Pre-Analytical Issues and Interferences in Transfusion Medicine Tests

Nedelcu, Elena

doi:10.1016/b978-0-12-415783-5.00017-7

Cited by 2 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second ambiguous result was the presence of immune antibodies detected in the screening test that were not confirmed in further analysis. In general, when the antibody screen is positive, further testing by an extended panel is required to determine whether there is any specificity against major RBC antigens [7] . Thus, according to the Polish legal regulations, the consequence of ascertaining the presence of immune antibodies in a screening test was the sending of the samples to the Regional Centre for the consultative testing whose purpose is to identify the immune antibodies and select the appropriate blood for the patient [7] .…”

Section: Introductionmentioning

confidence: 99%

“…Interferences in pre-transfusion blood testing can cause potentially fatal errors or delay in supplying blood for transfusion [7] . In our case, paradoxically, the delay probably prevented an unnecessary transfusion, since it is likely that, in the absence of immune antibodies in the screening test, the blood for transfusion would have been delivered earlier and the transfusion would have been performed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnosis of severe anaemia and positive antibody screening as consequences of pre-analytical error

2023

View full text Add to dashboard Cite

The pre-analytical phase is the principal source of errors in laboratory medicine and continues to pose a challenge to laboratory professionals. We present the case of a 73-year-old female patient with a very low haemoglobin level (69 g/L) and positive indirect antiglobulin test result that indicates the key role of phlebotomy as an important error-prone process in which mistakes can have serious consequences for the patient’s diagnosis and treatment. We conclude that there is still an urgent and continuous need to provide educational activities for healthcare professionals involved in blood collection, improve blood collection guideline adherence, and eliminate the errors which can affect diagnosis and treatment, thus jeopardising patient safety.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnosis of severe anaemia and positive antibody screening as consequences of pre-analytical error

2023

View full text Add to dashboard Cite

show abstract

Convolutional neural network-based automatic classification for incomplete antibody reaction intensity in solid phase anti-human globulin test image

Duan

Wang

et al. 2022

Med Biol Eng Comput

View full text Add to dashboard Cite

The precise classification of incomplete antibody reaction intensity (IARI) in hydrogel chromatography medium high density medium solid-phase Coombs test is essential for haemolytic disease screening. However, an automatic and contactless method is required for accurate classification of IARI. Here, we present a deep ensemble learning model that integrates five different convolutional neural networks into a single model for IARI classification. A dataset, including 1628 IARI images and corresponding labels of IARI categories ((-), (1 +), (2 +), (3 +), and (4 +)), was used. We trained our model using 1302 IARIs and validated its performance using 326 IARIs. The proposed model achieved 100%, 99.4%, 99.4%, 100%, and 100% accuracies in the ( −), (1 +), (2 +), (3 +), and (4 +) categories, respectively. The results were compared with those of manual classification by immunologists (average accuracy: 99.8% vs. 88.3%, p < 0.01). Following model assistance, all three immunologists achieved increased accuracy (average accuracy: + 6.1%), with the average accuracy of junior immunologists maximum increasing by 11.3%. The time required for model classification was 0.094 s·image –1 , whereas that required manually was 5.528 s·image –1 . The proposed model can thus substantially improve the accuracy and efficiency of IARI classification and facilitate the automation of haemolytic disease screening equipment. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11517-022-02523-1.

show abstract

Pre-Analytical Issues and Interferences in Transfusion Medicine Tests

Cited by 2 publications

References 25 publications

Diagnosis of severe anaemia and positive antibody screening as consequences of pre-analytical error

Diagnosis of severe anaemia and positive antibody screening as consequences of pre-analytical error

Convolutional neural network-based automatic classification for incomplete antibody reaction intensity in solid phase anti-human globulin test image

Contact Info

Product

Resources

About