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Photo of Mattias Ohlsson

Mattias Ohlsson

Professor

Photo of Mattias Ohlsson

A machine learning model for prediction of 30-day primary graft failure after heart transplantation

Author

  • Björn Linse
  • Mattias Ohlsson
  • Joseph Stehlik
  • Lars H. Lund
  • Bodil Andersson
  • Johan Nilsson

Summary, in English

Background: Primary graft failure (PGF) remains the most common cause of short-term mortality after heart transplantation. The main objective was to develop and validate a risk model for prediction of short-term mortality due to PGF after heart transplantation using the ISHLT Heart Transplant Registry. Methods: We developed a non-linear artificial neural networks (ANN) model to evaluate the association between recipient-donor variables and post-transplant PGF. Patients in the ISHLT registry were randomly divided into derivation and an independent internal validation cohort. The primary endpoint was PGF defined as death within 30 days due to Graft failure or Cardiovascular causes or retransplant within 30 days for causes other than rejection. Results: Among 64,964 adult recipients transplanted between 1994 and 2013, mean age was 51 years and 22% were female. The incidence of PGF up to 30 days was 3.7%. The ANN model selected 33 of 77 risk variables as relevant for PGF prediction. The C-index in the test cohort was 0.70 (95% CI: 0.68-0.71). The risk variables which most influenced the PGF were underlying HF diagnosis, ischemia time and sex, while renal function had a lower influence. Conclusion: An ANN model to predict primary graft dysfunction was derived and independently validated. The good discrimination of the ANN model likely results from its flexibility to model potentially non-linear relationships and interactions. Whether this model with improved discrimination can assist in clinical decisions at the time of transplant should be tested.

Department/s

  • Computational Biology and Biological Physics - Undergoing reorganization
  • LUCC: Lund University Cancer Centre
  • eSSENCE: The e-Science Collaboration
  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • Hepato-Pancreato-Biliary Surgery
  • Surgery (Lund)
  • Thoracic Surgery
  • Heparin bindning protein in cardiothoracic surgery
  • Artificial Intelligence and Bioinformatics in Cardiothoracic Sciences (AIBCTS)
  • Heart and Lung transplantation

Publishing year

2023-03

Language

English

Publication/Series

Heliyon

Volume

9

Issue

3

Document type

Journal article

Publisher

Elsevier

Topic

  • Surgery

Keywords

  • Artificial neural network
  • Heart transplantation
  • Prediction
  • Primary graft failure

Status

Published

Research group

  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • Hepato-Pancreato-Biliary Surgery
  • Heparin bindning protein in cardiothoracic surgery
  • Artificial Intelligence and Bioinformatics in Cardiothoracic Sciences (AIBCTS)
  • Heart and Lung transplantation

ISBN/ISSN/Other

  • ISSN: 2405-8440