A machine learning-based prediction model for delayed clinically important postoperative nausea and vomiting in high-risk patients undergoing laparoscopic gastrointestinal surgery

Delayed clinically important postoperative nausea and vomiting (CIPONV) could lead to significant consequences following surgery. We aimed to develop a prediction model for it using machine learning algorithms utilizing perioperative data from patients undergoing laparoscopic gastrointestinal surger...

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Veröffentlicht in:The American journal of surgery 2024-11, Vol.237, p.115912, Article 115912
Hauptverfasser: Zheng, Zhinan, Huang, Yabin, Zhao, Yingyin, Shi, Jiankun, Zhang, Shimin, Zhao, Yang
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container_start_page 115912
container_title The American journal of surgery
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creator Zheng, Zhinan
Huang, Yabin
Zhao, Yingyin
Shi, Jiankun
Zhang, Shimin
Zhao, Yang
description Delayed clinically important postoperative nausea and vomiting (CIPONV) could lead to significant consequences following surgery. We aimed to develop a prediction model for it using machine learning algorithms utilizing perioperative data from patients undergoing laparoscopic gastrointestinal surgery. All 1154 patients in the FDP-PONV trial were enrolled. The optimal features for model development were selected by least absolute shrinkage and selection operator and stepwise regression from 81 perioperative variables. The machine learning algorithm with the best area under the receiver operating characteristic curve (ROCAUC) was determined and assessed. The interpretation of the prediction model was performed by the SHapley Additive Explanations library. Six important predictors were identified. The random forest model showed the best performance in predicting delayed CIPONV, achieving an ROCAUC of 0.737 in the validation cohort. This study developed an interpretable model predicting personalized risk for delayed CIPONV, aiding high-risk patient identification and prevention strategies. •Delayed clinically important PONV is linked to predisposing and precipitating factors.•The prediction model for delayed clinically important PONV was developed.•The model included six simple variables and was visualized by SHAP.
doi_str_mv 10.1016/j.amjsurg.2024.115912
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subjects Adult
Aged
Algorithms
Artificial intelligence
Datasets
Delayed clinically important postoperative nausea and vomiting
Digestive System Surgical Procedures - adverse effects
Female
Gastrointestinal surgery
Humans
Laparoscopy
Laparoscopy - adverse effects
Learning algorithms
Machine Learning
Male
Middle Aged
Missing data
Nausea
Patients
Performance evaluation
Performance prediction
Postoperative nausea and vomiting
Postoperative Nausea and Vomiting - epidemiology
Postoperative Nausea and Vomiting - prevention & control
Prediction model
Prediction models
Predictor
Regression models
Risk
Risk Assessment - methods
Risk Factors
Risk groups
ROC Curve
Surgery
Variables
Vomiting
title A machine learning-based prediction model for delayed clinically important postoperative nausea and vomiting in high-risk patients undergoing laparoscopic gastrointestinal surgery
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