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Intravenous immunoglobulin weaning evaluation with zero-shot large language model classification.

Emily Burton1,2,3,4, Shrirajh Satheakeerthy2,4, Rudy Goh1,2,3,4

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Transfusion Medicine (Oxford, England)
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A large language model (LLM) demonstrated 78.6% accuracy in identifying patients suitable for weaning off intravenous immunoglobulin (IVIg). This AI approach could help optimize resource use and reduce adverse effects associated with IVIg therapy.

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Area of Science:

  • Neurology
  • Artificial Intelligence in Medicine
  • Health Informatics

Background:

  • Intravenous immunoglobulin (IVIg) therapy is resource-intensive and carries potential adverse effects.
  • Weaning patients from IVIg is crucial for optimizing healthcare resource utilization and mitigating risks.

Purpose of the Study:

  • To evaluate the performance of a large language model (LLM) using a zero-shot strategy for classifying factors related to IVIg weaning.
  • To assess the accuracy of an LLM in identifying patients eligible for IVIg discontinuation.

Main Methods:

  • A cohort study of neurology outpatients receiving regular IVIg was conducted.
  • Prespecified criteria were applied to identify potential candidates for IVIg weaning.
  • A LLM was utilized to classify extracted patient data against these criteria.

Main Results:

  • Four out of 14 identified patients met the criteria for possible IVIg weaning.
  • The LLM achieved an overall classification accuracy of 78.6% (11/14) when applying a rule-based approach to extracted clinical notes.
  • Potential annual cost savings of $84,702.20 were estimated from IVIg weaning.

Conclusions:

  • Further research is needed to determine the prevalence of patients suitable for IVIg weaning across different centers.
  • Investigating the extent to which LLMs can assist in identifying patients for IVIg weaning is warranted.