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Prediction of Complications After Major Gastrointestinal Surgery With Machine Learning and Point of Care Ultrasound

Prediction of Complications After Major Gastrointestinal Surgery With Machine Learning and Point of Care Ultrasound: an Observational Cohort Study.

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06166719
Acronym
AI_PLUS
Enrollment
200
Registered
2023-12-12
Start date
2023-11-20
Completion date
2026-01-20
Last updated
2023-12-22

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Overload Fluid, Surgery-Complications

Keywords

Intensive care unit, Gastro-intestinal surgery, Prediction

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.

Interventions

OTHERNo intervention

No intervention

Sponsors

Edwards Lifesciences
CollaboratorINDUSTRY
Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum

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.

Design outcomes

Primary

MeasureTime frameDescription
Post-operative complications28 daysmainly complications such as ICU admission, re-OR, hemodynamic or respiratory instability, death, organ failure

Secondary

MeasureTime frameDescription
fluid overload or fluid intolerance28 daysultrasound measurements such as lung ultrasound and VExUS

Other

MeasureTime frameDescription
renin en aldosterone activity28 dayspre and post-opative (day 0 day 1 and day 3) Renin and aldosterone levels in the blood

Countries

Netherlands

Contacts

Primary ContactP. Klompmaker, PhD
p.klompmaker@amsterdamumc.nl020 444 4444

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026