@article{ff3965b371bd4650b7e3d3ce34de3acd,
title = "A novel algorithmic approach to generate consensus treatment guidelines in adult Acute Myeloid Leukaemia",
abstract = "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.",
keywords = "classifications, clinical haematology, diagnostic haematology, myeloid leukaemia",
author = "Thomas Coats and Daniel Bean and Aymeric Basset and Tamir Sirkis and Jonathan Brammeld and Sean Johnson and Ian Thomas and Amanda Gilkes and Kavita Raj and Mike Dennis and Steven Knapper and Priyanka Mehta and Asim Khwaja and Hannah Hunter and Sudhir Tauro and David Bowen and Gail Jones and Dobson, {Richard J.} and Russell, {Nigel H.} and Richard Dillon",
note = "We are grateful to Cancer Research UK (CRUK) for the research funding for the AML17 clinical trial and to Alan Burnett for his work as chief investigator of the trial. DBe holds a UK Research and Innovation (UKRI) Fellowship as part of Health Data Research UK (HDRUK) MR/S00310X/1 RDo is supported by the following: (1) NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London, London, UK; (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust; (3) The BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116074. This Joint Undertaking receives support from the European Union{\textquoteright}s Horizon 2020 research and innovation programme and EFPIA; it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and ESC; (4) the National Institute for Health Research University College London Hospitals Biomedical Research Centre; (5) the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King{\textquoteright}s College London; (6) the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare; (7) the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King{\textquoteright}s College Hospital NHS Foundation Trust.",
year = "2022",
month = mar,
day = "18",
doi = "10.1111/bjh.18013",
language = "English",
volume = "196",
pages = "1337--1343",
journal = "British Journal of Haematology",
issn = "0007-1048",
publisher = "Wiley",
number = "6",
}