Abstract
Objectives
Contamination of blood samples from patients receiving intravenous fluids is a common error with potential risk to the patient. Algorithms based on the presence of aberrant results have been described but have the limitation that not all infusion fluids have the same composition. Our objective is to develop an algorithm based on the detection of the dilution observed on the analytes not usually included in infusion fluids.
Methods
A group of 89 cases was selected from samples flagged as contaminated. Contamination was confirmed by reviewing the clinical history and comparing the results with previous and subsequent samples. A control group with similar characteristics was selected. Eleven common biochemical parameters not usually included in infusion fluids and with low intraindividual variability were selected. The dilution in relation to the immediate previous results was calculated for each analyte and a global indicator, defined as the percentage of analytes with significant dilution, was calculated. ROC curves were used to define the cut-off points.
Results
A cut-off point of 20 % of dilutional effect requiring also a 60 % dilutional ratio achieved a high specificity (95 % CI 91–98 %) with an adequate sensitivity (64 % CI 54–74 %). The Area Under Curve obtained was 0.867 (95 % CI 0.819–0.915).
Conclusions
Our algorithm based on the global dilutional effect presents a similar sensitivity but greater specificity than the systems based on alarming results. The implementation of this algorithm in the laboratory information systems may facilitate the automated detection of contaminated samples.
Acknowledgments
Thanks to Alejandro Gómez from Siemens Healthineers for contribution on LIS implementation of the algorithm.
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Research funding: None declared.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: Authors state no conflict of interest.
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Informed consent: Not applicable.
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Ethical approval: Not applicable.
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