Surgeon's Performance in Predicting Postoperative Infections

NCT05961930 | Completed

• 1 other identifier: 2022-64
• Study Type: observational
• Target: 594
• Locations: 1 country
• Sites: 1

Brief Summary

Post-surgical (bacterial) infections are the most frequent post-surgical complications, including deep or superficial wound infections, urinary tract infections, pneumonia, and even sepsis. Approximately 6.5-25% of all surgical patients will develop any type of bacterial infection. To personalize surgical infection management, (Artificial Intelligence) models are in the making to predict which patients are at high or low risk of developing a post-surgical infection. In order to benchmark these prediction models to the predictive capabilities of surgeons, the investigators aim to investigate the performance of surgeons in predicting the risk of a patient developing (any type) of post-surgical infection within 30 days.

Conditions

Postoperative Infection

Outcome Measures

Primary Outcomes (2)

• The discriminative predictive performance of surgeons with respect to estimating the risk of developing (any type) bacterial post-surgical within 30 days of surgery

  The primary outcome measure of discrimination are area under the receiving operating characteristic curve (AUROC). Predictions are compared to the occurrence of a postoperative infection requiring treatment, surgical intervention or registration within 30 days of surgery.

  30 days

• The calibration properties of surgeons with respect to estimating the risk of developing (any type) bacterial post-surgical within 30 days of surgery

  Calibration plots with slope and intercept

  30 days

Secondary Outcomes (5)

• Relationship between the certainty in estimate and the predictive performance of surgeons

  30 days

• Relationship between patient factors and predicted risk

  30 days

• Predictive performance per surgeons and patients subgroups

  30 days

• Relationship between predicted risk of surgeons and if they perform additional actions

  30 days

• Comparison between the predicted risk of surgeons and an artificial intelligence algorithm

  30 days

Study Arms (1)

Surgeons estimate of postoperative infection

All surgeons will be asked to fill in a questionnaire, containing 5 questions, pertaining to the estimated risk of postoperative infection (within 30days). Thus there is a single arm and no comparison.

Behavioral: Questionnaire

Interventions

Questionnaire BEHAVIORAL

Surgeons will be asked to fill in a short questionnaire after surgery on risk of postoperative infection

Surgeons estimate of postoperative infection

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult, surgical population of different surgical subspecialties.

You may not qualify if:

• Adult patients (\>18 years old)
• Acute or elective surgery
• Invasive or minimally invasive surgical procedures
• Outpatient procedures or procedures not requiring any form of monitoring/anesthesia
• Procedures for which the primary indication is (treatment for) an infection
• Radiological procedures
• Cardiological catheterization procedures
• Psychiatric treatment under anaesthesia (i.e. electroconvulsive therapy)
• Sole anaesthetic procedures except for implantation of a neurostimulator
• Brachytherapy procedures
• Endoscopic procedures for diagnostic purposes only
• Procedures that only entail the taking of a biopsy for diagnostic purposes
• Patients that are pregnant
• Procedures out of office hours (before 8.00 am or after 5.00 pm)

Sponsors & Collaborators

• Leiden University Medical Center: lead

Study Sites (1)

Sesmu Arbous

Leiden, South Holland, 2333 ZA, Netherlands

Location

MeSH Terms

Interventions

Surveys and Questionnaires

Intervention Hierarchy (Ancestors)

Data Collection; Epidemiologic Methods; Investigative Techniques; Health Care Evaluation Mechanisms; Quality of Health Care; Health Care Quality, Access, and Evaluation; Public Health; Environment and Public Health

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator, Sesmu M. Arbous, MD PhD MSc

Study Record Dates

• First Submitted: March 3, 2023
• First Posted: July 27, 2023
• Study Start: February 1, 2023
• Primary Completion: August 10, 2023
• Study Completion: September 10, 2023
• Last Updated: April 4, 2025

Record last verified: 2023-07

Data Sharing

• IPD Sharing: Will not share

Locations

NL (1)