Ignite Creation Date:
2024-05-19 @ 5:35 PM
Last Modification Date:
2024-10-26 @ 3:29 PM
Study NCT ID:
NCT06411496
Status:
COMPLETED
Last Update Posted:
2024-05-13
First Post:
2024-05-02
Brief Title:
Creation Implementation and Validation of Intra- and Postoperative Risk Prediction Models
Sponsor:
Hospital Galdakao-Usansolo
Organization:
Hospital Galdakao-Usansolo
Study Overview
Official Title:
Creation Implementation and Validation of Intra- and Postoperative Risk Prediction Models
Status:
COMPLETED
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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 45 Basque hospitals ie generated in our population
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 45 Basque hospitals ie generated in our population
Detailed 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 andor 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 osabide39s 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
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 andor 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 osabide39s 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None