Perioperative/Postoperative ComplicationsPROTECTNCT04092933
Perioperative Risk Calculator
An observational study of Perioperative/Postoperative Complications, run by Technical University of Munich.
Trial 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… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Perioperative/Postoperative Complications
- How many people
- 175,559 participants
- Where
- No sites listed yet
- Study dates
- 1 May 2014to 31 Dec 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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
Who may be able to join
- all patients who underwent surgery with anesthesia
Who cannot join
- none
About this study
Official title: Machine-learning Model for Perioperative Risk Calculation
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.
- Keywords
- machine learning, decision support, risk calculator, anesthesia
- Medical subject headings
- Postoperative Complications
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 30 days
Secondary outcomes (1)
in-hospital mortality
mortality within hospital stay
Measured: 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Sep 2019
- Study start1 May 2014
- Primary completion30 Sep 2022
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Perioperative/Postoperative ComplicationsStudies 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 175,559 participants (actual)
Sponsor and investigators
- Lead sponsor
- Technical University of Munich
- Sponsor type
- University, hospital or other
- Collaborators
- Health Information Management, Belgium
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04092933
- Sponsor's protocol ID
- 253/19
- Acronym
- PROTECT
- Status last verified
- Oct 2023
- First submitted
- 8 Jul 2019
- Last updated
- 22 Nov 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 37046259
Study at a glance
From the registry record, last updated by the sponsor on 22 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine-learning Model for Perioperative Risk Calculation
- ClinicalTrials.gov ID
- NCT04092933
- Acronym / sponsor ID
- PROTECT / 253/19
- Keywords
- machine learning, decision support, risk calculator, anesthesia
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 175,559 participants (actual)
- Lead sponsor
- Technical University of MunichUniversity, hospital or other
- Collaborators
- Health Information Management, Belgium
- Study start
- 1 May 2014 (actual)
- Primary completion
- 30 Sep 2022 (actual)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 17 Sep 2019 (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.
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.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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
Key inclusion criteria (1 listed)
- all patients who underwent surgery with anesthesia
Key exclusion criteria (1 listed)
- none
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
postoperative complications
Time frame: 30 days
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
1 secondary outcome
in-hospital mortality
Time frame: 30 days
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Nov 2023.
- Derived from this study: PubMed 37046259Andonov 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.
- Individual participant dataSharing planned: No
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Perioperative/Postoperative Complications, run by Technical University of Munich.
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 1 inclusion and 1 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: status not confirmed, as of the sponsor's last update on 22 Nov 2023. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
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?
postoperative complications (time frame: 30 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04092933, which also lists 1 linked publication.
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 22 Nov 2023.