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Model-Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation

Jules M Janssen Daalen1, Djoeke Doesburg2, Liesbeth Hunik3

  • 1Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.

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Summary

Model-informed precision dosing (MIPD) using machine learning improves levothyroxine dosing in primary care. This AI tool reduced dosage errors and increased optimal starting doses, enhancing patient safety and treatment effectiveness.

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Area of Science:

  • Pharmacology
  • Artificial Intelligence
  • Clinical Medicine

Background:

  • Levothyroxine is a widely prescribed medication with challenging dosing due to individual variability and a narrow therapeutic window.
  • Current dosing practices in primary care lack advanced decision support, leading to potential under or overdosage.
  • A gap exists between AI developers and clinicians, hindering the adoption of healthcare algorithms.

Purpose of the Study:

  • To develop, validate, and clinically simulate the first model-informed precision dosing (MIPD) application for levothyroxine in primary care.
  • To assess the safety, feasibility, and clinical impact of MIPD compared to traditional dosing methods.
  • To improve the accuracy of initial levothyroxine dosage selection by general practitioners.

Main Methods:

  • A multiclass random forest model was trained and validated on a national primary care database (n=19,004) to predict optimal levothyroxine dosing classes.
  • Key predictive features identified included TSH, fT4, weight, and age.
  • A clinical simulation study involved 51 general practitioners prescribing levothyroxine for 20 cases, with and without MIPD support.

Main Results:

  • The MIPD model achieved a weighted AUC of 0.71 for predicting dosing classes, demonstrating effectiveness even in subclinical hypothyroidism.
  • MIPD significantly reduced overdosage rates (30.5% to 23.9%) and magnitude (median 50 to 37.5 μg).
  • The use of MIPD increased the prescription of optimal starting dosages (18.3% to 30.2%) and GPs considered lab results more frequently.

Conclusions:

  • The developed MIPD application is the first of its kind for levothyroxine in primary care, demonstrating clinical relevance and safety.
  • MIPD effectively assists general practitioners in selecting safer and more optimal starting levothyroxine dosages.
  • The study highlights the potential of AI-driven decision support to enhance precision medicine and improve patient outcomes in routine clinical practice.