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New solutions to old problems: A practical approach to identify samples with intravenous fluid contamination in
Ashley Newbigging1, Natalie Landry2, Miranda Brun3
1Department of Laboratory Medicine and Pathology, Faculty of Medicine and Dentistry, College of Health Science, University of Alberta, Edmonton, Alberta, Canada.
Insights
A new algorithm effectively detects intravenous (IV) fluid contamination in patient samples using common lab tests. This method improves accuracy in identifying contaminated specimens, reducing errors in hospitalized patients.
Area of Science:
- Clinical Chemistry
- Laboratory Medicine
- Diagnostic Accuracy
Background:
- Intravenous (IV) fluid contamination is a frequent issue in healthcare, leading to specimen rejection and inaccurate patient results.
- Existing methods for identifying IV fluid contamination often lack sufficient sensitivity and specificity.
- Accurate identification of contaminated samples is crucial for reliable patient diagnosis and treatment.
Purpose of the Study:
- To develop and validate criteria for detecting IV fluid contamination using routine laboratory tests.
- To assess the performance of a novel algorithm designed to identify IV fluid contamination.
- To evaluate the feasibility and flagging rates of the algorithm in various laboratory settings.
Main Methods:
- Developed detection criteria by analyzing patterns in laboratory results from contaminated and non-contaminated samples.
- Prospectively implemented and evaluated the algorithm in a tertiary care hospital laboratory over six months.
- Assessed the algorithm's performance retrospectively across multiple hospital and community laboratories, including external validation.
Main Results:
- The algorithm demonstrated a high positive predictive value (92%) and negative predictive value (91%), with 92% overall agreement when two or more criteria were met.
- Flagging rates for IV fluid contamination were low, ranging from 0.03% to 0.07% in hospital labs and 0.003% in community labs.
- Prospective and retrospective analyses confirmed the algorithm's effectiveness in identifying true contamination.
Conclusions:
- The developed algorithm accurately identifies true IV fluid contamination with minimal false positives in hospital laboratories.
- The algorithm is suitable for clinical laboratory implementation to flag samples potentially contaminated with IV fluids for further investigation.
- This approach offers a reliable method to improve the quality of laboratory testing in hospitalized patients.
Objectives:
Contamination with intravenous (IV) fluids is a common cause of specimen rejection or erroneous results in hospitalized patients. Identification of contaminated samples can be difficult. Common measures such as failed delta checks may not be adequately sensitive nor specific. This study aimed to determine detection criteria using commonly ordered tests to identify IV fluid contamination and validate the use of these criteria.
Methods:
Confirmed contaminated and non-contaminated samples were used to identify patterns in laboratory results to develop criteria to detect IV fluid contamination. The proposed criteria were implemented at a tertiary care hospital laboratory to assess performance prospectively for 6 months, and applied to retrospective chemistry results from 3 hospitals and 1 community lab to determine feasibility and flagging rates. The algorithm was also tested at an external institution for transferability.
Results:
The proposed algorithm had a positive predictive value of 92 %, negative predictive value of 91 % and overall agreement of 92 % when two or more criteria are met (n = 214). The flagging rates were 0.03 % to 0.07 % for hospital and 0.003 % for community laboratories.
Conclusions:
The proposed algorithm identified true contamination with low false flagging rates in tertiary care urban hospital laboratories. Retrospective and prospective analysis suggest the algorithm is suitable for implementation in clinical laboratories to identify samples with possible IV fluid contamination for further investigation.
