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New solutions to old problems: A practical approach to identify samples with intravenous fluid contamination in clinical laboratories.
Clinical Biochemistry 2024 May
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.
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.
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