We investigate the problem of automatic cardiomegaly diagnosis. We approach this by developing classifiers using multimodal data enhanced by two image-derived digital biomarkers, the cardiothoracic ratio (CTR) and the cardiopulmonary area ratio (CPAR). The CTR and CPAR values are estimated using segmentation and detection models. These are then integrated into a multimodal network trained simultaneously on chest radiographs and ICU data (vital sign values, laboratory values and metadata). We compare the predictive power of different data configurations with and without the digital biomarkers. There was a negligible performance difference between the XGBoost model containing only CTR and CPAR (accuracy 81.2%, F1 0.859, AUC 0.810) and black-box models which included full images (ResNet-50: accuracy 81.9%, F1 0.874, AUC 0.767; Multimodal: 81.9%, F1 0.873, AUC 0.768). We concluded that models incorporating domain knowledge-based digital biomarkers CTR and CPAR provide comparable performance to blackbox multimodal approaches with the former providing better clinical explainability.