Ignite Creation Date:
2024-06-16 @ 11:50 AM
Last Modification Date:
2024-10-26 @ 3:31 PM
Study NCT ID:
NCT06445920
Status:
COMPLETED
Last Update Posted:
2024-06-06
First Post:
2024-06-02
Brief Title:
Risk Factors Analysis for Clinical Important Postoperative Nausea and Vomiting
Sponsor:
Sixth Affiliated Hospital Sun Yat-sen University
Organization:
Sixth Affiliated Hospital Sun Yat-sen University
Study Overview
Official Title:
Risk Factors Analysis for Clinical Important Postoperative Nausea and Vomiting in Patients Undergoing Laparoscopic Gastrointestinal Surgery Based on LASSO and Stepwise Regression
Status:
COMPLETED
Status Verified Date:
2024-06
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 is a distressing and common complication after surgery The concept of clinical important PONV CI-PONV assesses the impact of PONV on patient-reported outcomes This research aims to conduct an analysis of the risk factors contributing to CI-PONV utilizing the least absolute shrinkage and selection operator LASSO and stepwise regression techniques All 1154 patients participating in the FDP-PONV trial are included in this study and categorized into two groups the CI-PONV group and the non-CI-PONV group CI-PONV is defined as the occurrence of PONV with a simplified PONV impact scale score of 5 or higher within 24 hours after surgery The LASSO method is employed to identify the most relevant variables from an initial set of 56 related variables and stepwise regression is used to further refine the selection of the ultimate predictorsA logistic regression model was developed based on the selected factors influencing CIPONV A nomogram was developed as a tool for clinical application
Detailed Description:
Drawing from prior studies we conducted a sample size calculation for a predictive model using the website httpsmvansmedenshinyappsioBeyondEPV By setting the number of candidate predictors to 9 the events fraction to 014 and the criterion value for rMPSE to 004 we determined that a minimum total sample size of 900 is required with a minimally expected event per variable of 139 All patients were classified into either the CI-PONV group or the non-CI-PONV group All 56 perioperative clinical features encompassing baseline characteristics preoperative conditions and intraoperative information were considered as potential predictive factors In the quest to uncover potential predictive factors associated with CI-PONV we employed the least absolute shrinkage and selection operator LASSO to sift through clinically significant variables Subsequently we utilized stepwise regression based on the Akaike Information Criterion AIC to further refine the selection of the ultimate predictors Finally a logistic regression model was developed based on the selected factors influencing CIPONV The discrimination of the model was assessed by the ROCAUC and the goodness of fit of the model was evaluated using the Hosmer-Lemeshow test and calibration plots A nomogram based on the logistic regression model output was developed as a tool for clinical application
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