Prediction of Incident Atrial Fibrillation in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort Study.
Age Factors
Aged
Atrial Fibrillation
/ blood
Biomarkers
/ blood
Female
Follow-Up Studies
Glomerular Filtration Rate
Humans
Likelihood Functions
Machine Learning
Male
Middle Aged
Natriuretic Peptide, Brain
/ blood
Peptide Fragments
/ blood
Race Factors
Renal Insufficiency, Chronic
/ blood
Risk Assessment
Risk Factors
Troponin T
/ blood
atrial fibrillation
cardiovascular disease
chronic kidney disease
clinical epidemiology
Journal
Clinical journal of the American Society of Nephrology : CJASN
ISSN: 1555-905X
Titre abrégé: Clin J Am Soc Nephrol
Pays: United States
ID NLM: 101271570
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
received:
21
01
2021
accepted:
28
04
2021
entrez:
1
10
2021
pubmed:
2
10
2021
medline:
3
3
2022
Statut:
ppublish
Résumé
Atrial fibrillation (AF) is common in CKD and associated with poor kidney and cardiovascular outcomes. Prediction models developed using novel methods may be useful to identify patients with CKD at highest risk of incident AF. We compared a previously published prediction model with models developed using machine learning methods in a CKD population. We studied 2766 participants in the Chronic Renal Insufficiency Cohort study without prior AF with complete cardiac biomarker (N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T) and clinical data. We evaluated the utility of machine learning methods as well as a previously validated clinical prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology [CHARGE]-AF, which included 11 predictors, using original and re-estimated coefficients) to predict incident AF. Discriminatory ability of each model was assessed using the ten-fold cross-validated Mean (SD) age of participants was 57 (11) years, 55% were men, 38% were Black, and mean (SD) eGFR was 45 (15) ml/min per 1.73 m Using machine learning algorithms, a model that included 12 standard clinical variables and cardiac-specific biomarkers N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T had moderate discrimination for incident AF in a CKD population.
Sections du résumé
BACKGROUND AND OBJECTIVES
Atrial fibrillation (AF) is common in CKD and associated with poor kidney and cardiovascular outcomes. Prediction models developed using novel methods may be useful to identify patients with CKD at highest risk of incident AF. We compared a previously published prediction model with models developed using machine learning methods in a CKD population.
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS
We studied 2766 participants in the Chronic Renal Insufficiency Cohort study without prior AF with complete cardiac biomarker (N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T) and clinical data. We evaluated the utility of machine learning methods as well as a previously validated clinical prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology [CHARGE]-AF, which included 11 predictors, using original and re-estimated coefficients) to predict incident AF. Discriminatory ability of each model was assessed using the ten-fold cross-validated
RESULTS
Mean (SD) age of participants was 57 (11) years, 55% were men, 38% were Black, and mean (SD) eGFR was 45 (15) ml/min per 1.73 m
CONCLUSIONS
Using machine learning algorithms, a model that included 12 standard clinical variables and cardiac-specific biomarkers N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T had moderate discrimination for incident AF in a CKD population.
Identifiants
pubmed: 34597264
pii: 01277230-202107000-00008
doi: 10.2215/CJN.01060121
pmc: PMC8425618
doi:
Substances chimiques
Biomarkers
0
Peptide Fragments
0
Troponin T
0
pro-brain natriuretic peptide (1-76)
0
Natriuretic Peptide, Brain
114471-18-0
Types de publication
Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1015-1024Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR002548
Pays : United States
Organisme : NIDDK NIH HHS
ID : U24 DK060990
Pays : United States
Organisme : NIDDK NIH HHS
ID : U01 DK060963
Pays : United States
Organisme : NCRR NIH HHS
ID : UL1 RR024131
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK103612
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR000003
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR000439
Pays : United States
Organisme : NCRR NIH HHS
ID : UL1 RR029879
Pays : United States
Organisme : NIDDK NIH HHS
ID : U01 DK061028
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR000433
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR000424
Pays : United States
Organisme : NCRR NIH HHS
ID : M01 RR016500
Pays : United States
Organisme : NIGMS NIH HHS
ID : P20 GM109036
Pays : United States
Organisme : NIDDK NIH HHS
ID : U01 DK060902
Pays : United States
Informations de copyright
Copyright © 2021 by the American Society of Nephrology.
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