Blending Education, Machine Learning to Detect IV Fluid Contaminated CBCs, With Carly Maucione, MD

Improved detection of IV fluid contamination in complete blood counts (CBCs) could prevent a subset of unnecessary downstream blood transfusions, whether through clinician education or machine learning, according to new research.1

Findings from a multicenter machine learning study identified a surprising frequency of contaminated blood specimens, underscoring the clinical consequences of acting on erroneous laboratory results.1

“I would be happy if more people read about this problem and familiarized themselves with what IV fluid contamination could look like in their patient specimens,” said study investigator Carly Maucione, MD, a resident physician at Washington University in St. Louis, in an interview with HCPLive. “When they’re working on the floor, and they do see something suspicious, they might say, ‘I remember hearing about this. I think this might be a contaminated specimen.’ Then they do a double check before ordering blood products, or take a moment to make sure they’re not acting on a false basic metabolic panel result.”

IV fluid contamination can be difficult to distinguish from true clinical changes, particularly in hospitalized patients with multiple lines and ongoing infusions, increasing the risk of downstream clinical interventions. Since blood transfusions are commonly initiated in response to abnormal laboratory values, even small rates of undetected contamination can have meaningful clinical implications.2

Maucione and colleagues identified machine learning as a potential method to standardize CBC evaluation and assist pathologists with greater precision. Within laboratory practice, there are limited techniques for detecting IV fluid contamination, and commonly used approaches, such as delta checks, are not designed to have high sensitivity for this issue.1,2

“There’s a lot of potential, at least in the laboratory, for machine learning in areas like flow cytometry,” said Maucione. “A lot of the preprocessing and gating is really manual. It’s pattern recognition, and that’s something machine learning can do really well. That’s a hang-up in our workflow, and anything that could do it faster is where I think we should be putting our efforts.”

The retrospective, multicenter machine learning diagnostic validation study used real-world inpatient data from 2 institutions, developing and testing 2 machine learning models with areas under the receiver operating characteristic curve of 0.972 and 0.957 and areas under the precision-recall curve of 0.723 and 0.619 for identifying IV fluid contamination in CBC results.1

Results from the models revealed IV fluid contamination in approximately 2% of CBCs, with investigators estimating 6-9% of subsequent transfusions may have been unnecessary.1

After identifying the unexpectedly high frequency of contaminated specimens and potentially avoidable transfusions, Maucione and colleagues aimed to address the gap. She acknowledged, however, that there is no gold standard for confirming IV fluid contamination and that further validation will be necessary to ensure the findings are not attributable to other confounding factors. Improving the positive predictive value of the models would be required before using them to delay the release of blood products when contamination is suspected, although this remains a longer-term goal.1

While real-time implementation remains a longer-term goal, Maucione underscored that education represents a more immediate and achievable intervention. Increasing awareness among pathologists, laboratorians, phlebotomists, nurses, and other clinicians who draw blood could help prevent misinterpretation of contaminated specimens and reduce unnecessary transfusions.1

Ultimately, the study highlights both the promise and the limitations of machine learning in laboratory medicine. Although artificial intelligence may help close gaps in detecting IV fluid contamination, Maucione stressed that thoughtful integration, rigorous validation, and clinician education will remain essential to ensuring patient safety and improving care.1

Editor’s Note: Maucione reports no relevant disclosures.

References

1. Maucione C, McLamb N, Zaydman MA, Pearson LN, Metcalf RA, Spies NC. Identification of IV fluid contamination in complete blood counts and subsequent unnecessary red blood cell transfusions using artificial intelligence. Transfusion. Published online August 2026:10.1111/trf.70072. doi:https://doi.org/10.1111/trf.70072

2. Spies NC, Farnsworth CW. Impact and frequency of IV fluid contamination on basic metabolic panel results using quality metrics. Journal of Laboratory Medicine. 2023;48(1):29-36. doi:https://doi.org/10.1515/labmed-2023-0098