This paper presents a multimodal deep learning (DL) model for diabetic retinopathy (DR) referral that integrates retinal fundus images with clinically relevant data selected through an explainable process. Using Shapley Additive exPlanations (SHAP) across five machine learning (ML) models, we identified urinary albumin excretion, diabetes duration, insulin use, HbA1c, and systolic blood pressure as the most informative clinical features. We integrated these variables into an InceptionV3-based convolutional neural network (CNN) through late fusion and evaluated the model on two independent datasets from Hospital de Cl´ınicas de Porto Alegre (HCPA-2019: 2,522 images; HCPA-2021: 1,555 images). Compared with an image-only baseline, the multimodal model increased specificity from 56.7% to 64.7% in HCPA-2019 and from 72.4% to 77.5% in HCPA-2021, while maintaining sensitivity above 95% and an AUC above 0.93. These findings indicate that incorporating clinically interpretable metadata can reduce false-positive referrals and improve the clinical relevance of Artificial Intelligence (AI)- based DR screening.