Prediction of complications after major gastrointestinal surgery with machine learning and point of care ultrasound: an observational cohort study.

Prediction of complications after major gastrointestinal surgery with machine learning and point of care ultrasound: an observational cohort study. - AI_PLUS

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON53240

Enrollment

200

Registered

2023-10-12

Start date

2023-11-29

Completion date

Unknown

Last updated

2024-12-16

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

Conditions

perioperatieve complicaties en vochthuishouding/hypotensie perioperative complications and fluid tolerance.

Interventions

None listed

Eligibility

Age

18 Years to 99 Years

Inclusion criteria

Inclusion criteria: >=18 years of age. elective major gastrointestinal surgery: esophagectomy, gastrectomy, pancreactomy or major liver resection (3 segments or more).

Exclusion criteria

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

Measure	Time frame
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	—

Measure

Time frame

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

Measure	Time frame
point of care ultrasound of heart, lungs and abdominal veins, and clinical data from the electronic medical record will be collected. In a subgroup of 40 patients RAAS levels and portal blood samples will be analysed.	—

Countries

Netherlands

Outcome results