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There is an urgent need to better understand the pathophysiology of primary graft dysfunction (PGD) to develop point-of-care methods predicting those at risk. We utilized a multiplex multivariable approach to define cytokines, chemokines, and growth factors in patient-matched biospecimens to identify factors predictive of PGD. Biospecimens were collected from patients undergoing bilateral lung transplantation (LTx) from three sites: donor lung perfusate, post-transplant bronchoalveolar lavage (BAL) fluid (2h), and plasma (2h, 24h, 72h, and 1 and 2 wks). A 71-multiplex panel was performed on each. Cross-validated logistic regression (LR) and random forest (RF) models determined whether analytes from each site, alone or combined with clinical data, discriminated PGD grade 0 (n = 9) vs. 3 (n = 8). BAL fluid at 2h was most predictive of PGD (LR, 0.825; RF, 0.919), followed by multi-timepoint plasma (LR, 0.841; RF, 0.653), then perfusate (LR, 0.565; RF, 0.448). Combined clinical, BAL, and plasma data yielded the strongest performance (LR, 1.000; RF, 1.000). BAL collected 2h post-transplant showed the strongest discriminatory signal for severe PGD in this exploratory cohort. This integrative approach identified IL-1RA, BCA-1, and Fractalkine as hypothesis-generating candidate biomarkers that warrant validation in larger independent studies.