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The following is a summary of “A clinical predictive score of high liver iron content in metabolic hyperferritinemia: a retrospective cohort pilot study,” published in the May 2025 issue of BMC Gastroenterology by Boughzala et al. 

Researchers conducted a retrospective study to develop a clinical predictive score for high liver iron content (LIC) in metabolic hyperferritinemia (MH) to guide the use of magnetic resonance imaging (MRI) of the liver.  

They assessed patients with MH who underwent LIC evaluation at diagnosis. Patients with excessive alcohol consumption were excluded. A multivariate analysis followed by a 1000-bootstrap replicate analysis using an expectation–maximization algorithm was performed to identify predictive factors of high LIC. A receiver operative curve (ROC) analysis was constructed to evaluate the performance of the predictive score based on odds-ratios (OR) from the multivariate analysis.  

The results showed that 217 patients (180 men, mean age 57 years) were included, with 55 patients (25%) having high LIC (≥ 100 µmol/g). Univariate analysis revealed that a family history of hyperferritinemia requiring phlebotomies and transferrin saturation > 45% (P< 0.001) were associated with high LIC. Multivariate regression identified significant associations with high LIC for a family history of hyperferritinemia ([OR] 6.15, CI95 [2.11–17.92]), ferritin level ≥600 µg/L (OR 5.53, CI95 [1.43–21.42]), and transferrin saturation ≥45% (OR 2.63, CI95 [1.32–5.23]). A 15-point predictive score was developed, with an area-under-the-curve (AUC) of 0.72 (CI95 [0.64–0.79], P< 0.001), yielding an OR of 4.17 (CI95 [2.15–8.07], P< 0.001), sensitivity of 60%, specificity of 97%, and negative predictive value of 84%.  

Investigators concluded that in this pilot study, ferritin ≥ 600 µg/L, transferrin saturation ≥ 45%, and a family history of hyperferritinemia requiring bloodletting provided a reliable clinical score to predict high LIC in metabolic adult hyperferritinemia, with the bootstrap analysis confirming the robustness of the model. 

Source: bmcgastroenterol.biomedcentral.com/articles/10.1186/s12876-025-03891-2