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Development and validation of a risk prediction model for acute chemotherapy-induced nausea and vomiting in pediatric
Luyan Yu1, Yiheng Wu2, Nan Lin3
1Department of Neurosurgery, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Insights
This study developed a risk prediction model for chemotherapy-induced nausea and vomiting (CINV) in pediatric cancer patients. The model identifies key predictors, enabling personalized interventions to reduce CINV occurrence in children.
Area of Science:
- Pediatric Oncology
- Clinical Pharmacology
- Biostatistics
Background:
- Acute chemotherapy-induced nausea and vomiting (CINV) is highly prevalent in pediatric cancer patients (80-95%).
- Existing risk prediction models for CINV are scarce for pediatric populations, despite distinct risk patterns from adults.
- There is a need for tools to guide CINV prophylaxis and reduce its occurrence in children.
Purpose of the Study:
- To develop and validate a risk prediction model for acute CINV in pediatric cancer patients.
- To provide a clinical tool for predicting CINV risk and guiding personalized prophylaxis.
- To reduce the incidence and adverse events of CINV in pediatric oncology.
Main Methods:
- A cohort of 378 hospitalized children undergoing chemotherapy was analyzed.
- Multivariate logistic regression identified predictors including prior CINV, body weight, mood, chemotherapy cycle, drug emetogenicity, sleep, and tumor type.
- Model performance was evaluated using nomograms, ROC curves, calibration, and decision curves, with external validation on 230 patients.
Main Results:
- Independent predictors for chemotherapy-induced nausea (CIN) included prior CINV, body weight, and negative emotions.
- Predictors for chemotherapy-induced vomiting (CIV) included chemotherapy cycle, drug emetogenicity, sleep duration, tumor type, and prior CINV experience.
- The developed nomograms demonstrated good predictive performance for both CIN and CIV.
Conclusions:
- This is the first risk prediction model for CINV specifically developed for pediatric cancer patients.
- The validated models offer a practical tool for healthcare professionals to predict CINV risk.
- Timely and personalized interventions can be implemented to prevent CINV and mitigate associated adverse events in children.
Background:
Acute chemotherapy-induced nausea and vomiting (CINV) affects 80-95% of pediatric cancer patients, with distinct risk patterns from adults, yet few risk prediction models exist for this population. We aimed to develop and validate a prediction model for acute CINV in pediatric patients with cancers, providing a tool to guide the clinical implementation of CINV prophylaxis and reduce CINV occurrence in children.
Methods:
A total of 378 hospitalized children who underwent chemotherapy at the Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China, between August 1, 2022, and March 31, 2023, were enrolled. Demographic, disease-related, and chemotherapy-related factors were collected using a self-developed questionnaire. Multivariate logistic regression was employed to identify predictors for the model. Nomograms, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses were used to evaluate model performance. External validation was conducted on 230 patients treated at the Children's Hospital, Zhejiang University School of Medicine from 1 May to 31 August 2023.
Results:
Independent predictors of chemotherapy-induced nausea (CIN) included prior CINV experience, body weight, and negative emotions or mood changes during chemotherapy. Predictors of chemotherapy-induced vomiting (CIV) included chemotherapy cycle count, emetogenicity risk grade of chemotherapy drugs, adequate sleep duration, tumor type, and prior CINV experience. The nomogram parameters, along with ROC, calibration, and decision curves demonstrated good predictive performance for both CIN and CIV.
Conclusions:
This is the first study to develop a risk prediction model for CINV among pediatric cancer patients. The prediction models were relatively fit. It provides clinical healthcare professionals with an effective and easy-to-use tool for predicting the risk of having CINV; thus, they could provide timely and personalized interventions to prevent CINV and reduce adverse events associated with CINV before chemotherapy.
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