Machine-learning Model for Perioperative Risk Calculation

Perioperative Risk Calculator

Sponsors

Lead sponsor: Technische Universität München

Collaborator: Health Information Management, Belgium

Source Technische Universität München
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).

Overall Status Active, not recruiting
Start Date May 1, 2014
Completion Date February 28, 2022
Primary Completion Date June 30, 2019
Study Type Observational
Primary Outcome
Measure Time Frame
postoperative complications 30 days
Secondary Outcome
Measure Time Frame
in-hospital mortality 30 days
Enrollment 109000
Condition
Eligibility

Sampling method: Non-Probability Sample

Criteria:

Inclusion Criteria:

- all patients who underwent surgery with anesthesia

Exclusion Criteria:

- none

Gender: All

Minimum age: N/A

Maximum age: N/A

Healthy volunteers: No

Verification Date

January 2020

Responsible Party

Responsible party type: Sponsor

Keywords
Has Expanded Access No
Condition Browse
Acronym PROTECT
Patient Data No
Study Design Info

Observational model: Case-Only

Time perspective: Retrospective

Source: ClinicalTrials.gov