Objective: The purpose of this study was to evaluate the ability of expert reviewers to differentiate an anemic from a nonanemic state on the basis of visual assessment of the relative attenuation of blood in the left ventricle on noncontrast thoracic CT images and to compare reviewer performance with quantitative measurement of CT density in Hounsfield units.
Materials And Methods: One hundred two noncontrast thoracic CT examinations were qualitatively reviewed by three independent reviewers. Hounsfield unit measurements of the blood in the left ventricle were recorded by a fourth individual. Anemia was defined as a hemoglobin level of less than 10 g/dL. Receiver operating characteristic (ROC) analyses of expert reviewers were compared with measured Hounsfield units.
Results: Hounsfield unit measurements performed significantly better than subjective reviewer analyses for differentiation of an anemic from a nonanemic state (area under ROC curve = 0.85 vs 0.72, 0.70, and 0.69; 95% confidence interval, 0.78-0.92 vs 0.63-0.81, 0.61-0.79, and 0.60-0.78, respectively; p < 0.05). With use of a CT density threshold of 35 H, the sensitivity for anemia was 76% and specificity was 81%, whereas the sensitivity of three reviewers was 40-72% with a specificity of 60-83%. Interobserver agreement was found to be poor by kappa statistic (0.0906-0.2128). The correlation coefficient for the analysis of Hounsfield unit versus hemoglobin level was 0.72. Separating data by patient sex revealed a correlation coefficient of 0.81 for men versus 0.52 for women, although the actual regression lines were not statistically different (p > 0.05).
Conclusion: Despite expert reviewer analyses, subjective evaluations of blood attenuation characteristics are prone to inaccuracy and show poor interobserver agreement. Quantitative measurements of CT density in Hounsfield units should be performed to accurately differentiate an anemic from a nonanemic state when serum hemoglobin levels are not readily available.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.2214/AJR.04.1171 | DOI Listing |
BMC Anesthesiol
January 2025
Department of Anesthesia, College of Medicine and Health Sciences, Bahir Dar University, PO Box 79, Bahir Dar, Ethiopia.
Introduction: In a low-income country, the impact of preoperative anemia on postoperative mortality among noncardiac surgery patients is little understood. As a result, we aim to investigate the association between preoperative anemia and postoperative mortality in noncardiac surgery patients in Northwest Ethiopia.
Methods: This is a prospective follow-up study of 3506 noncardiac surgery patients who were included in the final analysis between June 1, 2019, and July 1, 2021.
Sci Rep
January 2025
Internal Medicine Service, Hospital Viamed Santa Ángela de la Cruz, Seville, Spain.
Obesity and iron deficiency (ID) are widespread health issues, with subclinical inflammation in obesity potentially contributing to ID through unclear mechanisms. The aim of the present work was to elucidate how obesity-associated inflammation disturb iron metabolism and to investigate the effect of intravenous (IV) iron supplementation on absolute iron deficient pre-obese (BMI 25.0-29.
View Article and Find Full Text PDFPerioper Med (Lond)
December 2024
Department of Anesthesia, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia.
Introduction: Preoperative anemia in orthopedic surgery is linked to adverse outcomes such as longer hospital stays, higher rates of blood transfusion, and increased risk of death. Effectively addressing and managing this condition is essential for improving patient outcomes and shortening the length of hospital stays. In Ethiopia and other low-income countries, studies on preoperative anemia and its impact on the length of hospital stay following orthopedic surgery are limited.
View Article and Find Full Text PDFBMC Public Health
December 2024
Health Informatics, Ethiopian Public Health Institute, P.O. Box 1242, Addis Ababa, Ethiopia.
Background: Anemia during pregnancy is a significant public health concern, particularly in resource-limited settings. Machine learning (ML) offers promising avenues for improved anemia detection and management. This study investigates the potential of ML models in predicting anemia severity among pregnant women attending Antenatal Care (ANC) visits in Ethiopia.
View Article and Find Full Text PDFAm J Trop Med Hyg
December 2024
UMR 261-MERIT, Institut de Recherche pour le Développement (IRD), Université Paris Cité, Paris, France.
Anemia in pregnancy, defined by a hemoglobin level (Hb) of less than 110 g/L, contributes to infant mortality and morbidity in sub-Saharan Africa. Maternal Hb changes physiologically and pathologically during pregnancy. However, the impact of these changes on long-term child neurocognitive function is unknown.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!