Potential of Parameters of Iron Metabolism for the Diagnosis of Anemia of Inflammation in the Critically Ill
© 2019 S. Karger AG, Basel. 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...
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th-mahidol.523482020-01-27T17:37:22Z Potential of Parameters of Iron Metabolism for the Diagnosis of Anemia of Inflammation in the Critically Ill Margit Boshuizen Jan M. Binnekade Benjamin Nota Kirsten Van De Groep Olaf L. Cremer Janneke Horn Marcus J. Schultz Robin Van Bruggen Nicole P. Juffermans University Medical Center Utrecht Mahidol University Universiteit van Amsterdam Medicine © 2019 S. Karger AG, Basel. 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. 2020-01-27T10:37:22Z 2020-01-27T10:37:22Z 2019-01-01 Article Transfusion Medicine and Hemotherapy. (2019) 10.1159/000497123 16603818 16603796 2-s2.0-85065627359 https://repository.li.mahidol.ac.th/handle/123456789/52348 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065627359&origin=inward |
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Medicine Margit Boshuizen Jan M. Binnekade Benjamin Nota Kirsten Van De Groep Olaf L. Cremer Janneke Horn Marcus J. Schultz Robin Van Bruggen Nicole P. Juffermans Potential of Parameters of Iron Metabolism for the Diagnosis of Anemia of Inflammation in the Critically Ill |
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© 2019 S. Karger AG, Basel. 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. |
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University Medical Center Utrecht |
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University Medical Center Utrecht Margit Boshuizen Jan M. Binnekade Benjamin Nota Kirsten Van De Groep Olaf L. Cremer Janneke Horn Marcus J. Schultz Robin Van Bruggen Nicole P. Juffermans |
format |
Article |
author |
Margit Boshuizen Jan M. Binnekade Benjamin Nota Kirsten Van De Groep Olaf L. Cremer Janneke Horn Marcus J. Schultz Robin Van Bruggen Nicole P. Juffermans |
author_sort |
Margit Boshuizen |
title |
Potential of Parameters of Iron Metabolism for the Diagnosis of Anemia of Inflammation in the Critically Ill |
title_short |
Potential of Parameters of Iron Metabolism for the Diagnosis of Anemia of Inflammation in the Critically Ill |
title_full |
Potential of Parameters of Iron Metabolism for the Diagnosis of Anemia of Inflammation in the Critically Ill |
title_fullStr |
Potential of Parameters of Iron Metabolism for the Diagnosis of Anemia of Inflammation in the Critically Ill |
title_full_unstemmed |
Potential of Parameters of Iron Metabolism for the Diagnosis of Anemia of Inflammation in the Critically Ill |
title_sort |
potential of parameters of iron metabolism for the diagnosis of anemia of inflammation in the critically ill |
publishDate |
2020 |
url |
https://repository.li.mahidol.ac.th/handle/123456789/52348 |
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1763488060701409280 |