Clinical Trials Logo

Clinical Trial Summary

Postoperative nausea and vomiting (PONV) can lead to serious postoperative complications, but most symptoms are mild. Clinically important PONV (CIPONV) refers to PONV symptoms that have a significant impact on the patient's well-being and recovery. Present predictive systems for PONV are mainly concentrated on early PONV. However, there is currently no suitable prediction model for delayed PONV, particularly delayed CI-PONV. This study aims to develop and validate a prediction model for delayed CI-PONV using machine learning algorithms utilizing perioperative data from patients undergoing laparoscopic gastrointestinal surgery. All 1154 patients in the FDP-PONV trial will be enrolled in this study. Delayed CIPONV is defined as experiencing CIPONV between 25-120 hours after surgery. After selecting the modeling variables from 81 perioperative clinical features, six machine learning models are established to generate the risk prediction models for delayed CIPONV. The area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score and Brier score are used to evaluate the model performance. Shape Additive explanation analysis was conducted to evaluate feature importance.


Clinical Trial Description

The website https://mvansmeden.shinyapps.io/BeyondEPV/ was used for sample size calculation, considering 6 candidate predictors, an event fraction of 0.14, and a criterion value for reduced mean predictive squared error of 0.03. The calculated sample size is 1080, with a minimally required expected event per variable of 25.1. Therefore, a sample size of 1154 patients is deemed sufficient to support the inclusion of 6 predictors in the development of the predictive model. A total of 81 variables, including demographics, comorbidities, laboratory findings, as well as information related to anesthesia and surgery, are prospectively collected in the FDP-PONV trial and considered as potential predictive factors in this study. The least absolute shrinkage and selection operator method is used to identify clinically significant variables. Further selection of the final predictors is performed using stepwise regression based on the Akaike Information Criterion. The entire dataset is randomly divided into a training set and a validation set in a ratio of 7:3. Six machine learning models, namely logistic regression, random, extreme gradient boosting, k-nearest neighbor, gradient boosting decision, and multi-layer perceptron, were developed to create risk prediction models for delayed CIPONV. The performance of the models is assessed by comparing the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, Brier score and calibration curve. Bootstrap resamples is conducted 1000 times on the training cohort to evaluate the predictive model's performance. Decision curve analysis is conducted to assess the clinical applicability of the model. The SHapley Additive Explanations library (SHAP) is used to interpret the prediction model. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT06443697
Study type Observational
Source Sixth Affiliated Hospital, Sun Yat-sen University
Contact Zhinan Zheng, MD
Phone 0086-15915734893
Email zhengzhn5@mail.sysu.edu.cn
Status Recruiting
Phase
Start date April 23, 2024
Completion date May 30, 2024

See also
  Status Clinical Trial Phase
Completed NCT04466046 - The Effect on Anxiolytics With Type of Antiemetic Agents on Postoperative Nausea and Vomiting in High Risk Patients
Completed NCT03139383 - Dextrose Containing Fluid and the Postoperative Nausea and Vomiting in the Gynecologic Laparoscopic Surgery N/A
Recruiting NCT04069806 - Preoperative Oral Carbohydrate for Nausea and Vomiting Prevention During Cesarian Section N/A
Completed NCT04043247 - Transcutaneous Electrical Acupoint Stimulation for Prevention of Postoperative Nausea and Vomiting N/A
Terminated NCT01975727 - Dexamethasone for the Treatment of Established Postoperative Nausea and Vomiting Phase 2
Completed NCT03662672 - Rib Raising for Post-operative Ileus N/A
Completed NCT00090155 - 2 Doses of an Approved Drug Being Studied for a New Indication for the Prevention of Postoperative Nausea and Vomiting (0869-090)(COMPLETED) Phase 3
Recruiting NCT05375721 - Prevention of PONV With Traditional Chinese Medicine N/A
Completed NCT02480088 - Comparison of Palonosetron and Ramosetron for Preventing Patient-controlled Analgesia Related Nausea and Vomiting Following Spine Surgery; Association With ABCB1 Polymorphism Phase 4
Recruiting NCT06137027 - Cannabidiol Oil Extract for Prevention of Postoperative Nausea and Vomiting Early Phase 1
Not yet recruiting NCT05529004 - A 6 Months Double Blind Trial to Prevent PONV in Laparoscopic Cholecystectomy Phase 2
Completed NCT02944942 - Risk Factors for Postoperative Nausea/Vomiting N/A
Recruiting NCT02571153 - Low Doses of Ketamine and Postoperative Quality of Recovery Phase 4
Completed NCT02449291 - Study of APD421 as PONV Treatment (no Prior Prophylaxis) Phase 3
Completed NCT02550795 - Dexmedetomidine or Dexmedetomidine Combined With Dexamethasone on Postoperative Nausea and Vomiting in Breast Cancer N/A
Recruiting NCT01442012 - Utility of Acupuncture in the Treatment of Postoperative Nausea and Vomiting in Ambulatory Surgery N/A
Completed NCT01478165 - Comparison of TIVA (Total Intravenous Anesthesia) and TIVA Plus Palonosetron in Preventing Postoperative Nausea and Vomiting N/A
Unknown status NCT01268748 - Single Port Versus Four Ports Laparoscopic Cholecystectomy and Early Postoperative Pain N/A
Completed NCT02143531 - Intravenous Haloperidol Versus Ondansetron for Treatment of Established Post-operative Nausea and Vomiting Phase 4
Completed NCT00734929 - Aprepitant With Dexamethasone Versus Ondansetron With Dexamethasone for PONV Prophylaxis in Patients Having Craniotomy Phase 4