Improving 1-year mortality prediction in ACS patients using machine learning.

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Etat: Public
Version: Final published version
Licence: Tous droits réservés
ID Serval
serval:BIB_6CD3CDCD389B
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Improving 1-year mortality prediction in ACS patients using machine learning.
Périodique
European heart journal. Acute cardiovascular care
Auteur⸱e⸱s
Weichwald S., Candreva A., Burkholz R., Klingenberg R., Räber L., Heg D., Manka R., Gencer B., Mach F., Nanchen D., Rodondi N., Windecker S., Laaksonen R., Hazen S.L., von Eckardstein A., Ruschitzka F., Lüscher T.F., Buhmann J.M., Matter C.M.
ISSN
2048-8734 (Electronic)
ISSN-L
2048-8726
Statut éditorial
Publié
Date de publication
27/10/2021
Peer-reviewed
Oui
Volume
10
Numéro
8
Pages
855-865
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
The Global Registry of Acute Coronary Events (GRACE) score is an established clinical risk stratification tool for patients with acute coronary syndromes (ACS). We developed and internally validated a model for 1-year all-cause mortality prediction in ACS patients.
Between 2009 and 2012, 2'168 ACS patients were enrolled into the Swiss SPUM-ACS Cohort. Biomarkers were determined in 1'892 patients and follow-up was achieved in 95.8% of patients. 1-year all-cause mortality was 4.3% (n = 80). In our analysis we consider all linear models using combinations of 8 out of 56 variables to predict 1-year all-cause mortality and to derive a variable ranking.
1.3% of 1'420'494'075 models outperformed the GRACE 2.0 Score. The SPUM-ACS Score includes age, plasma glucose, NT-proBNP, left ventricular ejection fraction (LVEF), Killip class, history of peripheral artery disease (PAD), malignancy, and cardio-pulmonary resuscitation. For predicting 1-year mortality after ACS, the SPUM-ACS Score outperformed the GRACE 2.0 Score which achieves a 5-fold cross-validated AUC of 0.81 (95% CI 0.78-0.84). Ranking individual features according to their importance across all multivariate models revealed age, trimethylamine N-oxide, creatinine, history of PAD or malignancy, LVEF, and haemoglobin as the most relevant variables for predicting 1-year mortality.
The variable ranking and the selection for the SPUM-ACS Score highlight the relevance of age, markers of heart failure, and comorbidities for prediction of all-cause death. Before application, this score needs to be externally validated and refined in larger cohorts.
NCT01000701.
Mots-clé
Acute Coronary Syndromes, GRACE 2.0 Score, Machine Learning, NT-proBNP, age
Pubmed
Web of science
Création de la notice
31/05/2021 8:46
Dernière modification de la notice
24/11/2022 6:46
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