Perioperative Risk Calculator

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

175559 participants

Study Classification

OBSERVATIONAL

Study Start Date

2014-05-01

Study Completion Date

2024-12-31

Brief Summary

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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.

Detailed Description

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Conditions

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Perioperative/Postoperative Complications

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* all patients who underwent surgery with anesthesia

Exclusion Criteria

* none

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Responsible Party

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Responsibility Role SPONSOR

References

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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.

Reference Type DERIVED

PMID: 37046259 (View on PubMed)

Other Identifiers

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253/19

Identifier Type: -

Identifier Source: org_study_id