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Anders Björkelund

Forskare

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

Författare

  • Pontus Olsson de Capretz
  • Anders Björkelund
  • Jonas Björk
  • Mattias Ohlsson
  • Arash Mokhtari
  • Axel Nyström
  • Ulf Ekelund

Summary, in English

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.

Avdelning/ar

  • Akutsjukvård
  • Beräkningsvetenskap för hälsa och miljö
  • Centrum för miljö- och klimatvetenskap (CEC)
  • Beräkningsbiologi och biologisk fysik - Har omorganiserats
  • eSSENCE: The e-Science Collaboration
  • EpiHealth: Epidemiology for Health
  • EPI@LUND
  • LU profilområde: Naturlig och artificiell kognition
  • Astrofysik
  • NPWT teknologin
  • Skonsammare hjärtkirurgi
  • Avdelningen för arbets- och miljömedicin

Publiceringsår

2023-12

Språk

Engelska

Publikation/Tidskrift/Serie

BMC Medical Informatics and Decision Making

Volym

23

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

BioMed Central (BMC)

Ämne

  • Cardiac and Cardiovascular Systems

Nyckelord

  • Acute myocardial infarction
  • Chest pain
  • Deep learning
  • Emergency department
  • High-sensitivity troponin
  • Machine learning

Aktiv

Published

Projekt

  • AIR Lund - Artificially Intelligent use of Registers

Forskningsgrupp

  • Emergency medicine
  • Computational Science for Health and Environment
  • EPI@LUND
  • NPWT technology
  • Less invasive cardiac surgery

ISBN/ISSN/Övrigt

  • ISSN: 1472-6947