| Title: | Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features |
| Journal: | Circulation Cardiovascular Quality and Outcomes |
| Published: | 14 Nov 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39540287/ |
| DOI: | https://doi.org/10.1161/circoutcomes.123.010602 |
Publication 14182
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Abstract
BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network-derived ECG features could be used to predict future cardiovascular disease and mortality and have phenotypic and genotypic associations.</p>
METHODS: We extracted 5120 neural network-derived ECG features from an artificial intelligence-enabled ECG model trained for 6 simple diagnoses and applied unsupervised machine learning to identify 3 phenogroups. Using the identified phenogroups, we externally validated our findings in 5 diverse cohorts from the United States, Brazil, and the United Kingdom. Data were collected between 2000 and 2023.</p>
RESULTS: In total, 1 808 584 patients were included in this study. In the derivation cohort, the 3 phenogroups had significantly different mortality profiles. After adjusting for known covariates, phenogroup B had a 20% increase in long-term mortality compared with phenogroup A (hazard ratio, 1.20 [95% CI, 1.17-1.23]; P<0.0001; phenogroup A mortality, 2.2%; phenogroup B mortality, 6.1%). In univariate analyses, we found phenogroup B had a significantly greater risk of mortality in all cohorts (log-rank P<0.01 in all 5 cohorts). Phenome-wide association study showed phenogroup B had a higher rate of future atrial fibrillation (odds ratio, 2.89; P<0.00001), ventricular tachycardia (odds ratio, 2.00; P<0.00001), ischemic heart disease (odds ratio, 1.44; P<0.00001), and cardiomyopathy (odds ratio, 2.04; P<0.00001). A single-trait genome-wide association study yielded 4 loci. SCN10A, SCN5A, and CAV1 have roles in cardiac conduction and arrhythmia. ARHGAP24 does not have a clear cardiac role and may be a novel target.</p>
CONCLUSIONS: Neural network-derived ECG features can be used to predict all-cause mortality and future cardiovascular diseases. We have identified biologically plausible and novel phenotypic and genotypic associations that describe mechanisms for the increased risk identified.</p>
18 Keywords
- Aged
- Cardiovascular Diseases
- Electrocardiography
- Female
- Heart Rate
- Humans
- Male
- Middle Aged
- Neural Networks, Computer
- Phenotype
- Predictive Value of Tests
- Prognosis
- Reproducibility of Results
- Risk Assessment
- Risk Factors
- Time Factors
- United States
- Unsupervised Machine Learning
30 Authors
- Arunashis Sau
- Antônio H. Ribeiro
- Kathryn A. McGurk
- Libor Pastika
- Nikesh Bajaj
- Mehak Gurnani
- Ewa Sieliwonczyk
- Konstantinos Patlatzoglou
- Maddalena Ardissino
- Jun Yu Chen
- Huiyi Wu
- Xili Shi
- Katerina Hnatkova
- Sean L. Zheng
- Annie Britton
- Martin Shipley
- Irena Andršová
- Tomáš Novotný
- Ester C. Sabino
- Luana Giatti
- Sandhi M. Barreto
- Jonathan W. Waks
- Daniel B. Kramer
- Danilo Mandic
- Nicholas S. Peters
- Declan P. O'Regan
- Marek Malik
- James S. Ware
- Antonio Luiz P. Ribeiro
- Fu Siong Ng
Enabling scientific discoveries that improve human health