Background: Cardiac allograft vasculopathy (CAV) is a leading cause of morbidity and mortality for heart transplant recipients. Although clinical risk factors for CAV have been established, no personalized prognostic test exists to confidently identify patients at high versus low risk of developing aggressive CAV. This investigation aimed to leverage computational methods for analyzing digital pathology images from routine endomyocardial biopsies (EMBs) to develop a precision medicine tool for predicting CAV years before overt clinical presentation.
Methods: Clinical data from 1 year after transplant were collected on 302 transplant recipients from the University of Pennsylvania, including 53 patients with early-onset CAV and 249 no early-onset CAV controls. These data were used to generate a clinical model (Clinical Risk Factor Future Cardiac Allograft Vasculopathy Prediction Model [ClinCAV-Pr]) for predicting future CAV development. From this cohort, 183 archived EMBs were collected for CD31 and modified trichrome staining and then digitally scanned. These included 1-year posttransplant EMBs from 50 patients with early-onset CAV and 82 patients with no early-onset CAV, as well as 51 EMBs from disease control patients obtained at the time of definitive coronary angiography confirming CAV. Using biologically inspired, handcrafted features extracted from digitized EMBs, quantitative histological models for differentiating no early-onset CAV from disease controls (Histological Cardiac Allograft Vasculopathy Diagnostic Model [HistoCAV-Dx]) and for predicting future CAV from 1-year posttransplant EMBs were developed (Histological Future Cardiac Allograft Vasculopathy Prediction Model [HistoCAV-Pr]). The performance of histological and clinical models for predicting future CAV (ie, HistoCAV-Pr and ClinCAV-Pr, respectively) were compared in a held-out validation set before being combined to assess the added predictive value of an integrated predictive model (Integrated Histological/Clinical Risk Factor Future Cardiac Allograft Vasculopathy Prediction Model [iCAV-Pr]).
Results: ClinCAV-Pr achieved modest performance on the independent test set, with an area under the receiver operating curve (AUROC) of 0.70. The HistoCAV-Dx model for diagnosing CAV achieved excellent discrimination, with an AUROC of 0.91, whereas the HistoCAV-Pr model for predicting CAV achieved good performance with an AUROC of 0.80. The integrated iCAV-Pr model achieved excellent predictive performance, with an AUROC of 0.93 on the held-out test set.
Conclusions: Prediction of future CAV development is greatly improved by incorporation of computationally extracted histological features. These results suggest morphological details contained within regularly obtained biopsy tissue have the potential to enhance precision and personalization of treatment plans for patients after heart transplant.
Keywords: allografts; cardiac transplantation; computer vision systems; coronary arteries; graft rejection; histology; machine learning.