Development and validation of a machine learning model for accurate detection of wrong blood in tube errors in hospitalized patients
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Abstract
Objectives
To develop and validate a machine-learning model based on routinely available biochemical and hematological parameters for detecting wrong blood in tube (WBIT) errors in hospitalized patients.
Methods
A retrospective multicenter study including one internal cohort (IC) and two external validation cohorts (EVC, EVC2). The IC was balanced (50 % correct, 50 % WBIT; 25 % real, 25 % simulated), while EVC (n=800) and EVC2 (n=460) represented more realistic scenarios (95 % correct, 5 % WBIT; equally distributed between real and simulated). Parameters present in ≥ 95 % of requests were selected, and their normalized variation from the immediately preceding result was calculated. The IC was divided into a training set (IC-TS, n=324) and an internal validation set (IC-VS, n=108). Feature selection was refined with Elastic Net before training an XGBoost model. Performance was assessed in IC-VS, EVC, and EVC2. For benchmarking, the model’s discriminative ability was also compared with a multivariate Mahalanobis-based approach and with univariate delta checks within IC-TS/IC-VS.
Results
Sixteen of 25 candidate variables were retained. The model achieved ROC-AUC values of 0.98–0.99 and PR-AUC values of 0.93–0.99 across all validation cohorts. Recalibration improved positive predictive value and net benefit by reducing false positives, with a slight decrease in sensitivity, although all values remained ≥90 %. Specificities ranged from 98 to 99 %. The model consistently outperformed both the multivariate Mahalanobis approach and univariate delta checks within the internal cohort.
Conclusions
This machine-learning model, leveraging widely available routine laboratory parameters, shows strong potential for integration into clinical workflows, enhancing WBIT detection and improving patient safety.
Acknowledgments
We sincerely thank Jesús Álvarez-Sáez for his invaluable technical support in the development of the CDS system and his assistance in building the Python library, as well as for his constant availability and commitment throughout the process. We also gratefully acknowledge Laura Martínez-Racaj for her continued technical support and dedication in the implementation of the CDS infrastructure.
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Research ethics: The study was approved by the Institutional Ethics Committee (reference number 2024-1073-1) and conducted in accordance with the principles of the Declaration of Helsinki, ensuring data confidentiality. All data used in this study were pseudonymised prior to analysis by assigning a unique patient identifier. The original identifying information remained securely stored on hospital computers protected by password access and was not transferred or accessible during the analysis process. The study was conducted in accordance with the General Data Protection Regulation (EU) 2016/679 (GDPR) and the Spanish Organic Law 3/2018 on the Protection of Personal Data and Guarantee of Digital Rights (LOPDGDD).
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Informed consent: This retrospective study was conducted using clinical data originally collected for healthcare purposes. In accordance with applicable regulations and following review by the Institutional Ethics Committee, the study was deemed exempt from the requirement for individual informed consent, as it involved no intervention and posed no risk to the patients.
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Author contributions: Jordi Tortosa-Carreres: Conceptualization; Methodology; Software; Data curation; Formal analysis; Investigation; Visualization; Writing – Original Draft; Ana Vañó-Bellver: Investigation; Data curation; Andreu Martínez-Cerezuela: Investigation; Data curation; Óscar Fuster-Lluch: Writing – Review & Editing; Supervision; Lucía García-Ruiz: Investigation; Data curation; Elena Rodríguez-Romero: Investigation; Data curation; Antonio Sierra-Rivera: Investigation; Data curation; Writing – Review & Editing; Ana Comes-Raga: Investigation; Data curation; Writing – Review & Editing; Carlos Cátedra: Investigation; Data curation; Virginia Tadeo-Garisto: Investigation; Data curation; Alexandra Igumnova: Investigation; Data curation; Rafael Gisbert-Criado: Investigation; Data curation; Laura Sahuquillo-Frías: Supervision; Resources; Begoña Laiz-Marro: Supervision; Resources. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: We declare the use of ChatGPT (OpenAI) to support English grammar and style revision during the manuscript preparation.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: The training dataset, which consists of fully anonymized data, is available in the GitHub repository (link provided in the Discussion section). The remaining data are available from the corresponding author (JTC) upon reasonable request.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0564).
© 2025 Walter de Gruyter GmbH, Berlin/Boston
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- The European Health Data Space: challenges and opportunities for laboratory medicine
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- Saliva sampling matters for salivary diagnostics of viral infections
- Opinion Papers
- Mass spectrometry based precision diagnostics: on the cusp of selective testing
- Supporting trend detection in the cumulative display of electronic laboratory reports from multiple laboratories while preserving measurement provenance
- Secondary use of external quality assessment data – estimating inter-assay variation in LOINC-coded datasets
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- Pitfalls of immunoassays for diagnosis of hypoglycemia of undetermined etiology
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