Prospective Multicenter Observational Cohort Study on Time to Death in Potential Controlled Donation After Circulatory Death Donors-Development and External Validation of Prediction Models: The DCD III Study.
Journal
Transplantation
ISSN: 1534-6080
Titre abrégé: Transplantation
Pays: United States
ID NLM: 0132144
Informations de publication
Date de publication:
01 09 2022
01 09 2022
Historique:
pubmed:
11
3
2022
medline:
27
8
2022
entrez:
10
3
2022
Statut:
ppublish
Résumé
Acceptance of organs from controlled donation after circulatory death (cDCD) donors depends on the time to circulatory death. Here we aimed to develop and externally validate prediction models for circulatory death within 1 or 2 h after withdrawal of life-sustaining treatment. In a multicenter, observational, prospective cohort study, we enrolled 409 potential cDCD donors. For model development, we applied the least absolute shrinkage and selection operator (LASSO) regression and machine learning-artificial intelligence analyses. Our LASSO models were validated using a previously published cDCD cohort. Additionally, we validated 3 existing prediction models using our data set. For death within 1 and 2 h, the area under the curves (AUCs) of the LASSO models were 0.77 and 0.79, respectively, whereas for the artificial intelligence models, these were 0.79 and 0.81, respectively. We were able to identify 4% to 16% of the patients who would not die within these time frames with 100% accuracy. External validation showed that the discrimination of our models was good (AUCs 0.80 and 0.82, respectively), but they were not able to identify a subgroup with certain death after 1 to 2 h. Using our cohort to validate 3 previously published models showed AUCs ranging between 0.63 and 0.74. Calibration demonstrated that the models over- and underestimated the predicted probability of death. Our models showed a reasonable ability to predict circulatory death. External validation of our and 3 existing models illustrated that their predictive ability remained relatively stable. We accurately predicted a subset of patients who died after 1 to 2 h, preventing starting unnecessary donation preparations, which, however, need external validation in a prospective cohort.
Sections du résumé
BACKGROUND
Acceptance of organs from controlled donation after circulatory death (cDCD) donors depends on the time to circulatory death. Here we aimed to develop and externally validate prediction models for circulatory death within 1 or 2 h after withdrawal of life-sustaining treatment.
METHODS
In a multicenter, observational, prospective cohort study, we enrolled 409 potential cDCD donors. For model development, we applied the least absolute shrinkage and selection operator (LASSO) regression and machine learning-artificial intelligence analyses. Our LASSO models were validated using a previously published cDCD cohort. Additionally, we validated 3 existing prediction models using our data set.
RESULTS
For death within 1 and 2 h, the area under the curves (AUCs) of the LASSO models were 0.77 and 0.79, respectively, whereas for the artificial intelligence models, these were 0.79 and 0.81, respectively. We were able to identify 4% to 16% of the patients who would not die within these time frames with 100% accuracy. External validation showed that the discrimination of our models was good (AUCs 0.80 and 0.82, respectively), but they were not able to identify a subgroup with certain death after 1 to 2 h. Using our cohort to validate 3 previously published models showed AUCs ranging between 0.63 and 0.74. Calibration demonstrated that the models over- and underestimated the predicted probability of death.
CONCLUSIONS
Our models showed a reasonable ability to predict circulatory death. External validation of our and 3 existing models illustrated that their predictive ability remained relatively stable. We accurately predicted a subset of patients who died after 1 to 2 h, preventing starting unnecessary donation preparations, which, however, need external validation in a prospective cohort.
Identifiants
pubmed: 35266926
doi: 10.1097/TP.0000000000004106
pii: 00007890-202209000-00027
doi:
Banques de données
ClinicalTrials.gov
['NCT04123275']
Types de publication
Journal Article
Multicenter Study
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1844-1851Informations de copyright
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare no funding or conflicts of interest.
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