Depatment of Anesthesiology, The First Medical Center Affiliation: Chinese PLA General Hospital
Beijing, Beijing Municipality, 100853, China
NCT Number: NCT06265493
The investigators established a first-ever convenient scoring system for clinicians to assess the risk of Postoperative infectious complications (PICs) for elderly patients. Our scoring system can aid in the early detection of potential risks for postoperative infections. Higher-score patients were more likely to experience postoperative infections.
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Notify Me65 year and older
All sexes
Observational
Beijing, Beijing Municipality, 100853, China
Background:
Postoperative infectious complications (PICs) are related to increased morbidity, mortality, and costs, especially in elderly patients. However, published prediction models of PICs are rarely based on elderly patients.
Objective:
The study wanted to identify practical and valuable variables that could be used to predict postoperative infections and establish a convenient scoring system for clinicians to assess the risk of such conditions in elderly patients.
Methods: Data from 2 population-based cohorts of elderly patients undergoing non-cardiac and non-neurology surgery were used to derive and validate multivariable logistic regression models. The risk prediction models were derived from 37230 patients hospitalized in the First Medical Center of the Chinese PLA General Hospital (January 2012 - August 2018). The risk prediction models were externally validated with data from a cohort of 10252 patients hospitalized in Henan Provincial People's Hospital (November 2014 to May 2022).
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: January 2012 - August 2018
A Derivation and External Validation of Prediction Models based on two centers large sample
Weidong Mi
Other
Postoperative Infectious Complications Calculator for Elderly Patients--A Derivation and External Validation of Prediction Models Based on Two Centers Large Sample
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