Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT06411496 |
| Other study ID # |
PI2023/029 |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
June 1, 2018 |
| Est. completion date |
June 1, 2023 |
Study information
| Verified date |
May 2024 |
| Source |
Hospital Galdakao-Usansolo |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
This project aims to create and validate surgical risk prediction models for the prediction
of complications in patients pending surgery during the operation, in the immediate
postoperative period and up to one month after discharge.
At present there is no risk assessment system in place, except for the ASA scale which is
mainly based on the subjective impression of the facultative, who assesses it in the
universal preoperative consultations that we have planned in the system. In this project we
intend to provide robust models, based on the analysis of data from patients in 4/5 Basque
hospitals, i.e. generated in our population.
Description:
A three-phase study has been designed:
1. st phase: Derivation and internal validation of the predictive model by means of a
reprospective cohort study in which patients operated on at the Galdakao-Usansolo
Hospital (HGU), Urduliz Hospital (HU), Basurto University Hospital (HUB), Donostia
University Hospital (HUD) and Araba University Hospital (HUA) will be recruited.
Hospital universitario de Donostia (HUD) and Hospital universitario de Araba (HUA) over
XXX years and data will be obtained from the preoperative period until the month of
discharge from the operation. For the identification and creation of these models,
machine learning techniques will be used with the main purpose of identifying variables
not described in the literature. Machine learning is the most important branch of
Artificial Intelligence. Within Machine Learning, supervised learning is the most widely
used area. Supervised learning allows computers to learn to perform tasks by discovering
and exploiting complex patterns in large amounts of data. In the specific case of data
from electronic medical records, Machine Learning algorithms allow us to use the
historical data of each patient so that the computer learns to anticipate future events
in a personalised way.
2. nd phase: External validation of the models created in the first phase in a cohort of
patients operated on in 2020 in the same centres. The methodology proposed by Debray et
al. will be applied.
3. rd phase: Evaluation of results after the implementation of the models in the EHR of the
Galdakao-Usansolo Hospital in the form of an 'Action Guide'. Based on the risk
stratification carried out in the previous phases, the anaesthesia department will
create recommendations for action according to the level of risk. The percentages of
mortality and intra- and postoperative complications will be compared by means of a
quasi-experimental intervention study, comparing the results of the HGU hospital where
the risk scale and the consequent recommendations will be implemented, before and after
its implementation, and also comparing them with the percentages of patients who become
complicated and/or die in HU, HUB, HUD and HUA, where the usual clinical practice will
be followed, based on the ASA scale. This prospective cohort, once the risk scale has
been implemented, will also be used for external validation (2020-2021).
Socio-demographic and clinical variables (main diagnosis, comorbidities, treatments, previous
interventions, intraoperative data, post-operative data, procedures performed during
hospitalisation, and complications up to one month after hospital discharge) and laboratory
parameters will be collected.
This information will be extracted from osabide's global data exploitation system, Oracle
Business Intelligence, and the laboratory data will be extracted from the information systems
of the clinical laboratories of the centres involved.
