Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
received:
04
10
2022
accepted:
28
03
2023
medline:
24
5
2023
pubmed:
12
5
2023
entrez:
11
5
2023
Statut:
ppublish
Résumé
Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and time from symptom onset. To improve diagnosis, we developed machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with clinical features and compute the Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) score (0-100) that corresponds to an individual's probability of myocardial infarction. The models were trained on data from 10,038 patients (48% women), and their performance was externally validated using data from 10,286 patients (35% women) from seven cohorts. CoDE-ACS had excellent discrimination for myocardial infarction (area under curve, 0.953; 95% confidence interval, 0.947-0.958), performed well across subgroups and identified more patients at presentation as low probability of having myocardial infarction than fixed cardiac troponin thresholds (61 versus 27%) with a similar negative predictive value and fewer as high probability of having myocardial infarction (10 versus 16%) with a greater positive predictive value. Patients identified as having a low probability of myocardial infarction had a lower rate of cardiac death than those with intermediate or high probability 30 days (0.1 versus 0.5 and 1.8%) and 1 year (0.3 versus 2.8 and 4.2%; P < 0.001 for both) from patient presentation. CoDE-ACS used as a clinical decision support system has the potential to reduce hospital admissions and have major benefits for patients and health care providers.
Identifiants
pubmed: 37169863
doi: 10.1038/s41591-023-02325-4
pii: 10.1038/s41591-023-02325-4
pmc: PMC10202804
doi:
Substances chimiques
Biomarkers
0
Troponin I
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1201-1210Subventions
Organisme : British Heart Foundation
ID : FS/18/25/33454
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/V007254/1
Pays : United Kingdom
Organisme : British Heart Foundation
ID : CH/F/21/90010
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RG/20/10/34966
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N013166/1
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RE/18/5/34216
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/W000598/1
Pays : United Kingdom
Investigateurs
A Mark Richards
(AM)
Chris Pemberton
(C)
Richard W Troughton
(RW)
Sally J Aldous
(SJ)
Anthony F T Brown
(AFT)
Emily Dalton
(E)
Chris Hammett
(C)
Tracey Hawkins
(T)
Shanen O'Kane
(S)
Kate Parke
(K)
Kimberley Ryan
(K)
Jessica Schluter
(J)
Karin Wild
(K)
Desiree Wussler
(D)
Òscar Miró
(Ò)
F Javier Martin-Sanchez
(FJ)
Dagmar I Keller
(DI)
Michael Christ
(M)
Andreas Buser
(A)
Maria Rubini Giménez
(MR)
Stephanie Barker
(S)
Jennifer Blades
(J)
Andrew R Chapman
(AR)
Takeshi Fujisawa
(T)
Dorien M Kimenai
(DM)
Jeremy Leung
(J)
Ziwen Li
(Z)
Michael McDermott
(M)
David E Newby
(DE)
Stacey D Schulberg
(SD)
Anoop S V Shah
(ASV)
Andrew Sorbie
(A)
Grace Soutar
(G)
Fiona E Strachan
(FE)
Caelan Taggart
(C)
Daniel Perez Vicencio
(DP)
Yiqing Wang
(Y)
Ryan Wereski
(R)
Kelly Williams
(K)
Christopher J Weir
(CJ)
Colin Berry
(C)
Alan Reid
(A)
Donogh Maguire
(D)
Paul O Collinson
(PO)
Yader Sandoval
(Y)
Stephen W Smith
(SW)
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2023. The Author(s).
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