Clinical trial NCT06166719: Prediction of Complications After Major Gastrointestinal Surgery With Machine Learning and Point of Care Ultrasound
Official title: Prediction of Complications After Major Gastrointestinal Surgery With Machine Learning and Point of Care Ultrasound: an Observational Cohort Study.
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Surgery-Complications
Overload Fluid
Keywords: Intensive care unit
Gastro-intestinal surgery
Prediction
Lead sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Sponsor class: OTHER
Collaborators: name: Edwards Lifesciences
First posted: 2023-12-12
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients who will undergo elective, major gastrointestinal surgery.

Brief summary: 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: 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Post-operative complications
timeFrame: 28 days
Secondary outcomes:
measure: fluid overload or fluid intolerance
timeFrame: 28 days
Other outcomes:
measure: renin en aldosterone activity
timeFrame: 28 days
Locations:
city: Amsterdam
country: Netherlands
facility: Amsterdam UMC
state: Noord-Holland
Study officials:
affiliation: Department of Intensive Care, Amsterdam UMC
name: A.P. J. Vlaar, PhD
role: PRINCIPAL_INVESTIGATOR
References: