Researchers have for the first time developed statistical models to predict outcomes of patients receiving non-intensive therapies for acute myeloid leukemia (AML) – a development that could eventually help in informed decision making and ultimately improve treatment outcomes.
When it comes to acute myeloid leukemia, generally the intensive chemotherapy is chosen and models are already present to predict the outcome of patients from this therapy. However, intensive therapies do not work well with patients aged 75 and above and specifically those with other illenesses may not be able to tolerate the side effects of intensive chemotherapy and are therefore treated with alternative less intensive therapies.
Before the latest study and research, there were no tools to accurately predict which patients are likely to benefit most from these non-intensive therapies.
To address this lack, the researchers developed and tested several statistical models that predicted the risk of early death for AML patients on non-intensive therapies. They initially built the models using data from the LI-1 clinical trial of 796 patients (median age 75), which was conducted by the MRC/NCRI. The team then validated the accuracy of their models against data from another 540 patients (median age 77) who had enrolled in three leukemia clinical trials conducted by the SWOG Cancer Research Network – S0432, S0703, and S1612.
The models use a patient’s age and a variety of measures of the patient’s health, such as performance status (a measure of how well the patient can perform ordinary tasks), white blood cell and platelet counts, the presence or absence of a specific genetic mutation (NPM1), and several self-reported measures of the patient’s 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 a widely used patient-reported outcome survey known as the QLQ-C30 instrument.
The researchers say their results highlight the difficulties in predicting outcomes for these patients using only routinely available clinical information.
