Machine-learning based prediction of in-hospital death for patients with takotsubo syndrome: the InterTAK-ML model

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F23%3A43925920" target="_blank" >RIV/00064173:_____/23:43925920 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11120/23:43925920 RIV/00098892:_____/23:10158390

Result on the web

<a href="https://doi.org/10.1002/ejhf.2983" target="_blank" >https://doi.org/10.1002/ejhf.2983</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/ejhf.2983" target="_blank" >10.1002/ejhf.2983</a>

Result language

angličtina

Original language name

Machine-learning based prediction of in-hospital death for patients with takotsubo syndrome: the InterTAK-ML model

Original language description

AIMS: Takotsubo syndrome (TTS) is associated with a substantial rate of adverse events. We sought to design a machine-learning (ML) based model to predict the risk of in-hospital death and to perform a clustering of TTS patients to identify different risk profiles. METHODS AND RESULTS: A Ridge Logistic Regression-based ML model for predicting in-hospital death was developed on 3482 TTS patients from the International Takotsubo Registry, randomly split in a train and an internal validation cohort (75% and 25% of the sample size, respectively) and evaluated in an external validation cohort (1037 patients). 31 clinically relevant variables were included in the prediction model. Model performance represented the primary endpoint and was assessed according to area under the receiver-operating characteristic curve (AUC), Sensitivity and Specificity. As secondary endpoint, a K-Medoids clustering algorithm was designed to stratify patients into phenotypic groups based on the ten most relevant features emerging from the main model. The overall incidence of in-hospital death was 5.2%. The InterTAK-ML model showed an AUC of 0.89 (0.85-0.92), Sensitivity 0.85 (0.78-0.95) and Specificity 0.76 (0.74-0.79) in the internal validation cohort and an AUC of 0.82 (0.73-0.91), a sensitivity of 0.74 (0.61-0.87) and a specificity of 0.79 (0.77-0.81) in the external cohort for in-hospital death prediction. By exploiting the 10 variables showing the highest feature importance, TTS patients were clustered into six groups associated with different risks of in-hospital death (28.8% vs 15.5% vs 5.4% vs 0.8% vs 0.5%) which were consistent also in the external cohort. CONCLUSION: A ML-based approach for the identification of TTS patients at risk of adverse short-term prognosis is feasible and effective. The InterTAK-ML model showed unprecedented discriminative capability for the prediction of in-hospital death.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30201 - Cardiac and Cardiovascular systems

Project

—

Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2023

Confidentiality

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

Name of the periodical

European Journal of Heart Failure

ISSN

1388-9842

e-ISSN

1879-0844

Volume of the periodical

25

Issue of the periodical within the volume

12

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

2299-2311

UT code for WoS article

001068884600001

EID of the result in the Scopus database

2-s2.0-85171557517