A predictive model for therapy failure in patients with chronic myeloid leukemia receiving tyrosine kinase inhibitor therapy

Abstract

Although tyrosine kinase inhibitor (TKI) therapy has markedly improved the survival of people with chronic-phase chronic myeloid leukemia (CML), 20% to 30% of people still experienced therapy failure. Data from 1955 consecutive patients with chronic-phase CML diagnosed by the European LeukemiaNet recommendations from 1 center receiving initial imatinib or a second-generation (2G) TKI therapy were interrogated to develop a clinical prediction model for TKI-therapy failure. This model was subsequently validated in 3454 patients from 76 other centers. Using the predictive clinical covariates associated with TKI-therapy failure, we developed a model that stratified patients into low-, intermediate- and high-risk subgroups with significantly different cumulative incidences of therapy failure (P < .001). There was good discrimination and calibration in the external validation data set, and the performance was consistent with that of the training data set. Our model had the better prediction discrimination than the Sokal and European Treatment and Outcome Study long-term survival scores, with the greater time-dependent area under the receiver-operator characteristic curve values and a better ability to redefine the risk of therapy failure. Our model could help physicians estimate the likelihood of initial imatinib or 2G TKI-therapy failure in people with chronic-phase CML.

Graphical abstract

Figure 1.

Study flowchart.

Figure 2.

Cumulative incidences of the TKI-therapy failure and probabilities of FFS using the predictive model.

Figure 3.

Cumulative incidences of the TKI-therapy failure in the imatinib and 2G-TKI cohorts using the predictive model.

Figure 4.

Comparison of the predictive discrimination for therapy failure among Sokal, ELTS, and the therapy-failure predictive models.