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Published on: February 8, 2016
Automated detection of intravenous fluid contamination using longitudinal routine laboratory data: development and
Jordi Tortosa-Carreres1, Nahúm Martí-Montoro1, Jonnathan Andrés Acevedo-Galvis1
1Laboratory Department, Hospital Universitari i Politècnic La Fe. Av. Fernando Abril Martorell, 106, 46026 València, Spain; Health Research Institute Hospital La Fe (IISLaFe), Valencia, Spain; Clinical Laboratory Medicine Research Group.
Objectives:
To develop and externally validate an automated framework for intravenous fluid contamination (IFC) screening using routinely available longitudinal laboratory data.
Methods:
A bicentric retrospective study included an internal cohort (IC; Hospital La Fe, València) for model development and an external validation cohort (EVC; Hospital General Universitari de València) composed of hospitalized patients with a previous determination within 5 days. Normalized relative change variables were calculated from paired measurements. Controls were retrospectively selected in the IC after removal of outliers using an autoencoder; contaminated samples were generated through controlled in vitro admixture with normal saline and glucose-containing solutions. A multitask neural network provided contamination probability, likely contaminant type, and severity-oriented estimation. Performance was evaluated using repeated internal 80:20 partitions (IRP; n = 30), the EVC, and an exploratory real-world dataset of retrospectively suspected IFC cases (RW-IFC). Model outputs were used to derive a stratified risk framework.
Results:
The IC included 1760 samples (50% IFC), the EVC 1371 samples (5.4% IFC), and RW-IFC 37 samples. Discriminative performance remained high (ROC-AUC 0.94-0.96), with sensitivities and specificities ≥85%. Most false-negatives corresponded to contamination ≤5%, and the mean predicted contamination level among false-positive cases was 2.12 ± 1.06%. Multiclass accuracy ranged 0.87-0.90. Severity estimation showed strong internal correlation (ρ = 0.83; p < 0.0001) and good external agreement (0.84 within ±1 level), although correlation decreased in the EVC. 97% of RW-IFC cases were correctly identified. Risk stratification in the EVC achieved a sensitivity of 99.1% while reducing unnecessary alerts by 17%.
Conclusions:
This framework showed potential to support automated IFC screening.
