A novel prognostic tool to predict mortality in patients with atrial fibrillation: The BASIC-AF risk score.
atrial fibrillation
biomarkers
left atrial volume
machine learning
mortality
risk score
Journal
Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
ISSN: 2241-5955
Titre abrégé: Hellenic J Cardiol
Pays: Netherlands
ID NLM: 101257381
Informations de publication
Date de publication:
Historique:
received:
01
12
2020
revised:
03
01
2021
accepted:
25
01
2021
pubmed:
2
2
2021
medline:
17
11
2021
entrez:
1
2
2021
Statut:
ppublish
Résumé
This study sought to develop and validate a risk score to predict mortality in patients with atrial fibrillation (AF) after a hospitalization for cardiac reasons. The new risk score was derived from a prospective cohort of hospitalized patients with concurrent AF. The outcome measures were all-cause and cardiovascular mortality. Random forest was used for variable selection. A risk points model with predictor variables was developed by weighted Cox regression coefficients and was internally validated by bootstrapping. In total, 1130 patients with AF were included. During a median follow-up of 2 years, 346 (30.6%) patients died and 250 patients had a cardiovascular cause of death. N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin-T were the most important predictors of mortality, followed by indexed left atrial volume, history and type of heart failure, age, history of diabetes mellitus, and intraventricular conduction delay, all forming the BASIC-AF risk score (Biomarkers, Age, ultraSound, Intraventricular conduction delay, and Clinical history). The score had good discrimination for all-cause (c-index = 0.85 and 95% CI 0.82-0.88) and cardiovascular death (c-index = 0.84 and 95% CI 0.81-0.87). The predicted probability of mortality varied more than 50-fold across deciles and adjusted well to observed mortality rates. A decision curve analysis revealed a significant net benefit of using the BASIC-AF risk score to predict the risk of death, when compared with other existing risk schemes. We developed and internally validated a well-performing novel risk score for predicting death in patients with AF. The BASIC-AF risk score included routinely assessed parameters, selected through machine-learning algorithms, and may assist in tailored risk stratification and management of these patients.
Sections du résumé
BACKGROUND
BACKGROUND
This study sought to develop and validate a risk score to predict mortality in patients with atrial fibrillation (AF) after a hospitalization for cardiac reasons.
METHODS
METHODS
The new risk score was derived from a prospective cohort of hospitalized patients with concurrent AF. The outcome measures were all-cause and cardiovascular mortality. Random forest was used for variable selection. A risk points model with predictor variables was developed by weighted Cox regression coefficients and was internally validated by bootstrapping.
RESULTS
RESULTS
In total, 1130 patients with AF were included. During a median follow-up of 2 years, 346 (30.6%) patients died and 250 patients had a cardiovascular cause of death. N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin-T were the most important predictors of mortality, followed by indexed left atrial volume, history and type of heart failure, age, history of diabetes mellitus, and intraventricular conduction delay, all forming the BASIC-AF risk score (Biomarkers, Age, ultraSound, Intraventricular conduction delay, and Clinical history). The score had good discrimination for all-cause (c-index = 0.85 and 95% CI 0.82-0.88) and cardiovascular death (c-index = 0.84 and 95% CI 0.81-0.87). The predicted probability of mortality varied more than 50-fold across deciles and adjusted well to observed mortality rates. A decision curve analysis revealed a significant net benefit of using the BASIC-AF risk score to predict the risk of death, when compared with other existing risk schemes.
CONCLUSIONS
CONCLUSIONS
We developed and internally validated a well-performing novel risk score for predicting death in patients with AF. The BASIC-AF risk score included routinely assessed parameters, selected through machine-learning algorithms, and may assist in tailored risk stratification and management of these patients.
Identifiants
pubmed: 33524615
pii: S1109-9666(21)00007-5
doi: 10.1016/j.hjc.2021.01.007
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
339-348Informations de copyright
Copyright © 2021 Hellenic Society of Cardiology. Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of interest None declared.