Machine Learning-Based Prediction Model for Delayed Chemotherapy-Induced Nausea and Vomiting in Pediatric Cancer: A Prospective Cohort Study.
Delayed chemotherapy-induced nausea and vomiting (CINV) in pediatric oncology patients is currently under-recognized. This study aims to develop, validate, and visualize a machine learning-based model to predict delayed CINV risk in children.
This prospective cohort study was conducted from November 2021 to December 2022 at a tertiary hospital in southern China. Pediatric delayed CINV data were collected via an electronic diary using the Pediatric Nausea Assessment Tool (PeNAT) and National Cancer Institute-Common Terminology Criteria for Adverse Events (NCI-CTCAE) (v4.03), with PeNAT ≥3 or CTCAE grade ≥2 as the primary outcomes. Seven machine learning models, including random forest, support vector machine, and artificial neural network (ANN), were developed and validated using 29 sociodemographic and clinical features. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and other metrics. Shapley's Additive Explanations (SHAP) enhanced interpretability, and the models were integrated into a web-based calculator for visualization.
Overall, 399 pediatric patients (60.4% male; aged 4-18 years) were included. The AUC of the seven models ranged from 0.782 to 0.815, with the ANN model performing best (AUC 0.815; 95% CI, 0.695-0.903). The ANN model's global SHAP plot revealed that the most influential features were acute CINV, days of chemotherapy, age, number of recreational activities, expectancy of CINV, and control effectiveness of CINV. The ANN model was then deployed as a web-based risk calculator for pediatric delayed CINV.
The ANN model demonstrated good performance in identifying children at high risk of delayed CINV. Our web-based calculator provides a reliable tool for clinical staff to support targeted CINV management.
This prospective cohort study was conducted from November 2021 to December 2022 at a tertiary hospital in southern China. Pediatric delayed CINV data were collected via an electronic diary using the Pediatric Nausea Assessment Tool (PeNAT) and National Cancer Institute-Common Terminology Criteria for Adverse Events (NCI-CTCAE) (v4.03), with PeNAT ≥3 or CTCAE grade ≥2 as the primary outcomes. Seven machine learning models, including random forest, support vector machine, and artificial neural network (ANN), were developed and validated using 29 sociodemographic and clinical features. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and other metrics. Shapley's Additive Explanations (SHAP) enhanced interpretability, and the models were integrated into a web-based calculator for visualization.
Overall, 399 pediatric patients (60.4% male; aged 4-18 years) were included. The AUC of the seven models ranged from 0.782 to 0.815, with the ANN model performing best (AUC 0.815; 95% CI, 0.695-0.903). The ANN model's global SHAP plot revealed that the most influential features were acute CINV, days of chemotherapy, age, number of recreational activities, expectancy of CINV, and control effectiveness of CINV. The ANN model was then deployed as a web-based risk calculator for pediatric delayed CINV.
The ANN model demonstrated good performance in identifying children at high risk of delayed CINV. Our web-based calculator provides a reliable tool for clinical staff to support targeted CINV management.
Authors
Deng Deng, Lou Lou, Liu Liu, Zhong Zhong, Wu Wu, Zeng Zeng, Jiang Jiang, Guan Guan, Cai Cai
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