Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F24%3A00081037" target="_blank" >RIV/65269705:_____/24:00081037 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14110/24:00138589

Výsledek na webu

<a href="https://www.ahajournals.org/doi/full/10.1161/CIRCOUTCOMES.123.010602" target="_blank" >https://www.ahajournals.org/doi/full/10.1161/CIRCOUTCOMES.123.010602</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1161/CIRCOUTCOMES.123.010602" target="_blank" >10.1161/CIRCOUTCOMES.123.010602</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features

Popis výsledku v původním jazyce

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. 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. 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&lt;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&lt;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&lt;0.00001), ventricular tachycardia (odds ratio, 2.00; P&lt;0.00001), ischemic heart disease (odds ratio, 1.44; P&lt;0.00001), and cardiomyopathy (odds ratio, 2.04; P&lt;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. 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.

Název v anglickém jazyce

Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features

Popis výsledku anglicky

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. 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. 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&lt;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&lt;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&lt;0.00001), ventricular tachycardia (odds ratio, 2.00; P&lt;0.00001), ischemic heart disease (odds ratio, 1.44; P&lt;0.00001), and cardiomyopathy (odds ratio, 2.04; P&lt;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. 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30201 - Cardiac and Cardiovascular systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Circulation-Cardiovascular Quality and Outcomes

ISSN

1941-7705

e-ISSN

1941-7713

Svazek periodika

17

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

"e010602"

Kód UT WoS článku

001379134100004

EID výsledku v databázi Scopus

2-s2.0-85209674912