Retinal imaging and supervised learning predict hospitalizations, kidney, and heart-lung damage in sickle cell disease.

Sickle cell disease (SCD) is a single-gene illness which causes painful vaso-occlusion, debilitating organ damage, and early mortality. Its clinical course is variable, but current prognostic tools have large data requirements. Understanding prognosis is important for clinicians and patients in selecting therapies, particularly curative therapies. Retinal imaging may provide a non-invasive indicator of vaso-occlusion and risk of organ damage in SCD. This prospective observational cohort study included 150 individuals (>15 years) living with SCD. Retinal optical coherence tomography-angiography and basic laboratory tests were performed at six-month intervals over median 450 days (IQR 295-744) of follow-up. Primary outcomes were hospitalization within 12 months, kidney damage (urine albumin-to-creatinine ratio, uACR >100 and increased by ≥15 at next study visit), and heart-lung damage (N-terminal pro-Brain Natriuretic Peptide (NTproBNP) >160 pg/mL and increased by ≥15 pg/mL at next study visit). We processed retinal image metrics for perfusion and vascularity using established methods. We extracted retinal image features using a Swin transformer, and created ensemble models to predict outcomes. Random forest models predicted hospitalization within 12 months with area under the receiver operating curve (AUROC) 0.717 (standard deviation, SD = 0.053, 5-fold cross-validation). We predicted future kidney damage, AUROC 0.881 (SD 0.083), and heart-lung damage, AUROC 0.866 (SD 0.106). Ensemble models including transformer-derived image features, processed retinal image metrics, and laboratory tests performed best. Using supervised machine learning on noninvasive retinal imaging and basic laboratory tests we predicted future SCD hospitalizations and organ damage. This method shows promise for prognostication in SCD.