Machine learning-based prediction of postoperative nausea and vomiting after spinal anesthesia: A retrospective observational study.

Authors

Hiroshi Hoshijima, Tomo Miyazaki, Shinichiro Omachi, Daisuke Konno, Shigekazu Sugino, Masanori Yamauchi, Toshiya Shiga, Kentaro Mizuta

Published in

PloS one. Volume 21. Issue 8. Pages e0333162. Epub Aug 05, 2026.

Abstract

Postoperative nausea and vomiting (PONV) is a frequent and serious complication after surgery. PONV also reduces patient satisfaction with surgery under spinal anesthesia and increases medical costs due to prolonged hospitalization. The purpose of this study is to apply artificial intelligence (AI) machine learning analysis to identify risk factors for PONV in patients undergoing surgery with spinal anesthesia. This retrospective study used artificial intelligence to analyze data of adult patients (aged ≥20 years) who underwent surgery under spinal anesthesia at Tohoku University Hospital from January 1, 2010 to December 31, 2022. To evaluate PONV, patients who experienced nausea and/or vomiting or used antiemetics within 24 hours after surgery were extracted from postoperative medical records. The selected data were analyzed after propensity score matching with patients who did not experience PONV. We created an ensemble model for predicting the probability of PONV using five machine learning algorithms: random forest, gradient boosting machine, k-nearest neighbor, multilayer perceptron, and decision tree. Data were available for 4,574 patients. We performed propensity score matching and selected 538 patients for analysis (269 in the PONV group and 269 in the non-PONV group). The use of postoperative fentanyl was identified as the strongest contributor to PONV, followed by duration of surgery, body mass index (BMI), total urine output, and duration of anesthesia. The identified risk factors were female sex, BMI < 25 kg/m2, and duration of surgery (< 60 min), duration of anesthesia (< 100 min), cesarean section, use of postoperative fentanyl, administration of fentanyl/ morphine into the spinal arachnoid, and puncture level of epidural anesthesia (Th7-12) were identified as anesthesia/surgery-related risk factors. We used machine learning AI to evaluate risk factors for PONV after spinal anesthesia. We identified several patient-related and anesthesia/surgery-related risk factors for PONV. The AI analysis used in this study was accurate enough to identify risk factors for PONV during spinal anesthesia and may provide sufficient evidence for predicting PONV.

PMID:
42555606
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.

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