Development and validation of a risk prediction model for acute chemotherapy-induced nausea and vomiting in pediatric patients with cancers.

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.