Newly developed statistical models can help to predict the risk of early death in patients being treated with non-intensive therapies for acute myeloid leukaemia (AML).
The 2021 American Society of Hematology annual meeting heard that researchers first built the models using data from the LI-1 clinical trial of 796 patients, whose median age was 75, which was conducted by the UK Medical Research Council/National Cancer Research Institute (MRC/NCRI).
They then went on to validate the accuracy of their models against data from another 540 patients, whose median age was 77, who had enrolled in three leukaemia clinical trials conducted by the SWOG Cancer Research Network.
The models use a patient’s age and measures of their health, such as performance status, white blood cell and platelet counts, the presence or absence of a specific genetic mutation (in the gene NPM1), and several self-reported measures of the patients’ quality of life.
The group found that, although all their models were only modestly successful in predicting early death in both the MRC/NCRI trial patients and the SWOG trial patients, the most effective models were those that incorporated several quality-of-life scores from the QLQ-C30 instrument, a widely used patient survey.
The researchers say their results demonstrate how difficult it is to predict outcomes for patients receiving non-intensive therapy using only routinely available clinical information.
Dr Megan Othus, of the Fred Hutchinson Cancer Research Center, Seattle, WA, USA, said: “Making predictive models that are more accurate and useful may require incorporating information from additional blood and toxicity biomarkers collected in the early stages of a patient’s treatment or information from additional patient-reported outcome measures.
“To our knowledge, this is the first attempt to build tools to predict outcomes for patients receiving non-intensive AML therapy to inform decision-making and, ultimately, improve treatment outcomes.”
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