Ignite Creation Date:
2024-10-26 @ 3:41 PM
Last Modification Date:
2024-10-26 @ 3:41 PM
Study NCT ID:
NCT06612177
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-09-21
Brief Title:
Elderly Patients Surgical Site Infection Phenotypes Identification
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Latent Class Analysis and Phenotypes Identification of Surgical Site Infection in Elderly Patients After Non-cardiac Surgery---Based on a Prediction Model Established by Two Centers Large Sample
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:
This study utilizes Latent Class Analysis LCA to identify phenotypes of Surgical Site Infection SSI in elderly patients following non-cardiac surgery By analyzing data from two large cohorts the research establishes a predictive model that uncovers independent risk factors for SSI including age hyperlipidemia and surgical characteristics The model with AUCs ranging from 0753 to 0791 across cohorts offers a calibrated prediction of SSI risk Furthermore LCA delineates four distinct SSI subphenotypes highlighting a critical subgroup with a higher infection rate This subgroup presents a complex interplay of risk factors indicating the need for tailored preventive strategies The studys findings contribute to a nuanced understanding of SSI in elderly surgical patients and pave the way for more targeted infection control measures
Detailed Description:
Backgrounds
With the widespread use of prophylactic antibiotics during perioperative period and the continuous promotion of minimally invasive non-cardiac surgery type such as laparoscopic and thoracoscopic surgery the incidence of superficial Surgical Site Infection SSI has been significantly reduced Organdeep SSI has become the dominant type of SSI Currently the classification of SSI is limited to the above location from shallow to deep the epidemiological and clinical characteristics of SSI after non-cardiac surgery in elderly patients are still inadequately defined
Objectives
The investigators aimed to determine main risk factors for SSI after non-cardiac surgery among elderly patients in China and to further reveal the clinical attributes of those elderly patients afflicted with SSI
Methods
Potential risk factors for developing SSI were selected based on published data clinical expertise pathophysiological reasoning and convenient considerations for future clinical applications These SSI outcomes were rigorously calibrated by researchers complying with back-to-back principles following uniform diagnostic standards--European Perioperative Clinical Outcome EPCO definitions According to the definitions SSI in this study consists of three sites superficial incision deep incision and organdeep The investigators define the occurrence of any of the above sites as SSI infection Multivariable logistic regression analysis was used to identify risk factors for SSI Data from population-based cohort of elderly patients undergoing non-cardiac and non-neurology surgery were used to derive the model The risk prediction model was derived from the First Medical Center of the Chinese PLA General Hospital January 2012 - August 2018 The investigators performed a nomogram a complanation model based on the regression model with the graduated line segments as the main body The discrimination was compared based on the AUC The calibration was assessed by the calibration intercept and the slope Decision curve analysis DCA was adopted to determine the nomograms clinical usefulness and net benefit Latent class analysis LCA was further used to explore the population features of SSI LCA combines the latent variable theory with classified variables to explore the category latent variables behind statistically related classified explicit variables Utilizing LCA patients were classified into distinct cluster classifications with each clusters traits explained based on clinical factors All continuous variables in the prediction model were treated as categorical variables before the LCA analysis The number of categories was ascertained via the Bayesian information criterion BIC The lower BIC was also the elbow point which indicates better model fit When determining the number of latent classes clinical interpretations were taken into account To demonstrate the combination of different risk factors in the prediction model a chord chart and a characteristic data distribution map of subphenotypes were devised
With the widespread use of prophylactic antibiotics during perioperative period and the continuous promotion of minimally invasive non-cardiac surgery type such as laparoscopic and thoracoscopic surgery the incidence of superficial Surgical Site Infection SSI has been significantly reduced Organdeep SSI has become the dominant type of SSI Currently the classification of SSI is limited to the above location from shallow to deep the epidemiological and clinical characteristics of SSI after non-cardiac surgery in elderly patients are still inadequately defined
Objectives
The investigators aimed to determine main risk factors for SSI after non-cardiac surgery among elderly patients in China and to further reveal the clinical attributes of those elderly patients afflicted with SSI
Methods
Potential risk factors for developing SSI were selected based on published data clinical expertise pathophysiological reasoning and convenient considerations for future clinical applications These SSI outcomes were rigorously calibrated by researchers complying with back-to-back principles following uniform diagnostic standards--European Perioperative Clinical Outcome EPCO definitions According to the definitions SSI in this study consists of three sites superficial incision deep incision and organdeep The investigators define the occurrence of any of the above sites as SSI infection Multivariable logistic regression analysis was used to identify risk factors for SSI Data from population-based cohort of elderly patients undergoing non-cardiac and non-neurology surgery were used to derive the model The risk prediction model was derived from the First Medical Center of the Chinese PLA General Hospital January 2012 - August 2018 The investigators performed a nomogram a complanation model based on the regression model with the graduated line segments as the main body The discrimination was compared based on the AUC The calibration was assessed by the calibration intercept and the slope Decision curve analysis DCA was adopted to determine the nomograms clinical usefulness and net benefit Latent class analysis LCA was further used to explore the population features of SSI LCA combines the latent variable theory with classified variables to explore the category latent variables behind statistically related classified explicit variables Utilizing LCA patients were classified into distinct cluster classifications with each clusters traits explained based on clinical factors All continuous variables in the prediction model were treated as categorical variables before the LCA analysis The number of categories was ascertained via the Bayesian information criterion BIC The lower BIC was also the elbow point which indicates better model fit When determining the number of latent classes clinical interpretations were taken into account To demonstrate the combination of different risk factors in the prediction model a chord chart and a characteristic data distribution map of subphenotypes were devised
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