Nomogram for Predicting Chemotherapy-Induced Nausea and Vomiting for Breast Cancer Patients
| Autor: | Hong-Yun Chen, Tao Wei, Ling-Qi Meng, Rong-Rong Fan, Yan Tan, Tong-Yu Wang, Lu Luo, Zhe-Yu Hu, Li Jinhua, Xuying Li, Xin-Juan Huang |
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| Rok vydání: | 2021 |
| Předmět: |
Oncology
medicine.medical_specialty Nausea Vomiting Antineoplastic Agents Breast Neoplasms General Biochemistry Genetics and Molecular Biology Breast cancer Internal medicine medicine Humans Prospective Studies business.industry Cancer Reproducibility of Results General Medicine Nomogram medicine.disease Chemotherapy regimen Confidence interval humanities Nomograms Female medicine.symptom business Chemotherapy-induced nausea and vomiting |
| Zdroj: | The Tohoku journal of experimental medicine. 254(2) |
| ISSN: | 1349-3329 |
| Popis: | Purpose: Chemotherapy-induced nausea and vomiting (CINV) is a common side effect of cancer treatment. The factors influencing CINV in breast cancer patients remain unclear. In this study we developed a nomogram for predicting the occurrence of CINV in this group using prospective clinical data from multiple sources Methods: We pooled data from several studies conducted in China on CINV caused by emetogenic chemotherapy. Demographic and clinical variables of 334 breast cancer patients at Hunan Cancer Hospital (training set) were analyzed to identify factors associated with CINV by multivariate logistic regression and construct a prediction nomogram. The external validation set comprised an additional 66 patients. The reliability of the nomogram was assessed by bootstrap resampling, and C-index and receiver operating characteristics curve (ROC) analyses were carried out to assess its discriminatory power. Results: Four risk factors were associated with CINV: history of CINV, chemotherapy target regimen, metastasis, and symptoms of distress. The C-index was 0.69 (95% confidence interval [CI], 0.63–0.75) for the training set and 0.83 (95% CI, 0.73–0.93) for the validation set. The area under the ROC curve indicated that the model had a specificity of 68.5% and 86.1% and sensitivity of 57.7% and 56.7%, for the training and validation sets, respectively. Calibration curves showed good concordance between predicted and actual occurrence of CINV. Conclusions: The developed nomogram can reliably predict the occurrence of CINV in breast cancer patients based on 4 variables, which can aid in clinical decision-making. |
| Databáze: | OpenAIRE |
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