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

Christopher-John Farrell1, Charles Makuni2, Aaron Keenan2

  • 1Clinical Chemistry Department, NSW Health Pathology-Liverpool Hospital, Sydney, Australia.

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|July 20, 2023
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Summary
This summary is machine-generated.

A new machine learning model effectively detects wrong blood in tube (WBIT) errors missed by current lab procedures. This advancement in laboratory diagnostics enhances patient safety by improving WBIT error identification.

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Area of Science:

  • Clinical diagnostics
  • Medical laboratory science
  • Artificial intelligence in healthcare

Background:

  • Current laboratory methods may not detect wrong blood in tube (WBIT) errors.
  • Existing machine learning models for WBIT detection have limitations, including handling missing data and low positive predictive value (PPV).
  • A need exists for a machine learning model suitable for routine clinical laboratory use.

Purpose of the Study:

  • To develop and evaluate a machine learning model for routine detection of wrong blood in tube (WBIT) errors.
  • To assess the model's ability to identify WBIT errors missed by conventional laboratory procedures.
  • To determine the positive predictive value (PPV) of the developed machine learning model.

Main Methods:

  • A machine learning model was trained on a retrospective dataset of 135,128 complete blood count (CBC) results.
  • The model was prospectively applied to routine laboratory samples over 22 weeks.
  • Samples flagged by the model underwent further investigation, including blood group and red cell phenotype testing.

Main Results:

  • The model was prospectively applied to 38,187 CBC results that passed routine checks.
  • 110 samples were identified for further testing, leading to the detection of 12 wrong blood in tube (WBIT) errors.
  • The positive predictive value (PPV) of the machine learning model was 10.9%.

Conclusions:

  • A machine learning model suitable for routine use can identify wrong blood in tube (WBIT) errors missed by current laboratory procedures.
  • Machine learning offers a valuable tool for enhancing WBIT error detection in clinical laboratories.
  • Validation and deployment of such models can significantly improve patient safety.