An open-source, expert-designed decision tree application to support accurate diagnosis of myeloid malignancies

A novel algorithmic approach to generate consensus treatment guidelines in adult acute myeloid leukaemia

Induction therapy for acute myeloid leukaemia (AML) has changed with the approval of a number of new agents. Clinical guidelines can struggle to keep pace with an evolving treatment and evidence landscape and therefore identifying the most appropriate front-line treatment is challenging for clinicians. Here, we combined drug eligibility criteria and genetic risk stratification into a digital format, allowing the full range of possible treatment eligibility scenarios to be defined. Using exemplar cases representing each of the 22 identified scenarios, we sought to generate consensus on treatment choice from a panel of nine aUK AML experts. We then analysed >2500 real-world cases using the same algorithm, confirming the existence of 21/22 of these scenarios and demonstrating that our novel approach could generate a consensus AML induction treatment in 98% of cases. Our approach, driven by the use of decision trees, is an efficient way to develop consensus guidance rapidly and could be applied to other disease areas. It has the potential to be updated frequently to capture changes in eligibility criteria, novel therapies and emerging trial data. An interactive digital version of the consensus guideline is available.