Supplementary Material for: Potential of Parameters of Iron Metabolism for the Diagnosis of Anemia of Inflammation in the Critically Ill

Background: Anemia of inflammation (AI) is the most common cause of anemia in the critically ill, but its diagnosis is a challenge. New therapies specific to AI are in development, and they require accurate detection of AI. This study explores the potential of parameters of iron metabolism for the diagnosis of AI during an ICU stay. Methods: In a nested case-control study, 30 patients developing AI were matched to 60 controls. The iron parameters were determined in plasma samples during an ICU stay. Receiver operating characteristic curves were used to determine the iron parameter threshold with the highest sensitivity and specificity to predict AI. Likelihood ratios as well as positive and negative predictive values were calculated as well. Results: The sensitivity of iron parameters for diagnosing AI ranges between 62 and 76%, and the specificity between 57 and 72%. Iron and transferrin show the greatest area under the curve. Iron shows the highest sensitivity, and transferrin and transferrin saturation display the highest specificity. Hepcidin and ferritin show the lowest specificity. At an actual anemia prevalence of 53%, the diagnostic accuracy of iron, transferrin, and transferrin saturation was fair, with a positive predictive value between 71 and 73%. Combining iron, transferrin, transferrin saturation, hepcidin, and/or ferritin levels did not increase the accuracy of the AI diagnosis. Conclusions: In this explorative study on the use of different parameters of iron metabolism for diagnosing AI during an ICU stay, low levels of commonly measured markers such as plasma iron, transferrin, and transferrin saturation have the highest sensitivity and specificity and outperform ferritin and hepcidin.

背景：炎症性贫血（AI）是重症患者贫血的最常见原因，但其诊断颇具挑战。针对AI的新型疗法正在研发中，并需依赖于对AI的精准检测。本研究旨在探讨在重症监护期间，铁代谢参数在AI诊断中的潜在应用。方法：在一项嵌套的病例对照研究中，30例发展为AI的患者与60例对照者相匹配。在重症监护期间，对血浆样本中的铁参数进行了测定。利用接受者操作特征曲线来确定预测AI的最高敏感性和特异性的铁参数阈值。同时计算了似然比以及阳性预测值和阴性预测值。结果：用于诊断AI的铁参数的敏感性范围为62%至76%，特异性范围为57%至72%。铁和转铁蛋白显示出曲线下面积最大。铁表现出最高的敏感性，而转铁蛋白和转铁蛋白饱和度显示出最高的特异性。他赛汀和铁蛋白显示出最低的特异性。在实际情况中，贫血的患病率为53%，铁、转铁蛋白和转铁蛋白饱和度的诊断准确性尚可，阳性预测值介于71%至73%之间。将铁、转铁蛋白、转铁蛋白饱和度、他赛汀和/或铁蛋白水平相结合并未提高AI诊断的准确性。结论：在本项关于在重症监护期间利用不同铁代谢参数诊断AI的探索性研究中，常规检测指标如血浆铁、转铁蛋白和转铁蛋白饱和度的低水平具有最高的敏感性和特异性，并优于铁蛋白和他赛汀。