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

abstractSickle cell disease (SCD) is a single-gene illness that 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 noninvasive indicator of vaso-occlusion and risk of organ damage in SCD. This prospective observational cohort study included 150 individuals (aged >15 years) living with SCD. Retinal optical coherence tomography angiography and basic laboratory tests were performed at 6-month intervals over a median of 450 days (interquartile range, 295-744) of follow-up. Primary outcomes were hospitalization within 12 months, kidney damage (urine albumin-to-creatinine ratio of >100 and increased by ≥15 at next study visit), and heart-lung damage (N-terminal pro-brain natriuretic peptide level of >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 an area under the receiver operating characteristic (AUROC) curve of 0.717 (standard deviation [SD], 0.053; five-fold cross-validation). We predicted future kidney damage with an AUROC of 0.881 (SD, 0.083) and heart-lung damage with an AUROC of 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.