Chen Qingqing, Huang Guoding, Xia Yaowen, Zhao Hongmei, Zheng Yu, Liao Yiyi
Department of Pharmacy, Hainan West Central Hospital, Danzhou, Hainan, China.
Department of Oncology, Hainan West Central Hospital, Danzhou, Hainan, China.
BMC Cancer. 2025 Jan 10;25(1):59. doi: 10.1186/s12885-025-13427-2.
To identify the factors influencing pyrotinib-induced severe diarrhea and to establish a risk prediction nomogram model.
The clinical data of 226 patients received pyrotinib from two medical institutions from January 2019 to December 2023 were analysed retrospectively. A training set was made up of 167 patients from Hainan Cancer Hospital, and the external validation set was made up of 59 patients from Hainan West Central Hospital. Univariate and multivariate logistic regression analysis were used to identify independent factors influencing pyrotinib-induced severe diarrhea, and a risk prediction nomogram model was constructed, which was verified on patients in the external validation set.
History of adverse reactions (ADRs), initial dose of pyrotinib, combination with capecitabine, thrombocytopenia, aspartate transaminase (AST), and use of probiotics or other drugs that regulate the gut microbiota were identified as independent influencing factors for pyrotinib-induced severe diarrhea (all P < 0.05). Based on these, a risk prediction nomogram model of pyrotinib-induced severe diarrhea was established. The area under the receiver operating characteristic curve was 0.794 and 0.863 in the training set and the external validation set, respectively. The calibration curve of the prediction model displayed good consistency both the two sets, which indicated that the model could have favourable predictive ability.
The risk prediction nomogram model of pyrotinib-induced severe diarrhea constructed in this study may identify high risk populations earlier so that clinicians can make appropriate decisions in time.
识别影响吡咯替尼所致严重腹泻的因素并建立风险预测列线图模型。
回顾性分析2019年1月至2023年12月期间来自两家医疗机构的226例接受吡咯替尼治疗患者的临床资料。训练集由海南省肿瘤医院的167例患者组成，外部验证集由海南西部中心医院的59例患者组成。采用单因素和多因素logistic回归分析确定影响吡咯替尼所致严重腹泻的独立因素，并构建风险预测列线图模型，在外部验证集患者中进行验证。
不良反应史、吡咯替尼初始剂量、联合卡培他滨、血小板减少、天门冬氨酸氨基转移酶（AST）以及使用益生菌或其他调节肠道微生物群的药物被确定为吡咯替尼所致严重腹泻的独立影响因素（均P<0.05）。基于此，建立了吡咯替尼所致严重腹泻的风险预测列线图模型。训练集和外部验证集的受试者工作特征曲线下面积分别为0.794和0.863。预测模型的校准曲线在两组中均显示出良好的一致性，表明该模型具有良好的预测能力。
本研究构建的吡咯替尼所致严重腹泻风险预测列线图模型可更早识别高危人群，以便临床医生及时做出适当决策。
