Ignite Creation Date:
2024-10-26 @ 3:37 PM
Last Modification Date:
2024-10-26 @ 3:37 PM
Study NCT ID:
NCT06547281
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-08-07
Brief Title:
Machine Learning Prediction of Multiple Infections in Elderly Surgical Patients
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Elderly Surgical Patients Multi-Infection Prediction Machine Learning Model Development Validation With SHAP Analysis
Status:
COMPLETED
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Utilizing machine learning techniques investigators developed the geriatric infection assessment model leveraging domestic databases to predict multiple postoperative infections in elderly patients The model addresses the current gap in predictive tools tailored for elderly surgical patients in China offering insights into both overall and specific infection risks
Detailed Description:
Backgrounds
Postoperative infections are a leading cause of adverse perioperative outcomes particularly for elderly patients Given the varied diagnostic presentations of infection there is a significant gap in the use of predictive tools to identify those at high risk of developing such complications
Objective
Investigators aimed at developing machine learning models to predict various postoperative infection risks in elderly patients facilitating early detection and intervention
Methods
A retrospective analysis was conducted on 42540 elderly patients who underwent non-cardiac surgery at the First Medical Center of the Chinese PLA General Hospital between January 2012 and August 2018 forming the Training set From this a 30 subset was randomly designated as the Test set The models incorporated 51 variables including key infection-related factors Three machine learning techniques-Logistic Regression LR Random Forest RF and Gradient Boosting Machines GBM-were utilized to develop predictive models for overall and specific postoperative infections categorized according to the European Perioperative Clinical Outcome EPCO definitions Model performance was gauged by metrics such as the Area Under the Receiver Operating Characteristic ROC Curve AUC accuracy and precision To enhance model interpretability investigators employed the RF models Variable Importance VIMP and Shapley Additive Explanations SHAP algorithm For a demonstrable prediction of specific infection types data of randomly selected 5 patients were fed into the model with the resulting probabilities depicted in a radar chart
Postoperative infections are a leading cause of adverse perioperative outcomes particularly for elderly patients Given the varied diagnostic presentations of infection there is a significant gap in the use of predictive tools to identify those at high risk of developing such complications
Objective
Investigators aimed at developing machine learning models to predict various postoperative infection risks in elderly patients facilitating early detection and intervention
Methods
A retrospective analysis was conducted on 42540 elderly patients who underwent non-cardiac surgery at the First Medical Center of the Chinese PLA General Hospital between January 2012 and August 2018 forming the Training set From this a 30 subset was randomly designated as the Test set The models incorporated 51 variables including key infection-related factors Three machine learning techniques-Logistic Regression LR Random Forest RF and Gradient Boosting Machines GBM-were utilized to develop predictive models for overall and specific postoperative infections categorized according to the European Perioperative Clinical Outcome EPCO definitions Model performance was gauged by metrics such as the Area Under the Receiver Operating Characteristic ROC Curve AUC accuracy and precision To enhance model interpretability investigators employed the RF models Variable Importance VIMP and Shapley Additive Explanations SHAP algorithm For a demonstrable prediction of specific infection types data of randomly selected 5 patients were fed into the model with the resulting probabilities depicted in a radar chart
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None