Prediction of survival after a lung transplant at 1 year (SALTO cohort) using information available at different key time points

Abstract

Abstract OBJECTIVES A lung transplant is the final treatment option for end-stage lung disease. We evaluated the individual risk of 1-year mortality at each stage of the lung transplant process. METHODS This study was a retrospective analysis of patients undergoing bilateral lung transplants between January 2014 and December 2019 in 3 French academic centres. Patients were randomly divided into development and validation cohorts. Three multivariable logistic regression models of 1-year mortality were applied (i) at recipient registration, (ii) the graft allocation and (iii) after the operation. The 1-year mortality was predicted for individual patients assigned to 3 risk groups at time points A to C. RESULTS The study population consisted of 478 patients with a mean (standard deviation) age of 49.0 (14.3) years. The 1-year mortality rate was 23.0%. There were no significant differences in patient characteristics between the development (n = 319) and validation (n = 159) cohorts. The models analysed recipient, donor and intraoperative variables. The discriminatory power (area under the receiver operating characteristic curve) was 0.67 (0.62–0.73), 0.70 (0.63–0.77) and 0.82 (0.77–0.88), respectively, in the development cohort and 0.74 (0.64–0.85), 0.76 (0.66–0.86) and 0.87 (0.79 – 0.95), respectively, in the validation cohort. Survival rates were significantly different among the low- (< 15%), intermediate- (15%–45%) and high-risk (> 45%) groups in both cohorts. CONCLUSIONS Risk prediction models allow estimation of the 1-year mortality risk of individual patients during the lung transplant process. These models may help caregivers identify high-risk patients at times A to C and reduce the risk at subsequent time points.