Comparing therapeutic effects across tyrosine kinase inhibitors: Chronic myeloid leukemia outcomes and analysis of influencing factors

Abstract

This study aims to comprehensively assess the effects of imatinib, nilotinib, and flumatinib in treating chronic myeloid leukaemia and to explore the main factors affecting its efficacy. Ninety-nine chronic myeloid leukaemia patients initially diagnosed and treated with one of these 3 tyrosine kinase inhibitors at a tertiary hospital in Shanxi Province between June 2018 and June 2023 were selected and divided into an imatinib group (n = 32), nilotinib group (n = 30), and flumatinib group (n = 37). Hematological response rates, cytogentic response rates, molecular response rates, and adverse reactions were compared among the 3 groups to statistically analyze efficacy and safety. Univariate analysis and logistic regression were used to explore the related factors influencing the curative effect. A nomogram prediction model of influencing factors of efficacy was constructed in R software and validated according to receiver operating characteristic and calibration curves, with a clinical decision curve and clinical impact curve further drawn to confirm its clinical practicability. The complete cytogenetic response at 3 months differed significantly, with rates of 53.13%, 76.67%, and 78.38% for the imatinib, nilotinib, and flumatinib groups, respectively (P < .05). Major molecular response (MMR) rates at 3 months were 25.00%, 53.33%, and 51.35%, reaching 78.13%, 90.00%, and 83.78% at 12 months, respectively. Deep molecular response (DMR) rates at 12 months were 50.00%, 76.67%, and 75.68% in each respective group (P < .05). Multivariate logistic regression indicated early molecular response, white blood cell count, red cell distribution width and platelet count as independent influencing factors of MMR. Age, drug type, early early molecular response, and red cell distribution width were identified as independent influencing factors of DMR (P < .05). The areas under the receiver operating characteristic curves for MMR and DMR nomogram models were 0.912(95% confidence interval: 0.833, 0.990)and 0.874 (95% confidence interval: 0.801, 0.946), respectively, indicating satisfactory model calibration. Nilotinib and flumatinib demonstrate superior efficacy over imatinib, with effectiveness influenced by various factors including sociodemographic characteristics, clinical heterogeneity, and drug side effects. The proposed clinical prediction model may provide valuable insights for decision-making and demonstrates generalizability and practical application value.

Keywords: chronic myeloid leukaemia; efficacy; flumatinib; imatinib; influencing factors; nilotinib.

Figure 1.

Nomogram prediction model of MMR. MMR = major molecular response.

Figure 2.

Nomogram prediction model of DMR. DMR = deep molecular response.

Figure 3.

Model fit assessments of MMR. (A) Receiver operating characteristic. (B) Calibration curves. (C) Clinical decision curve. (D) Clinical impact curve. MMR = major molecular response.

Figure 4.

Model fit assessments of DMR. (A) Receiver operating characteristic. (B) Calibration curves. (C) Clinical decision curve. (D) Clinical impact curve. DMR = deep molecular response.