A Machine Learning Model for the Routine Detection of “Wrong Blood in Complete Blood Count Tube” Errors

Abstract

Abstract Background Current laboratory procedures may fail to detect wrong blood in tube (WBIT) errors. Machine learning models have the potential to improve WBIT error detection, as demonstrated by proof-of-concept studies. The models developed so far, however, are not appropriate for routine use because they are unable to handle missing values and have low positive predictive value (PPV). In this study, a machine learning model suitable for routine use was developed. Methods A model was trained and a preliminary evaluation performed on a retrospective data set of 135 128 current and previous patient complete blood count (CBC) results. The model was then applied prospectively to routine samples tested in a public hospital laboratory over a period of 22 weeks. Each week, the 5 samples identified by the model as most likely to be WBIT errors underwent further investigation by testing blood group and red cell phenotype. The study assessed the number of WBIT errors that were missed by current procedures but detected by the model, as well as the PPV of the model. Results The model was applied prospectively to 38 187 CBC results that had passed routine laboratory checks. One hundred and ten samples were identified for further testing and 12 WBIT errors were detected. The PPV of the model was 10.9%. Conclusion A machine learning model suitable for routine use was able to identify WBIT errors missed by the laboratory’s current procedures. Machine learning models are valuable for the identification of WBIT errors, and their validation and deployment in clinical laboratories would improve patient safety.