Machine Learning Prediction of Multiple Infections in Elderly Surgical Patients

NCT06547281 | Completed

• 1 other identifier: PLAGH-AOC-L03
• Study Type: observational
• Target: 42,540
• Locations: 1 country
• Sites: 1

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.

Conditions

Postoperative Infection; Geriatrics; Prediction

Keywords

the elderly; postoperative infection; machine learning; risk model

Outcome Measures

Primary Outcomes (1)

• Machine Learning Prediction of Multiple Infections in Elderly Surgery Patients

  Utilizing machine learning techniques, investigators developed the geriatric infection assessment model, leveraging domestic databases to predict multiple postoperative infections in elderly patients.

  January 2012 - August 2018

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The Training set included 42,540 elderly patients treated at the First Medical Center of the Chinese PLA General Hospital (PLAGH) from January 2012 to August 2018.

You may qualify if:

• Age ≥ 65 years;
• Patients undergoing surgeries not involving local anesthesia.

You may not qualify if:

• Patients undergoing neurosurgery or cardiac surgery;
• Patients with preoperative infections (including pneumonia, SSIs, UTIs, and bloodstream infections).

Sponsors & Collaborators

• Weidong Mi: lead

Study Sites (1)

Depatment of Anesthesiology, The First Medical Center Affiliation: Chinese PLA General Hospital

Beijing, Beijing Municipality, 100853, China

Location

Study Officials

• Weidong Mi

  Depatment of Anesthesiology, The First Medical Center Affiliation: Chinese PLA General Hospital

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Depatment of Anesthesiology, The First Medical Center

Study Record Dates

• First Submitted: August 7, 2024
• First Posted: August 9, 2024
• Study Start: September 1, 2022
• Primary Completion: December 30, 2023
• Study Completion: March 30, 2024
• Last Updated: September 24, 2024

Record last verified: 2024-09

Locations

CN (1)