Prediction of Complications After Major Gastrointestinal Surgery With Machine Learning and Point of Care Ultrasound
NCT ID: NCT06166719
Last Updated: 2023-12-22
Study Results
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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RECRUITING
200 participants
OBSERVATIONAL
2023-11-20
2026-01-20
Brief Summary
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Detailed Description
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Study design:
Single centre observational cohort study
Study population:
Adult patients undergoing elective major gastrointestinal surgery
Primary study parameters/outcome of the study:
The main study endpoint is a machine learning framework based on the hemodynamic profile to predict major complications,especially cardiovascular/pulmonary instability, including, sepsis and septic shock. Data from the ClearSight will be used to collect non-invasive arterial pressure waveforms. point of care ultrasound of heart, lungs and abdominal veins, and clinical data from the electronic medical record will be collected
Secondary study parameters/outcome of the study (if applicable):
point of care ultrasound of heart, lungs and abdominal veins, and clinical data from the electronic medical record will be collected. Ina subgroup of 40 patients RAAS levels and portal blood samples will be analysed.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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No intervention
No intervention
Eligibility Criteria
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Inclusion Criteria
* elective major gastrointestinal surgery: esophagectomy, gastrectomy, pancreatomy or major liver resection (3 segments or more).
Exclusion Criteria
* Patients with major cardiac shunts
* Patients with dialysis shunts or peritoneal dialysis
* Patients in whom POCUS is not possible or assessment of fluid status is unreliable e.g. BMI\> 40, pulmonary fibrosis.
18 Years
ALL
No
Sponsors
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Edwards Lifesciences
INDUSTRY
Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
OTHER
Responsible Party
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A.P.J. Vlaar
prof. dr.
Principal Investigators
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A.P. J. Vlaar, PhD
Role: PRINCIPAL_INVESTIGATOR
Department of Intensive Care, Amsterdam UMC
Locations
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Amsterdam UMC
Amsterdam, North Holland, Netherlands
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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NL84107.018.23
Identifier Type: -
Identifier Source: org_study_id
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