Postoperative InfectionNCT06547281
Machine Learning Prediction of Multiple Infections in Elderly Surgical Patients
An observational study of Postoperative Infection and Geriatrics, run by Weidong Mi at 1 site in China.
Trial summary
Utilizing machine learning techniques, investigators developed the geriatric infection assessment model, leveraging domestic databases to predict multiple postoperative infections in elderly patients. Read more about this study
- Who is it for?
- Ages 65 years and older
- Conditions the study is for
- Postoperative Infection, Geriatrics, Prediction
- How many people
- 42,540 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2022to 30 Mar 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 years and older
- All sexes
- No healthy volunteers
- Older adult
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.
Who may be able to join
- Age ≥ 65 years;
- Patients undergoing surgeries not involving local anesthesia.
Who cannot join
- Patients undergoing neurosurgery or cardiac surgery;
- Patients with preoperative infections (including pneumonia, SSIs, UTIs, and bloodstream infections).
Trial locations and contacts
1 site in 1 country.
China1 site
Depatment of Anesthesiology, The First Medical Center Affiliation: Chinese PLA General Hospital
Beijing, Beijing Municipality, 100853
About this study
Official title: Elderly Surgical Patients Multi-Infection Prediction: Machine Learning Model Development & Validation With SHAP Analysis
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.
Read the 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 42,540 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 model's 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.
- Conditions
- Postoperative Infection, Geriatrics, Prediction
- Keywords
- the elderly, postoperative infection, machine learning, risk model
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: January 2012 - August 2018
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Aug 2024
- Study start1 Sep 2022
- Primary completion30 Dec 2023
- Study completion30 Mar 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Postoperative InfectionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 42,540 participants (actual)
Sponsor and investigators
- Lead sponsor
- Weidong Mi
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Weidong Mi, Chinese PLA General Hospital
- Investigators
- Weidong Mi, Study chair, Depatment of Anesthesiology, The First Medical Center Affiliation: Chinese PLA General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06547281
- Sponsor's protocol ID
- PLAGH-AOC-L03
- Status last verified
- Sep 2024
- First submitted
- 7 Aug 2024
- Last updated
- 24 Sep 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Elderly Surgical Patients Multi-Infection Prediction: Machine Learning Model Development & Validation With SHAP Analysis
- ClinicalTrials.gov ID
- NCT06547281
- Acronym / sponsor ID
- PLAGH-AOC-L03
- Conditions
- Postoperative Infection, Geriatrics, Prediction
- Keywords
- the elderly, postoperative infection, machine learning, risk model
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 42,540 participants (actual)
- Lead sponsor
- Weidong MiUniversity, hospital or other
- Study start
- 1 Sep 2022 (actual)
- Primary completion
- 30 Dec 2023 (actual)
- Study completion
- 30 Mar 2024 (actual)
- First posted
- 9 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 42,540 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 model's 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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
- 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.
Key inclusion criteria (2 listed)
- Age ≥ 65 years;
- Patients undergoing surgeries not involving local anesthesia.
Key exclusion criteria (2 listed)
- Patients undergoing neurosurgery or cardiac surgery;
- Patients with preoperative infections (including pneumonia, SSIs, UTIs, and bloodstream infections).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Machine Learning Prediction of Multiple Infections in Elderly Surgery Patients
Time frame: January 2012 - August 2018
Utilizing machine learning techniques, investigators developed the geriatric infection assessment model, leveraging domestic databases to predict multiple postoperative infections in elderly patients.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Sep 2024.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Weidong Mi study chair
Depatment of Anesthesiology, The First Medical Center Affiliation: Chinese PLA General Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Postoperative Infection and Geriatrics, run by Weidong Mi at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 24 Sep 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Machine Learning Prediction of Multiple Infections in Elderly Surgery Patients (time frame: January 2012 - August 2018). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Sep 2024.