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…
ID
Source
Brief title
Condition
- Other condition
Synonym
Health condition
perioperatieve complicaties en vochthuishouding/hypotensie
Research involving
Sponsors and support
Intervention
Outcome measures
Primary outcome
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 outcome
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.
Background summary
Major complications occur frequently in patients after major gastrointestinal
surgery. Changes in patients physiology often precedes deterioration and early
detection of these changes may improve outcomes. The hemodynamic profile of
these patients may hold information for the prediction of deterioration and
the optimal choice of resuscitation therapy.
In addition, point of care ultrasound can be used to assess fluid
responsiveness and fluid intolerance. We hypothesise that a machine learning
algorithm alongside point of care ultrasound can predict deterioration and
determine whether an intervention with fluids or vasopressors is the optimal
choice in each individual setting.
Study objective
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
RAAS is more active after liver resection.
Study design
Single centre observational cohort study
Study burden and risks
There are no additional risks or benefits associated with participation. There
are no investigational devices used in this study. There are no additional
risks associated with the use of the CS/EV1000/HemoSphere monitor other than
described in the instructions for use. There are also no or very small risks
associated with the other study procedures, including ultrasound.
De Boelelaan 1118
Amsterdam 1081 HZ
NL
De Boelelaan 1118
Amsterdam 1081 HZ
NL
Listed location countries
Age
Inclusion criteria
>=18 years of age.
elective major gastrointestinal surgery: esophagectomy, gastrectomy,
pancreactomy 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
Recruitment
Followed up by the following (possibly more current) registration
No registrations found.
Other (possibly less up-to-date) registrations in this register
No registrations found.
In other registers
| Register | ID |
|---|---|
| CCMO | NL84107.018.23 |