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

Linse, Björn LU ; Ohlsson, Mattias LU orcid ; Stehlik, Joseph ; Lund, Lars H. ; Andersson, Bodil LU orcid and Nilsson, Johan LU orcid (2023) In Heliyon 9(3).
Abstract

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... (More)

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.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial neural network, Heart transplantation, Prediction, Primary graft failure
in
Heliyon
volume
9
issue
3
article number
e14282
publisher
Elsevier
external identifiers
  • scopus:85149794947
  • pmid:36938431
ISSN
2405-8440
DOI
10.1016/j.heliyon.2023.e14282
language
English
LU publication?
yes
id
d3441484-5141-4c13-8bdd-b25b4475999d
date added to LUP
2023-06-09 13:15:35
date last changed
2024-06-15 03:51:03
@article{d3441484-5141-4c13-8bdd-b25b4475999d,
  abstract     = {{<p>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.</p>}},
  author       = {{Linse, Björn and Ohlsson, Mattias and Stehlik, Joseph and Lund, Lars H. and Andersson, Bodil and Nilsson, Johan}},
  issn         = {{2405-8440}},
  keywords     = {{Artificial neural network; Heart transplantation; Prediction; Primary graft failure}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{Elsevier}},
  series       = {{Heliyon}},
  title        = {{A machine learning model for prediction of 30-day primary graft failure after heart transplantation}},
  url          = {{http://dx.doi.org/10.1016/j.heliyon.2023.e14282}},
  doi          = {{10.1016/j.heliyon.2023.e14282}},
  volume       = {{9}},
  year         = {{2023}},
}