Citations are widely used for measuring scientific impact. The goal of the present study was to predict citation counts of manuscripts submitted to Transplant International (TI) in the two calendar years following publication. We considered a comprehensive set of 21 manuscript, author, and peerreviewrelated predictor variables available early in the peerreview process. We also evaluated how successfully the peerreview process at TI identified and accepted the most promising manuscripts for publication. A developed predictive model with nine selected variables showed acceptable test performance to identify often cited articles (AUROC = 0.685). Particularly important predictors were the number of pages, month of publication, publication type (review versus other), and study on humans (yes versus no). Accepted manuscripts at TI were cited more often than rejected but elsewhere published manuscripts (median 4 vs. 2 citations). The predictive model did not outperform the actual editorial decision. Both findings suggest that the peerreview process at TI, in its current form, was successful in selecting submitted manuscripts with a high scientific impact in the future. Predictive models might have the potential to support the review process when decisions are made under great uncertainty.