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

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.