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Personalised Treatment Trials:
Monitoring and Improving the Connection of Treatments to Patients

The study will deeply characterise patients before, during and after treatments are trialled through their General Practitioner (GP). Producing large scale data in combination with treatment outcome will be used to produce a predictive algorithm to improve the connection of patients to appropriate treatment options sooner.

  • Natalie Thomas, PhD
  • David Fineberg, MBBS, FRACGP, DCH
  • Neil McGregor, PhD
  • Michael Menden, PhD
  • Christopher Armstrong, PhD
  • IRB/Ethics commenced.
  • GP recruited as a PhD student.
  • Building out GP network across Victoria, needing 30 GPs to attract a large enough cohort for the study.
  • Ethics application approved for part 1 of the trial, part 2 and 3 ethics are being put together.
  • Part 1 is a pilot trial to assess clinical measures of value across a food test.
  • Developing collaborations with large biostatistics groups.
STUDY HYPOTHESIS AND DESCRIPTION

We’re conducting a study to closely watch how different treatments affect ME/CFS patients, using a very tailored approach. We’ll collect samples from patients before, during, and after their treatment to see what biological changes happen and how these relate to whether they feel better or not. This method will help us figure out which treatments work, why they work, and who they work best for.

We plan to study 200 patients over three years. Each patient will be part of the study for about 3 to 4 months, during which they will try 2 to 3 different treatments. This includes three visits to the clinic and involves analysing many samples they provide, like blood and urine. This large collection of data from all the patients will help us make better predictions about which treatments are most likely to help others in the future.

OBJECTIVES

  1. Pattern biology that corresponds to symptom/disease changes induced by treatment.
  2. Sample blood before and after exertion as a measure of improved exertion tolerance.
  3. Cluster patients based on biology-symptom dynamic and identify effective treatments.

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