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Introduction: The newest intravenous (IV) iron products show an improved safety profile over predecessors, allowing for the rapid administration of relatively high doses. Ferric derisomaltose (FDI; also known as iron isomaltoside), ferric carboxymaltose (FCM), and ferumoxytol (FER), are successful treatments for iron deficiency (Europe; FDI and FCM) and iron deficiency anemia (US; FDI, FCM, and FER). Areas covered: This review focusses on the chemistry and structure of FDI, FCM, and FER, and on three key aspects of IV iron safety: (1) hypersensitivity; (2) hypophosphatemia and sequelae; (3) cardiovascular safety. Expert opinion: Although the safety of modern IV iron has improved, immediate infusion reactions and the development of hypophosphatemia must be appreciated and recognized by those who prescribe and administer IV iron. Immediate infusion reactions can occur with any IV iron and are usually mild; severe reactions – particularly anaphylaxis – are extremely rare. The recognition and appropriate management of infusion reactions is an important consideration to the successful administration of IV iron. Severe, persistent, hypophosphatemia is a specific side effect of FCM. No cardiovascular safety signal has been identified for IV iron. Ongoing trials in heart failure will provide additional long-term efficacy and safety data.
Background: To assess the potential of radiomic features to quantify components of blood in intraaortic vessels to non-invasively predict moderate-to-severe anemia in non-contrast enhanced CT scans. Methods: One hundred patients (median age, 69 years; range, 19–94 years) who received CT scans of the thoracolumbar spine and blood-testing for hemoglobin and hematocrit levels ± 24 h between 08/2018 and 11/2019 were retrospectively included. Intraaortic blood was segmented using a spherical volume of interest of 1 cm diameter with consecutive radiomic analysis applying PyRadiomics software. Feature selection was performed applying analysis of correlation and collinearity. The final feature set was obtained to differentiate moderate-to-severe anemia. Random forest machine learning was applied and predictive performance was assessed. A decision-tree was obtained to propose a cut-off value of CT Hounsfield units (HU). Results: High correlation with hemoglobin and hematocrit levels was shown for first-order radiomic features (p < 0.001 to p = 0.032). The top 3 features showed high correlation to hemoglobin values (p) and minimal collinearity (r) to the top ranked feature Median (p < 0.001), Energy (p = 0.002, r = 0.387), Minimum (p = 0.032, r = 0.437). Median (p < 0.001) and Minimum (p = 0.003) differed in moderate-to-severe anemia compared to non-anemic state. Median yielded superiority to the combination of Median and Minimum (p(AUC) = 0.015, p(precision) = 0.017, p(accuracy) = 0.612) in the predictive performance employing random forest analysis. A Median HU value ≤ 36.5 indicated moderate-to-severe anemia (accuracy = 0.90, precision = 0.80). Conclusions: First-order radiomic features correlate with hemoglobin levels and may be feasible for the prediction of moderate-to-severe anemia. High dimensional radiomic features did not aid augmenting the data in our exemplary use case of intraluminal blood component assessment.