Development and validation of a machine learning model for accurate detection of wrong blood in tube errors in hospitalized patients.

Autores de IIS La Fe

• Jordi Tortosa Carreres

  Autor

• Ana Vaño Bellver

  Autor

• Andreu Martínez Cerezuela

  Autor

• Oscar Fuster Lluch

  Autor

• Lucia Garcia Ruiz

  Autor

• Elena Rodriguez Romero

  Autor

• Carlos Catedra Lopez

  Autor

• Virginia Tadeo Garisto

  Autor

• Aleksandra Igumnova

  Autor

• María Begoña Laiz Marro

  Autor

Participantes ajenos a IIS La Fe

• Sierra-Rivera A
• Comes-Raga A
• Gisbert-Criado R
• Sahuquillo-Frias L

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.

© 2025 Walter de Gruyter GmbH, Berlin/Boston.

Keywords

• delta check; machine learning; preanalytical errors; wrong blood in tube