Stratification Risk Analysis in Bridging Patients to Lung Transplant on ECMO: The STABLE Risk Score.
Journal
The Annals of thoracic surgery
ISSN: 1552-6259
Titre abrégé: Ann Thorac Surg
Pays: Netherlands
ID NLM: 15030100R
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
27
08
2019
revised:
26
02
2020
accepted:
23
03
2020
pubmed:
4
5
2020
medline:
11
11
2020
entrez:
4
5
2020
Statut:
ppublish
Résumé
No clinically validated tool exists to predict in-hospital mortality in patients requiring extracorporeal membrane oxygenation (ECMO) as a bridge to lung transplantation. We generated a quantitative risk assessment tool for these patients. Of 822 patients in the United Network for Organ Sharing (UNOS) database who required ECMO as bridge to lung transplant between 2004 and 2018, 630 were included in the analysis after exclusion for age <18 years, prior transplant, or treatment before 2004. Recipient-specific variables associated with posttransplant in-hospital mortality were incorporated into a multivariable logistic regression model in an automated stepwise fashion. Linear prediction was used to construct the Recipient Stratification Risk Analysis in Bridging Patients to Lung Transplant on ECMO (STABLE) score. K-fold cross-validation provided an unbiased estimate of out-of-sample performance. After further exclusion for University of Pennsylvania patients, the remaining cohort was used for external score validation. An iOS application was developed to aid clinical use. Six recipient-specific, pretransplant variables were translated into a 24-point score. STABLE scores in the United Network for Organ Sharing (UNOS) database ranged from 0 to 21, and each point increased the odds of in-hospital mortality by 22.0% (95% confidence interval, 1.14-1.29, P < .001). K-fold cross-validation yielded a receiver operating characteristic area under the curve of 86.2%. Validation of the STABLE score using our institutional database yielded an area under the curve of 89%. The STABLE score is a novel, internally cross-validated tool for risk stratification of patients on ECMO as a bridge to transplant. Its predictive power and accuracy may aid clinical decision-making and improve posttransplant outcomes.
Sections du résumé
BACKGROUND
No clinically validated tool exists to predict in-hospital mortality in patients requiring extracorporeal membrane oxygenation (ECMO) as a bridge to lung transplantation. We generated a quantitative risk assessment tool for these patients.
METHODS
Of 822 patients in the United Network for Organ Sharing (UNOS) database who required ECMO as bridge to lung transplant between 2004 and 2018, 630 were included in the analysis after exclusion for age <18 years, prior transplant, or treatment before 2004. Recipient-specific variables associated with posttransplant in-hospital mortality were incorporated into a multivariable logistic regression model in an automated stepwise fashion. Linear prediction was used to construct the Recipient Stratification Risk Analysis in Bridging Patients to Lung Transplant on ECMO (STABLE) score. K-fold cross-validation provided an unbiased estimate of out-of-sample performance. After further exclusion for University of Pennsylvania patients, the remaining cohort was used for external score validation. An iOS application was developed to aid clinical use.
RESULTS
Six recipient-specific, pretransplant variables were translated into a 24-point score. STABLE scores in the United Network for Organ Sharing (UNOS) database ranged from 0 to 21, and each point increased the odds of in-hospital mortality by 22.0% (95% confidence interval, 1.14-1.29, P < .001). K-fold cross-validation yielded a receiver operating characteristic area under the curve of 86.2%. Validation of the STABLE score using our institutional database yielded an area under the curve of 89%.
CONCLUSIONS
The STABLE score is a novel, internally cross-validated tool for risk stratification of patients on ECMO as a bridge to transplant. Its predictive power and accuracy may aid clinical decision-making and improve posttransplant outcomes.
Identifiants
pubmed: 32360877
pii: S0003-4975(20)30621-4
doi: 10.1016/j.athoracsur.2020.03.078
pii:
doi:
Types de publication
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1175-1184Informations de copyright
Copyright © 2020 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.