Background Host inflammation contributes to determine whether SARS-CoV-2 infection causes mild or life-threatening disease. Tools are needed for early risk assessment. Methods We studied in 111 COVID-19 patients prospectively followed at a single reference Hospital fifty-three potential biomarkers including alarmins, cytokines, adipocytokines and growth factors, humoral innate immune and neuroendocrine molecules and regulators of iron metabolism. Biomarkers at hospital admission together with age, degree of hypoxia, neutrophil to lymphocyte ratio (NLR), lactate dehydrogenase (LDH), C-reactive protein (CRP) and creatinine were analysed within a data-driven approach to classify patients with respect to survival and ICU outcomes. Classification and regression tree (CART) models were used to identify prognostic biomarkers. Results Among the fifty-three potential biomarkers, the classification tree analysis selected CXCL10 at hospital admission, in combination with NLR and time from onset, as the best predictor of ICU transfer (AUC [95% CI] = 0.8374 [0.6233–0.8435]), while it was selected alone to predict death (AUC [95% CI] = 0.7334 [0.7547–0.9201]). CXCL10 concentration abated in COVID-19 survivors after healing and discharge from the hospital. Conclusions CXCL10 results from a data-driven analysis, that accounts for presence of confounding factors, as the most robust predictive biomarker of patient outcome in COVID-19. Graphic abstract

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Advances in multimodal imaging have significantly contributed to the management of many uveitis diseases in recent years. The most significant developments include the use of optical coherence tomography to obtain a more accurate and reproducible assessment of ocular inflammation, the application of optical coherence tomography angiography in choroiditis and retinal vasculitis, new possibilities for studying vitritis with ultrawide field imaging, and the most recent applications of fundus autofluorescence in uveitis. In this review, we provide an overview of the most significant advances in multimodal imaging of uveitis achieved in recent years.

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