Minimizing the discard blood volume from the central venous catheter for routine laboratory testing in adult intensive care unit
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Ivana Lapić
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Josipa Kostelac
Abstract
Objectives
The aim was to reduce blood loss related to laboratory blood sampling by decreasing the discard blood volume from the central venous catheter (CVC) from 10 to 3 mL, and replacing the 3 mL tube for complete blood count (CBC) with a 2 mL tube. A comparison study was performed, and after introduction of this modified protocol the decrease in withdrawn blood was calculated.
Methods
In 52 adult inpatients at the ICU 3 mL of blood was discarded from the CVC, and blood was drawn in a 5 mL serum tube and a 2 mL EDTA tube (test samples), followed by subsequent sampling for routine laboratory testing using the same blood tubes (routine samples). In paired test and routine samples, clinical chemistry testing and CBC were performed.
Results
High correlations (ρ>0.90) were obtained for the majority of parameters. Small constant difference was obtained for hemoglobin. Small significant biases were observed for hemoglobin, MCHC, albumin, ALT, calcium, chlorides, MCV and glucose, however, being below the biases of the analytical methods. No statistically significant differences between test and routine samples were found for any of the assessed tests (p>0.05). Implementation of the 3 mL discard blood volume protocol and transition to a smaller volume tube for CBC reduced the amount of withdrawn blood by almost half (46 %).
Conclusions
The 3 mL discard blood volume from the CVC can be safely used for routine hematology and clinical chemistry testing. The dual approach assessed herein can contribute to reduction of iatrogenic blood loss.
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Research ethics: The local Institutional Review Board deemed the study exempt from review.
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Informed consent: Not applicable.
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Author contributions: IL, DR and RR designed the study, analyzed the data and wrote the manuscript. IL, LB, AVB and IRG acquired and analyzed the data, and co-wrote the manuscript. The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: The raw data can be obtained on request from the corresponding author.
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