Development and validation of primary graft dysfunction predictive algorithm for lung transplant candidates.

BACKGROUND: Primary graft dysfunction (PGD) is the leading cause of early morbidity and mortality after lung transplantation. Accurate prediction of PGD risk could inform donor approaches and perioperative care planning. We sought to develop a clinically useful, generalizable PGD prediction model to aid in transplant decision-making. METHODS: We derived a predictive model in a prospective cohort study of subjects from 2012 to 2018, followed by a single-center external validation. We used regularized (lasso) logistic regression to evaluate the predictive ability of clinically available PGD predictors and developed a user interface for clinical application. Using decision curve analysis, we quantified the net benefit of the model across a range of PGD risk thresholds and assessed model calibration and discrimination. RESULTS: The PGD predictive model included distance from donor hospital to recipient transplant center, recipient age, predicted total lung capacity, lung allocation score (LAS), body mass index, pulmonary artery mean pressure, sex, and indication for transplant; donor age, sex, mechanism of death, and donor smoking status; and interaction terms for LAS and donor distance. The interface allows for real-time assessment of PGD risk for any donor/recipient combination. The model offers decision-making net benefit in the PGD risk range of 10% to 75% in the derivation centers and 2% to 10% in the validation cohort, a range incorporating the incidence in that cohort. CONCLUSION: We developed a clinically useful PGD predictive algorithm across a range of PGD risk thresholds to support transplant decision-making, posttransplant care, and enrich samples for PGD treatment trials.

The Impact of Donor Smoking on Primary Graft Dysfunction and Mortality after Lung Transplantation.

Rationale: Primary graft dysfunction (PGD) is the leading cause of early morbidity and mortality after lung transplantation. Prior studies implicated proxy-defined donor smoking as a risk factor for PGD and mortality. Objectives: We aimed to more accurately assess the impact of donor smoke exposure on PGD and mortality using quantitative smoke exposure biomarkers. Methods: We performed a multicenter prospective cohort study of lung transplant recipients enrolled in the Lung Transplant Outcomes Group cohort between 2012 and 2018. PGD was defined as grade 3 at 48 or 72 hours after lung reperfusion. Donor smoking was defined using accepted thresholds of urinary biomarkers of nicotine exposure (cotinine) and tobacco-specific nitrosamine (4-[methylnitrosamino]-1-[3-pyridyl]-1-butanol [NNAL]) in addition to clinical history. The donor smoking-PGD association was assessed using logistic regression, and survival analysis was performed using inverse probability of exposure weighting according to smoking category. Measurements and Main Results: Active donor smoking prevalence varied by definition, with 34-43% based on urinary cotinine, 28% by urinary NNAL, and 37% by clinical documentation. The standardized risk of PGD associated with active donor smoking was higher across all definitions, with an absolute risk increase of 11.5% (95% confidence interval [CI], 3.8% to 19.2%) by urinary cotinine, 5.7% (95% CI, -3.4% to 14.9%) by urinary NNAL, and 6.5% (95% CI, -2.8% to 15.8%) defined clinically. Donor smoking was not associated with differential post-lung transplant survival using any definition. Conclusions: Donor smoking associates with a modest increase in PGD risk but not with increased recipient mortality. Use of lungs from smokers is likely safe and may increase lung donor availability. Clinical trial registered with www.clinicaltrials.gov (NCT00552357).