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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

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

RECRUITING

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-11-20

Study Completion Date

2026-01-20

Brief Summary

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This is an observational study in patients undergoing major surgery. In which we attempt to predict complications (e.g. low blood pressure, ICU-admittance after major surgery using continuous blood pressure measurements. We will also attempt to predict their response to fluid therapy using point of care ultrasound. Eventually we aim to combine these methods to detect complications earlier and to give advice about whether or not administration of fluid is appropriate

Detailed Description

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The primary aim of this study is to develop a machine learning framework to predict major complications after major gastro-intestinal surgery. Secondary aims include combining this framework with point of care ultrasound to determine the best initial resuscitative strategy; and to determine which ultrasound parameters are best predictive of fluid intolerance. Furthermore if the renin angiotensin aldosterone system is more active after liver resection.

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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Surgery-Complications Overload Fluid

Keywords

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Intensive care unit Gastro-intestinal surgery Prediction

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

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No intervention

No intervention

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* ≥18 years of age.
* elective major gastrointestinal surgery: esophagectomy, gastrectomy, pancreatomy or major liver resection (3 segments or more).

Exclusion Criteria

* no informed consent
* 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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Edwards Lifesciences

INDUSTRY

Sponsor Role collaborator

Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)

OTHER

Sponsor Role lead

Responsible Party

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A.P.J. Vlaar

prof. dr.

Responsibility Role PRINCIPAL_INVESTIGATOR

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

Site Status RECRUITING

Countries

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Netherlands

Central Contacts

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P. Klompmaker, PhD

Role: CONTACT

Phone: 020 444 4444

Email: [email protected]

Facility Contacts

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A.P. J. Vlaar, PhD

Role: primary

Other Identifiers

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NL84107.018.23

Identifier Type: -

Identifier Source: org_study_id