Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation

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de Capretz, Pontus OlssonLund University,Lunds universitet,Akutsjukvård,Forskargrupper vid Lunds universitet,Emergency medicine,Lund University Research Groups,Skåne University Hospital (författare)

Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation

Artikel/kapitelEngelska2023

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2023-02-02
London :BioMed Central (BMC),2023
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Nummerbeteckningar

LIBRIS-ID:oai:DiVA.org:hh-49965
https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-49965URI
https://doi.org/10.1186/s12911-023-02119-1DOI
https://lup.lub.lu.se/record/ed5bee3a-1094-4b93-a02d-ef6fce22c41fURI

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Språk:engelska
Sammanfattning på:engelska

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Open access funding provided by Lund University. This work was supported by an ALF research grant at Skåne University Hospital and by a grant from Region Skåne. This study was part of the AIR Lund (Artificially Intelligent use of Registers at Lund University) research environment and received funding from the Swedish Research Council [VR; Grant No. 2019-00198] and Sweden’s innovation agency [Vinnova; Grant No. 2018-01942]. In addition, the study was funded by the Swedish Heart-Lung Foundation [2018-0173]. There was no industry involvement. Funding organizations had no role in the planning, design or realisation of the study, collection, analysis or interpretation of data, or preparation, review or approval of the manuscript. The authors do hereby declare that all illustrations and figures in the manuscript are entirely original and do not require reprint permission.
Aims: In the present study, we aimed to evaluate the performance of machine learning (ML) models for identification of acute myocardial infarction (AMI) or death within 30 days among emergency department (ED) chest pain patients. Methods and results: Using data from 9519 consecutive ED chest pain patients, we created ML models based on logistic regression or artificial neural networks. Model inputs included sex, age, ECG and the first blood tests at patient presentation: High sensitivity TnT (hs-cTnT), glucose, creatinine, and hemoglobin. For a safe rule-out, the models were adapted to achieve a sensitivity > 99% and a negative predictive value (NPV) > 99.5% for 30-day AMI/death. For rule-in, we set the models to achieve a specificity > 90% and a positive predictive value (PPV) of > 70%. The models were also compared with the 0 h arm of the European Society of Cardiology algorithm (ESC 0 h); An initial hs-cTnT < 5 ng/L for rule-out and ≥ 52 ng/L for rule-in. A convolutional neural network was the best model and identified 55% of the patients for rule-out and 5.3% for rule-in, while maintaining the required sensitivity, specificity, NPV and PPV levels. ESC 0 h failed to reach these performance levels. Discussion: An ML model based on age, sex, ECG and blood tests at ED arrival can identify six out of ten chest pain patients for safe early rule-out or rule-in with no need for serial blood tests. Future studies should attempt to improve these ML models further, e.g. by including additional input data. © 2023, The Author(s).

Ämnesord och genrebeteckningar

MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Kardiologi hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Cardiac and Cardiovascular Systems hsv//eng
Acute myocardial infarction
Chest pain
Deep learning
Emergency department
High-sensitivity troponin
Machine learning
Acute myocardial infarction
Chest pain
Deep learning
Emergency department
High-sensitivity troponin
Machine learning

Biuppslag (personer, institutioner, konferenser, titlar ...)

Björkelund, AndersLund University,Lunds universitet,Centrum för miljö- och klimatvetenskap (CEC),Naturvetenskapliga fakulteten,Beräkningsbiologi och biologisk fysik - Har omorganiserats,Institutionen för astronomi och teoretisk fysik - Har omorganiserats,Beräkningsvetenskap för hälsa och miljö,Forskargrupper vid Lunds universitet,Centre for Environmental and Climate Science (CEC),Faculty of Science,Computational Biology and Biological Physics - Has been reorganised,Department of Astronomy and Theoretical Physics - Has been reorganised,Computational Science for Health and Environment,Lund University Research Groups(Swepub:lu)an4661bj (författare)
Björk, JonasLund University,Lunds universitet,EPI@LUND,Forskargrupper vid Lunds universitet,Lund University Research Groups,Skåne University Hospital(Swepub:lu)ymed-jbj (författare)
Ohlsson, Mattias,1967-Halmstad University,Lunds universitet,Högskolan i Halmstad,Akademin för informationsteknologi,Lund University, Lund, Sweden,LU profilområde: Naturlig och artificiell kognition,Lunds universitets profilområden,Astrofysik,Fysiska institutionen,Institutioner vid LTH,Lunds Tekniska Högskola,Beräkningsvetenskap för hälsa och miljö,Forskargrupper vid Lunds universitet,LU Profile Area: Natural and Artificial Cognition,Lund University Profile areas,Astrophysics,Department of Physics,Departments at LTH,Faculty of Engineering, LTH,Computational Science for Health and Environment,Lund University Research Groups(Swepub:lu)thep-moh (författare)
Mokhtari, ArashLund University,Lunds universitet,NPWT teknologin,Forskargrupper vid Lunds universitet,Skonsammare hjärtkirurgi,NPWT technology,Lund University Research Groups,Less invasive cardiac surgery,Skåne University Hospital(Swepub:lu)thor-ami (författare)
Nyström, AxelLund University,Lunds universitet,Avdelningen för arbets- och miljömedicin,Institutionen för laboratoriemedicin,Medicinska fakulteten,EPI@LUND,Forskargrupper vid Lunds universitet,Beräkningsvetenskap för hälsa och miljö,Division of Occupational and Environmental Medicine, Lund University,Department of Laboratory Medicine,Faculty of Medicine,Lund University Research Groups,Computational Science for Health and Environment(Swepub:lu)ax1431ny (författare)
Ekelund, UlfLund University,Lunds universitet,Akutsjukvård,Forskargrupper vid Lunds universitet,Emergency medicine,Lund University Research Groups,Skåne University Hospital(Swepub:lu)mphy-uek (författare)
AkutsjukvårdForskargrupper vid Lunds universitet (creator_code:org_t)

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Ingår i:BMC Medical Informatics and Decision MakingLondon : BioMed Central (BMC)23:1, s. 1-101472-6947

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Av författaren/redakt...: de Capretz, Pont ...; Björkelund, Ande ...; Björk, Jonas; Ohlsson, Mattias ...; Mokhtari, Arash; Nyström, Axel; visa fler...; Ekelund, Ulf; visa färre...

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