Predicting Donor Lung Acceptance for Transplant During Ex Vivo Lung Perfusion: the EX Vivo Lung PerfusIon Prediction (EXPIRE).

Ex vivo lung perfusion (EVLP) has being increasingly used for the pretransplant assessment of extended‐criteria donor lungs. Mathematical models to predict lung acceptance during EVLP have not been reported so far. Thus, we hypothesized that predictors of lung acceptance could be identified and used to develop a mathematical model describing the clinical decision‐making process used in our institution. Donor lungs characteristics and EVLP physiologic parameters included in our EVLP registry were examined (derivation cohort). Multivariable logistic regression analysis was performed to identify predictors independently associated with lung acceptance. A mathematical model (EX vivo lung PerfusIon pREdiction [EXPIRE] model) for each hour of EVLP was developed and validated using a new cohort (validation cohort). Two hundred eighty donor lungs were assessed with EVLP. Of these, 186 (66%) were accepted for transplantation. ΔPO2 and static compliance/total lung capacity were identified as independent predictors of lung acceptance and their respective cut‐off values were determined. The EXPIRE model showed a low discriminative power at the first hour of EVLP assessment (AUC: 0.69 [95% CI: 0.62–0.77]), which progressively improved up to the fourth hour (AUC: 0.87 [95% CI: 0.83–0.92]). In a validation cohort, the EXPIRE model demonstrated good discriminative power, peaking at the fourth hour (AUC: 0.85 [95% CI: 0.76–0.94]). The EXPIRE model may help to standardize lung assessment in centers using the Toronto EVLP technique and improve overall transplant rates.