Abstract
Lung transplantation is the gold standard for a carefully selected patient population with end-stage lung disease. This study sought to create a risk stratification model using only preoperative recipient data to predict 1-year postoperative mortality during the pretransplant assessment.
Data of lung transplant recipients at Houston Methodist Hospital in Houston, Texas from January 2009 to December 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 after transplantation were assigned weights on the basis of the beta coefficients, and risk scores were derived. Patients were stratified into low-, medium- and high-risk categories. The model was validated using the validation data set and data from other US transplant centers in the UNOS database.
The study randomized 633 lung recipients from Houston Methodist Hospital into development (n = 317 patients) and validation cohorts (n = 316). The 1-year survival after transplantation was significantly different among risk groups: 95% (low risk), 84% (medium risk), and 72% (high risk) (P < .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 < .001). Validation of the model with the UNOS data set included 9920 patients and found 1-year survival to be 91%, 86%, and 82%, respectively (P < .001).
Using only recipient data collected at the time of the prelisting evaluation, this simple scoring system was found to have good discrimination power and could be a practical tool in the assessment and selection of potential lung transplant recipients.
