore investigated whether AI-ECG-predicted HF risk aligns with established echocardiographic measures of myocardial dysfunction, remodelling, and filling pressures. We retrospectively analysed ECG and echocardiography data from 8147 patients who underwent both examinations within three days at Akershus University Hospital between 1 January 2023 and 1 June 2025. A previously validated AI-ECG model for HF detection was applied to all ECGs. Spearman's rank correlation ρ quantified associations between echocardiographic parameters and AI-ECG risk. Subgroup analyses were performed by sex and left ventricular ejection fraction (LVEF). External validation included 36,286 ECG-echocardiography pairs from Columbia University Irving Medical Center. Global longitudinal strain (GLS) showed the strongest correlation (ρ=0.57), followed by mitral annular plane systolic excursion (MAPSE) (ρ=-0.49) and LVEF (ρ=-0.45). In patients with LVEF>50%, correlations remained substantial for GLS, MAPSE, and diastolic-related parameters. Volumetric left ventricular indices correlated less strongly in women, whereas diastolic indices showed stronger correlations in women than in men. Physiological validation showed that AI-ECG HF risk predictions align primarily with measures of systolic function, particularly global longitudinal strain, while also capturing diastolic-related abnormalities in patients with preserved LVEF. This approach may improve clinical interpretability and identify opportunities for model refinement.
Elias Stenhede, Eivind Bjørkan Orstad, Torbjørn Omland +2
Medical Technology & E-Health, Akershus University Hospital, 1478 Lørenskog, Norway · Faculty of Medicine, University of Oslo, 0372 Oslo, Norway · Department of Cardiology, Akershus University Hospital, 1478 Lørenskog, Norway +1