Perioperative Risk Calculator

NCT04092933 | Unknown

• 1 other identifier: 253/19
• Study Type: observational
• Target: 175,559
• Locations: 0 countries
• Sites: N/A

Brief Summary

The aim of this project is to develop a machine-learning model for calculating the risk of postoperative complications. In addition to the data collected during the premedication, the model will include all intraoperative values recorded in the Patient Data Management System (PDMS), which include not only vital and respiratory parameters, but also medication and doses, intraoperative events and times. Postoperative complications are defined according to their severity according to the Clavien-Dindo score (Dindo, Demartines et al., 2004) and are collected from the data available in the health information system (HIS). The machine-learning model is created using an extreme-gradient boosting algorithm which has been updated with new data from the year 2021 to ensure accuracy of the model.

Conditions

Perioperative/Postoperative Complications

Keywords

machine learning; decision support; risk calculator; anesthesia

Outcome Measures

Primary Outcomes (1)

• postoperative complications

  Postoperative complications are classified by means of the Clavien-Dindo-Score. The Clavien-Dindo-Score describes classes of severity of postoperative complications: Grade I: any deviation from the normal postoperative course without the need for pharmacological treatment or surgical, endoscopic and radiological interventions Grade II: requiring pharmacological treatment Grade IIIa: requiring surgical, endoscopic or radiological intervention not under general anesthesia Grade IIIb: requiring surgical, endoscopic or radiological intervention under general anesthesia Grade IVa: single organ dysfunction Grade IVb: multiorgandysfunction Grade V: death of a patient

  30 days

Secondary Outcomes (1)

• in-hospital mortality

  30 days

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients who underwent surgical interventions with general or regional anesthesia at Klinikum rechts der Isar, Munich after the implementation of an electronic patient data management system in May 2014

You may qualify if:

• all patients who underwent surgery with anesthesia

You may not qualify if:

• none

Sponsors & Collaborators

• Technical University of Munich: lead
• Health Information Management, Belgium: collaborator

Related Publications (1)

• Andonov DI, Ulm B, Graessner M, Podtschaske A, Blobner M, Jungwirth B, Kagerbauer SM. Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality. BMC Med Inform Decis Mak. 2023 Apr 12;23(1):67. doi: 10.1186/s12911-023-02151-1.

  PMID: 37046259 DERIVED

MeSH Terms

Conditions

Postoperative Complications

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 8, 2019
• First Posted: September 17, 2019
• Study Start: May 1, 2014
• Primary Completion: September 30, 2022
• Study Completion: December 31, 2024
• Last Updated: November 22, 2023

Record last verified: 2023-10

Data Sharing

• IPD Sharing: Will not share