Background: Chemotherapy-induced thrombocytopenia is often a use-limiting adverse reaction to gemcitabine and cisplatin (GC) combination chemotherapy, reducing therapeutic intensity, and, in some cases, requiring platelet transfusion.
Objective: A retrospective cohort study was conducted on patients with urothelial cancer at the initiation of GC combination therapy and the objective was to develop a prediction model for the incidence of severe thrombocytopenia using machine learning.
Methods: We performed receiver operating characteristic analysis to determine the cut-off values of the associated factors. Multivariate analyses were conducted to identify risk factors associated with the occurrence of severe thrombocytopenia. The prediction model was constructed from an ensemble model and gradient-boosted decision trees to estimate the risk of an outcome using the risk factors associated with the occurrence of severe thrombocytopenia.
Results: Of 186 patients included in this study, 46 (25%) experienced severe thrombocytopenia induced by GC therapy. Multivariate analyses revealed that platelet count ≤ 21.4 (×104/µL) [odds ratio 7.19, p < 0.01], hemoglobin ≤ 12.1 (g/dL) [odds ratio 2.41, p = 0.03], lymphocyte count ≤ 1.458 (×103/µL) [odds ratio 2.47, p = 0.02], and dose of gemcitabine ≥ 775.245 (mg/m2) [odds ratio 4.00, p < 0.01] were risk factors of severe thrombocytopenia. The performance of the prediction model using these associated factors was high (area under the curve 0.76, accuracy 0.82, precision 0.68, recall 0.50, and F-measure 0.58).
Conclusions: Platelet count, hemoglobin level, lymphocyte count, and gemcitabine dose contributed to the development of a novel prediction model to identify the incidence of GC-induced severe thrombocytopenia.
© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.