Development and validation of primary graft dysfunction predictive algorithm for lung transplant candidates.
donor
lung transplantation
prediction
primary graft dysfunction
recipient
Journal
The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
ISSN: 1557-3117
Titre abrégé: J Heart Lung Transplant
Pays: United States
ID NLM: 9102703
Informations de publication
Date de publication:
Apr 2024
Apr 2024
Historique:
received:
14
03
2023
revised:
05
11
2023
accepted:
30
11
2023
medline:
18
3
2024
pubmed:
9
12
2023
entrez:
8
12
2023
Statut:
ppublish
Résumé
Primary graft dysfunction (PGD) is the leading cause of early morbidity and mortality after lung transplantation. Accurate prediction of PGD risk could inform donor approaches and perioperative care planning. We sought to develop a clinically useful, generalizable PGD prediction model to aid in transplant decision-making. We derived a predictive model in a prospective cohort study of subjects from 2012 to 2018, followed by a single-center external validation. We used regularized (lasso) logistic regression to evaluate the predictive ability of clinically available PGD predictors and developed a user interface for clinical application. Using decision curve analysis, we quantified the net benefit of the model across a range of PGD risk thresholds and assessed model calibration and discrimination. The PGD predictive model included distance from donor hospital to recipient transplant center, recipient age, predicted total lung capacity, lung allocation score (LAS), body mass index, pulmonary artery mean pressure, sex, and indication for transplant; donor age, sex, mechanism of death, and donor smoking status; and interaction terms for LAS and donor distance. The interface allows for real-time assessment of PGD risk for any donor/recipient combination. The model offers decision-making net benefit in the PGD risk range of 10% to 75% in the derivation centers and 2% to 10% in the validation cohort, a range incorporating the incidence in that cohort. We developed a clinically useful PGD predictive algorithm across a range of PGD risk thresholds to support transplant decision-making, posttransplant care, and enrich samples for PGD treatment trials.
Sections du résumé
BACKGROUND
BACKGROUND
Primary graft dysfunction (PGD) is the leading cause of early morbidity and mortality after lung transplantation. Accurate prediction of PGD risk could inform donor approaches and perioperative care planning. We sought to develop a clinically useful, generalizable PGD prediction model to aid in transplant decision-making.
METHODS
METHODS
We derived a predictive model in a prospective cohort study of subjects from 2012 to 2018, followed by a single-center external validation. We used regularized (lasso) logistic regression to evaluate the predictive ability of clinically available PGD predictors and developed a user interface for clinical application. Using decision curve analysis, we quantified the net benefit of the model across a range of PGD risk thresholds and assessed model calibration and discrimination.
RESULTS
RESULTS
The PGD predictive model included distance from donor hospital to recipient transplant center, recipient age, predicted total lung capacity, lung allocation score (LAS), body mass index, pulmonary artery mean pressure, sex, and indication for transplant; donor age, sex, mechanism of death, and donor smoking status; and interaction terms for LAS and donor distance. The interface allows for real-time assessment of PGD risk for any donor/recipient combination. The model offers decision-making net benefit in the PGD risk range of 10% to 75% in the derivation centers and 2% to 10% in the validation cohort, a range incorporating the incidence in that cohort.
CONCLUSION
CONCLUSIONS
We developed a clinically useful PGD predictive algorithm across a range of PGD risk thresholds to support transplant decision-making, posttransplant care, and enrich samples for PGD treatment trials.
Identifiants
pubmed: 38065239
pii: S1053-2498(23)02157-5
doi: 10.1016/j.healun.2023.11.019
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
633-641Informations de copyright
Copyright © 2023 International Society for the Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.