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Title: The Houston Methodist lung transplant risk model – a validated tool for pre-transplant risk assessment
BACKGROUND: Lung transplantation is the gold standard for a carefully selected patient population with end-stage lung disease. We sought to create a unique risk stratification model using only preoperative recipient data to predict one-year postoperative mortality during our pre-transplant assessment. METHODS: Data of lung transplant recipients at Houston Methodist Hospital (HMH) from 1/2009 to 12/2014 were extracted from the United Network for Organ Sharing (UNOS) database. Patients were randomly divided into development and validation cohorts. Cox proportional-hazards models were conducted. Variables associated with 1-year mortality post-transplant were assigned weights based on the beta coefficients, and risk scores were derived. Patients were stratified into low-, medium- and high-risk categories. Our model was validated using the validation dataset and data from other US transplant centers in the UNOS database RESULTS: We randomized 633 lung recipients from HMH into the development (n=317 patients) and validation cohort (n=316). One-year survival after transplant was significantly different among risk groups: 95% (low-risk), 84% (medium-risk), and 72% (high-risk) (p<0.001) with a C-statistic of 0.74. Patient survival in the validation cohort was also significantly different among risk groups (85%, 77% and 65%, respectively, p<0.001). Validation of the model with the UNOS dataset included 9,920 patients and found 1-year survival to be more » 91%, 86% and 82%, respectively (p < 0.001). CONCLUSIONS: Using only recipient data collected at the time of pre-listing evaluation, our simple scoring system has good discrimination power and can be a practical tool in the assessment and selection of potential lung transplant recipients. « less
Authors:
Award ID(s):
1826144
Publication Date:
NSF-PAR ID:
10110737
Journal Name:
The annals of thoracic surgery
ISSN:
0003-4975
Sponsoring Org:
National Science Foundation
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