ArticleBMC cancer2025
Development of a risk prediction nomogram model of pyrotinib-induced severe diarrhea.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Towards a prescribing monitoring system for medication safety evaluation within electronic health records: a scoping review.BMC medical informatics and decision making · 2025Article
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6 authors.
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Abstract
backgroundTo identify the factors influencing pyrotinib-induced severe diarrhea and to establish a risk prediction nomogram model.
methodsThe 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.
resultsHistory 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.
conclusionThe 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.
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