Journal Article

Dosing Strategy of Ramosetron to Prevent Postoperative Nausea and Vomiting and Development of Prediction Models Using Data Obtained From Randomized Controlled Trials: A Comparative Study

Noh, Gyu-Jeong, Kim, Eunha, Bang, Ji-Yeon, Kim, Kyung Mi, Kim, Dong Ho, Lee, Eun-Kyung, Choi, Byung-Moon

2024

Overview

•An additional intravenous dose of ramosetron once daily until the second-day post-surgery did not reduce the incidence of PONV in female patients undergoing breast surgery compared to the standard intravenous dose (0.3 mg) of ramosetron administered only once immediately before the end of surgery.•In predicting the occurrence of PONV, the Apfel model showed high sensitivity; however, its specificity and accuracy were lower than ML-based models.•In an adjusted analysis using a logistic regression model, the history of PONV or motion sickness and ASA were significant risk factors for PONV, and the odds ratio was high in this order. The study aimed to compare the postoperative nausea and vomiting (PONV) preventive effect of repeated administration of ramosetron with the standard treatment group and compare models to predict the incidence of PONV using machine-learning techniques. A total of 261 patients scheduled for breast surgery were analyzed to evaluate the effectiveness of repeated intravenous administration of ramosetron. All patients were administered 0.3 mg ramosetron just before the end of surgery. For the repeated dose of ramosetron group, an additional dose of 0.3 mg was administered at 4, 22, and 46 hours after the end of the surgery. Postoperative nausea, vomiting, and retching were evaluated using the Rhodes Index of Nausea, Vomiting, and Retching at 6, 24, and 48 hours postoperatively. Previously published randomized controlled data were combined with the data of this study to create a new dataset of 1390 patients, and machine-learning–based PONV prediction models (classification tree, random forest, extreme gradient boosting, and neural network) was constructed and compared with the Apfel model. Fifty patients (38.5%) and 60 patients (45.8%) reported nausea, vomiting, or retching 48 hours postoperatively in the standard and repeated-dose groups, respectively (P = 0.317, χ2 test). Median sensitivity, specificity, and accuracy of the Apfel model analyzed using the training set were 0.815, 0.344, and 0.495, respectively. The repeated administration of ramosetron did not reduce the incidence of PONV. The Apfel model had high sensitivity, however, its specificity and accuracy were lower than that in machine-learning–based models.

• Publisher: Elsevier Inc,Elsevier Limited
• Subject: Anesthesia / Antiemetics / Breast surgery / Chemotherapy / Clinical trials / Dosage / Internal Medicine / Intravenous administration / Learning algorithms / Machine learning / Nausea / Neural networks / Pain / Patients / Prediction / Prediction models / Ramosetron / Sensitivity analysis / Surgery / Vomiting