Machine Learning-based Prediction of Postoperative Nausea and Vomiting after Spinal Anesthesia: A Retrospective Observational Study

Author:

Hoshijima Hiroshi1,Miyazaki Tomo2,Omachi Shinichiro2,Konno Daisuke3,Sugino Shigekazu3,Yamauchi Masanori3,Shiga Toshiya4,Mizuta Kentaro1

Affiliation:

1. Tohoku University Graduate School of Dentistry

2. Tohoku University

3. Tohoku University School of Medicine

4. International University of Health and Welfare Ichikawa Hospital

Abstract

Abstract

Purpose In this study, we apply analysis using artificial intelligence to identify risk factors for Postoperative nausea and vomiting (PONV) during surgery under spinal anesthesia. Methods This retrospective study used artificial intelligence to analyze data of adult patients (aged ≥ 20 years) who underwent surgery under spinal anesthesia. To evaluate PONV, patients who experienced nausea and/or vomiting or used antiemetics within 24 hours after surgery were extracted from postoperative medical records. We create a model that predicts probability of PONV using the gradient tree boosting model. The model implementation used the LightGBM framework. Results Data were available for 4,574 patients. The identified risk factors were duration of surgery, female, no blood transfusion, spinal level 3–4 puncture, no concomitant epidural anesthesia, use of propofol, and dexmedetomidine, postoperative fentanyl use, cesarean section, and not using phenylephrine, atropine, or oxytocin. Conclusions We used artificial intelligence to evaluate the extent to which risk factors for PONV contribute to the development of PONV. We identifies female and cesarean section, which are known risk factors for PONV after surgery under spinal anesthesia. Our findings also suggest that fluid volume, blood transfusion, and agents that normalize hemodynamics, such as phenylephrine and atropine, are important in preventing PONV. Trial registration number: UMIN000050012

Publisher

Springer Science and Business Media LLC

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