The predictive model for risk of chemotherapy-induced thrombocytopenia based on antineoplastic drugs for solid tumors in eastern China

Abstract

Abstract Chemotherapy-related thrombocytopenia (CIT) is a significant adverse event during chemotherapy, which can lead to reduced relative dose intensity, increased risk of serious bleeding and additional medical expenditure. Herein, we aimed to develop and validate a predictive nomogram model for prediction of CIT in patients with solid tumor. From Jun 1, 2018 to Sep 9, 2021, a total of 1541 patients who received 5750 cycles of chemotherapy were retrospectively enrolled. Cox regression analysis was performed to identify predictive factors to establish the nomogram model for CIT. The incidence of chemotherapy-induced thrombocytopenia was 21.03% for patient-based and 10.26% for cycles of chemotherapy. The top five solid tumors with CIT are cervix, gastric, bladder, biliary systemic, and ovarian. The incidence of chemotherapy dose delays in any cycle because of CIT was 5.39%. Multivariate analysis showed that tumor site, treatment line, AST, oxaliplatin, and capecitabine were significantly associated with CIT. Moreover, we established a nomogram model for CIT probability prediction, and the model was well calibrated (Hosme-Lemeshow P = 0.230) and the area under the receiver operating characteristic curve was 0.844 (Sensitivity was 0.625, Specificity was 0.901). We developed a predictive model for chemotherapy-induced thrombocytopenia based on readily available and easily assessable clinical characteristics. The predictive model based on clinical and laboratory indices represents a promising tool in the prediction of CIT, which might complement the clinical management of thrombocytopenia.