Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis.
Journal
The Lancet. Digital health
ISSN: 2589-7500
Titre abrégé: Lancet Digit Health
Pays: England
ID NLM: 101751302
Informations de publication
Date de publication:
05 2022
05 2022
Historique:
received:
22
07
2021
revised:
06
12
2021
accepted:
01
02
2022
entrez:
24
4
2022
pubmed:
25
4
2022
medline:
27
4
2022
Statut:
ppublish
Résumé
Diagnostic pathways for myocardial infarction rely on fixed troponin thresholds, which do not recognise that troponin varies by age, sex, and time within individuals. To overcome this limitation, we recently introduced a machine learning algorithm that predicts the likelihood of myocardial infarction. Our aim was to evaluate whether this algorithm performs well in routine clinical practice and predicts subsequent events. The myocardial-ischaemic-injury-index (MI In total, 20 761 patients (64 years [SD 16], 9597 [46%] women) enrolled between June 10, 2013, and March 3, 2016, were included from the High-STEACS trial cohort, of whom 3272 (15·8%) had myocardial infarction. MI In consecutive patients undergoing serial cardiac troponin measurement for suspected acute coronary syndrome, the MI Medical Research Council, British Heart Foundation, National Institute for Health Research, and NHSX.
Sections du résumé
BACKGROUND
Diagnostic pathways for myocardial infarction rely on fixed troponin thresholds, which do not recognise that troponin varies by age, sex, and time within individuals. To overcome this limitation, we recently introduced a machine learning algorithm that predicts the likelihood of myocardial infarction. Our aim was to evaluate whether this algorithm performs well in routine clinical practice and predicts subsequent events.
METHODS
The myocardial-ischaemic-injury-index (MI
FINDINGS
In total, 20 761 patients (64 years [SD 16], 9597 [46%] women) enrolled between June 10, 2013, and March 3, 2016, were included from the High-STEACS trial cohort, of whom 3272 (15·8%) had myocardial infarction. MI
INTERPRETATION
In consecutive patients undergoing serial cardiac troponin measurement for suspected acute coronary syndrome, the MI
FUNDING
Medical Research Council, British Heart Foundation, National Institute for Health Research, and NHSX.
Identifiants
pubmed: 35461689
pii: S2589-7500(22)00025-5
doi: 10.1016/S2589-7500(22)00025-5
pmc: PMC9052331
pii:
doi:
Substances chimiques
Biomarkers
0
Troponin I
0
Banques de données
ClinicalTrials.gov
['NCT01852123']
Types de publication
Journal Article
Multicenter Study
Randomized Controlled Trial
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e300-e308Subventions
Organisme : British Heart Foundation
ID : FS/CRTF/21/24273
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 : Medical Research Council
ID : MR/V007017/1
Pays : United Kingdom
Organisme : British Heart Foundation
ID : FS/18/25/33454
Pays : United Kingdom
Organisme : British Heart Foundation
ID : BCDSA/100003
Pays : United Kingdom
Organisme : British Heart Foundation
ID : SP/12/10/29922
Pays : United Kingdom
Organisme : British Heart Foundation
ID : CH/09/002/26360
Pays : United Kingdom
Organisme : British Heart Foundation
ID : RE/18/5/34216
Pays : United Kingdom
Investigateurs
Nicholas L Mills
(NL)
Fiona E Strachan
(FE)
Christopher Tuck
(C)
Anoop Sv Shah
(AS)
Atul Anand
(A)
Andrew R Chapman
(AR)
Amy V Ferry
(AV)
Kuan Ken Lee
(KK)
Dimitrios Doudesis
(D)
Anda Bularga
(A)
Ryan Wereski
(R)
Caelan Taggart
(C)
Matthew Th Lowry
(MT)
Filip Mendusic
(F)
Dorien M Kimenai
(DM)
Dennis Sandeman
(D)
Philip D Adamson
(PD)
Catherine L Stables
(CL)
Catalina A Vallejos
(CA)
Athanasios Tsanas
(A)
Lucy Marshall
(L)
Stacey D Stewart
(SD)
Takeshi Fujisawa
(T)
Mischa Hautvast
(M)
Jean McPherson
(J)
Lynn McKinlay
(L)
Ian Ford
(I)
David E Newby
(DE)
Keith Aa Fox
(KA)
Colin Berry
(C)
Simon Walker
(S)
Christopher J Weir
(CJ)
Alasdair Gray
(A)
Paul O Collinson
(PO)
Fred S Apple
(FS)
Alan Reid
(A)
Anne Cruikshank
(A)
Iain Findlay
(I)
Shannon Amoils
(S)
David A McAllister
(DA)
Donogh Maguire
(D)
Jennifer Stevens
(J)
John Norrie
(J)
Jack Pm Andrews
(JP)
Alastair Moss
(A)
Mohamed S Anwar
(MS)
John Hung
(J)
Jonathan Malo
(J)
Colin Fischbacher
(C)
Bernard L Croal
(BL)
Stephen J Leslie
(SJ)
Catriona Keerie
(C)
Richard A Parker
(RA)
Allan Walker
(A)
Ronnie Harkess
(R)
Tony Wackett
(T)
Roma Armstrong
(R)
Laura Stirling
(L)
Claire MacDonald
(C)
Imran Sadat
(I)
Frank Finlay
(F)
Heather Charles
(H)
Pamela Linksted
(P)
Stephen Young
(S)
Bill Alexander
(B)
Chris Duncan
(C)
Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests NLM has received honoraria or consultancy from Abbott Diagnostics, Roche Diagnostics, Siemens Healthineers, and LumiraDx. KKL has received honoraria from Abbott Diagnostics. ASVS's institution (the University of Edinburgh) has received speaker fees from Abbott Diagnostics. JWP has undertaken consultancy for Abbott Diagnostics. MPT has received consulting fees, honoraria, or payment from Abbott, Roche, and Siemens; funding for clinical research from Radiometer; and participated on a data safety monitoring board or an advisory board for Abbott, Radiometer, Roche, and Siemens. All other authors declare no competing interests.
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