Ignite Creation Date:
2024-06-16 @ 11:50 AM
Last Modification Date:
2024-10-26 @ 3:31 PM
Study NCT ID:
NCT06443697
Status:
COMPLETED
Last Update Posted:
2024-06-05
First Post:
2024-05-30
Brief Title:
A Machine Learning Prediction Model for Delayed CIPONV
Sponsor:
Sixth Affiliated Hospital Sun Yat-sen University
Organization:
Sixth Affiliated Hospital Sun Yat-sen University
Study Overview
Official Title:
A Machine Learning-based Prediction Model for Delayed Clinically Important Postoperative Nausea and Vomiting in High-risk Patients Undergoing Laparoscopic Gastrointestinal Surgery
Status:
COMPLETED
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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 patients 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
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
Detailed Description:
The website httpsmvansmedenshinyappsioBeyondEPV was used for sample size calculation considering 6 candidate predictors an event fraction of 014 and a criterion value for reduced mean predictive squared error of 003 The calculated sample size is 1080 with a minimally required expected event per variable of 251 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 73 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 models 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
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 73 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 models 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None