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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. [ABSTRACT FROM AUTHOR]

Background: Anemia of inflammation (AI) is common in critically ill patients. Although this syndrome negatively impacts the outcome of critical illness, understanding of its pathophysiology is limited. Also, new therapies that increase iron availability for erythropoiesis during AI are upcoming. A model of AI induced by bacterial infections that are relevant for the critically ill is currently not available. This paper describes the development of an animal model for AI that is relevant for critical care research. Results: In experiments with rats, the rats were inoculated either repeatedly or with a slow release of Streptococcus pneumoniae or Pseudomonas aeruginosa. Rats became ill, but their hemoglobin levels remained stable. The use of a higher dose of bacteria resulted in a lethal model. Then, we turned to a model with longer disease duration, using pigs that were supported by mechanical ventilation after inoculation with P. aeruginosa. The pigs became septic 12 to 24 h after inoculation, with a statistically significant decrease in mean arterial pressure and base excess, while heart rate tended to increase. Pigs needed resuscitation and vasopressor therapy to maintain a mean arterial pressure > 60 mmHg. After 72 h, the pigs developed anemia (baseline 9.9 g/dl vs. 72 h, 7.6 g/dl, p = 0.01), characterized by statistically significant decreased iron levels, decreased transferrin saturation, and increased ferritin. Hepcidin levels tended to increase and transferrin levels tended to decrease. Conclusions: Using pathogens commonly involved in pulmonary sepsis, AI could not be induced in rats. Conversely, in pigs, P. aeruginosa induced pulmonary sepsis with concomitant AI. This AI model can be applied to study the pathophysiology of AI in the critically ill and to investigate the effectivity and toxicity of new therapies that aim to increase iron availability. [ABSTRACT FROM AUTHOR]