Biomarker profiling is being rapidly incorporated in many areas of modern medical practice to improve the precision of clinical decision-making. This potential improvement, however, has not been transferred to the practice of organ assessment and transplantation because previously developed gene-profiling techniques require an extended period of time to perform, making them unsuitable in the time-sensitive organ assessment process. We sought to develop a novel class of chip-based sensors that would enable rapid analysis of tissue levels of preimplantation mRNA markers that correlate with the development of primary graft dysfunction (PGD) in recipients after transplant. Using fractal circuit sensors (FraCS), three-dimensional metal structures with large surface areas, we were able to rapidly (<20 min) and reproducibly quantify small differences in the expression of interleukin-6 (IL-6), IL-10, and ATP11B mRNA in donor lung biopsies. A proof-of-concept study using 52 human donor lungs was performed to develop a model that was used to predict, with excellent sensitivity (74%) and specificity (91%), the incidence of PGD for a donor lung. Thus, the FraCS-based approach delivers a key predictive value test that could be applied to enhance transplant patient outcomes. This work provides an important step toward bringing rapid diagnostic mRNA profiling to clinical application in lung transplantation.