Machine Learning Using Multi-Site Cytokine Levels is Predictive of Primary Graft Dysfunction Following Lung Transplantation

Purpose: Primary graft dysfunction (PGD) is a syndrome of acute lung injury that occurs in approximately 30% of lung transplant recipients and predisposes to worse long-term outcomes. There is an urgent need to develop point-of-care methods to predict PGD post-transplant. To date, no human studies have investigated cytokine levels in donor lung perfusate, and no multivariable studies have used patient-matched biospecimens from multiple sites. We aimed to identify cytokines at multiple sites whose levels could predict PGD development.