Developing Approaches to Incorporate Donor Lung CT Images into Machine Learning Models to Predict Severe Primary Graft Dysfunction after Lung Transplantation.

Abstract

Primary graft dysfunction (PGD) is a common complication after lung transplantation associated with poor outcomes. Although risk factors have been identified, the complex interactions between clinical variables affecting PGD risk are not well understood, which can complicate decisions about donor lung acceptance. Previously, we developed a machine learning (ML) model to predict grade 3 PGD using donor and recipient electronic health record (EHR) data, but it lacked granular information from donor lung CT scans, which are routinely assessed during offer review. In this study, we used a gated approach to determine optimal methods for analyzing donor lung CT scans among patients receiving first-time, bilateral lung transplants at a single center over 10 years. We assessed four computer vision approaches and fused the best with EHR data at three points in the ML process. A total of 160 patients had donor-lung CT scans for analysis. The best imaging-only approach employed a 3D ResNet model, yielding a median (IQR) AUROC and AUPRC of 0.63 (0.49 - 0.72) and 0.48 (0.35 - 0.6), respectively. Combining imaging with clinical data using late fusion provided the highest performance, with a median AUROC and AUPRC of 0.74 (0.59 - 0.85) and 0.61 (0.47 - 0.72), respectively.