Generation of consensus guidelines in AML

• Research type

  Research Study

• Full title

  Algorithmic approach to generate consensus guidelines in acute myeloid leukaemia

• IRAS ID

  302973

• Contact name

  Thomas Coats

• Contact email

  thomas.coats@nhs.net

• Sponsor organisation

  Royal Devon & Exeter NHS Foundation Trust

• Duration of Study in the UK

  0 years, 5 months, 21 days

• Research summary

  Deciding on the most effective treatment for a newly diagnosed patient with AML can be difficult. The eligibility criteria for different drugs on the NHS are complex and the results from clinical trials are evolving all of the time. Clinical guidelines are a great resource for doctors to use, but usually take years to be produced and can therefore become out of date quickly.
  To solve this problem, we have created a series of algorithms using a computer programme that establishes whether a patient is eligible for each of the AML drugs. The algorithms require routinely available clinical and genetic information to run. Using the same information, the computer programme can also assign a patient to a prognostic group based on published guidelines, which gives information about how likely their disease is to come back after treatment. Combining these algorithms has enabled us to create a framework for categorising different patients according to what drugs they are eligible for, combined with how high risk their disease is.
  Using artificially generated cases we have previously surveyed UK experts as to their preferred treatment choice in each of the different scenarios identified by our computer programme. Where there is agreement between most of the experts (e.g. 75% agreement), this constitutes a consensus recommendation. This is a novel approach to rapidly create consensus guidelines. To confirm whether these scenarios exist in real life, we will analyse clinical data from a large cohort of patients using the same computer programme. This will identify the most commonly occurring scenarios where a consensus does not exist – and could inform future research - and establish how effective is our novel approach to creating consensus guidelines.

• REC name

  N/A

• REC reference

  N/A