Use and Waste of Reconstituted Whole Blood Exchange Transfusions: An 11-year National Observational Study.

OBJECTIVES: To identify indications for exchange transfusions, assess the use and waste of exchange transfusion products (ie, reconstituted whole blood exchange transfusions), and determine nationwide distribution and prevalence of these transfuions in the Netherlands. STUDY DESIGN: All nine neonatal intensive care units (NICU) and 15 non-NICU hospitals participated in this retrospective, observational, cohort study. We retrieved data on indications for and use of all exchange transfusion products ordered by participating centers over an 11-year period. RESULTS: A total of 574 patients for whom 1,265 products were ordered were included for analyses. Severe ABO (32.6%) and non-ABO (25.2%) immune hemolysis and subsequent hyperbilirubinemia were the most frequent indications. Rare indications were severe leukocytosis in Bordetella pertussis (2.1%) and severe anemia (1.5%). Approximately half of all ordered products remained unused. In 278 of 574 neonates (48.4%), one or more products were not used, of which 229 (82.7%) were due to the resolving of severe hyperbilirubinemia with further intensification of phototherapy. The overall prevalence of neonates who received an exchange transfusion was 14.6:100,000 liveborn neonates. CONCLUSION: A considerable proportion of products remained unused, and annually a limited number of patients are treated with an exchange transfusion in the Netherlands, highlighting the rarity of the procedure in the Netherlands.

Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data.

BACKGROUND: Hemoglobinopathies, the most common inherited blood disorder, are frequently underdiagnosed. Early identification of carriers is important for genetic counseling of couples at risk. The aim of this study was to develop and validate a novel machine learning model on a multicenter data set, covering a wide spectrum of hemoglobinopathies based on routine complete blood count (CBC) testing. METHODS: Hemoglobinopathy test results from 10 322 adults were extracted retrospectively from 8 Dutch laboratories. eXtreme Gradient Boosting (XGB) and logistic regression models were developed to differentiate negative from positive hemoglobinopathy cases, using 7 routine CBC parameters. External validation was conducted on a data set from an independent Dutch laboratory, with an additional external validation on a Spanish data set (n = 2629) specifically for differentiating thalassemia from iron deficiency anemia (IDA). RESULTS: The XGB and logistic regression models achieved an area under the receiver operating characteristic (AUROC) of 0.88 and 0.84, respectively, in distinguishing negative from positive hemoglobinopathy cases in the independent external validation set. Subclass analysis showed that the XGB model reached an AUROC of 0.97 for ß-thalassemia, 0.98 for α0-thalassemia, 0.95 for homozygous α+-thalassemia, 0.78 for heterozygous α+-thalassemia, and 0.94 for the structural hemoglobin variants Hemoglobin C, Hemoglobin D, Hemoglobin E. Both models attained AUROCs of 0.95 in differentiating IDA from thalassemia. CONCLUSIONS: Both the XGB and logistic regression model demonstrate high accuracy in predicting a broad range of hemoglobinopathies and are effective in differentiating hemoglobinopathies from IDA. Integration of these models into the laboratory information system facilitates automated hemoglobinopathy detection using routine CBC parameters.

[Normocytic anaemia due to multiple causes]. / Een normocytaire anemie met meerdere oorzaken.

Anaemia can be subdivided into microcytic, normocytic and macrocytic categories, which give direction to the investigation to identify the cause of the anaemia. However, this is not always straightforward, as this case illustrates. A 73-year-old man with normocytic anaemia appeared to have both beta thalassemia trait (microcytic) and myelodysplastic syndrome (usually macrocytic). This led to a mixture of red blood cells of different sizes and an average normal MCV. The manual differentiation of the blood smear was helpful to find the right diagnosis.