Postoperative ComplicationsNCT06411496
Creation, Implementation and Validation of Intra- and Postoperative Risk Prediction Models
An observational study of Postoperative Complications and Risk Factors, run by Hospital Galdakao-Usansolo at 1 site in Spain.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Postoperative Complications, Risk Factors
- How many people
- 112,745 participants
- Where
- 1 siteSpain
- Study dates
- 1 Jun 2018to 1 Jun 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Surgical patients at Galdakao-Usansolo Hospital between 2019 and 2022. We used anonymized patient level data from patients in waiting list to be intervened in four public hospitals in Basque Country. Participating hospitals serve a population of approximately 1.2 million and provide tertiary referral services to the surrounding region.
Who may be able to join
- Patients over 18 years of age pending scheduled or urgent surgery in non-cardiac surgery.
Who cannot join
- Surgery performed under local anaesthesia
- Paediatric Surgery
- Obstetric Patient
- Cardiac Surgery
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the detailed description
A three-phase study has been designed:
- 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.
- 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.
- 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.
- Conditions
- Postoperative Complications, Risk Factors
- Medical subject headings
- Postoperative Complications
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Scheduled or urgent surgery | This is a retrospective cohort study recruiting surgical patients at Galdakao-Usansolo Hospital between 2019 and 2022. We used anonymized patient level data from patients in waiting list to be intervened in four public hospitals in Basque Country. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Death intraoperatively and up to one month after surgery
yes/not
Measured: One-month
Secondary outcomes (3)
Intensive care unit admission
yes/not
Measured: One month
intra-operative complications
Categorical variable
Measured: Complications during the intervention
readmission
yes /not
Measured: One month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 May 2024
- Study start1 Jun 2018
- Primary completion1 Jan 2019
- Study completion1 Jun 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Postoperative ComplicationsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 112,745 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hospital Galdakao-Usansolo
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Susana García Gutiérrez, Hospital Galdakao-Usansolo
- Investigators
- Francisco Mendoza, MD, Principal investigator, Galdakao-Usansolo Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06411496
- Sponsor's protocol ID
- PI2023/029
- Status last verified
- May 2024
- First submitted
- 2 May 2024
- Last updated
- 13 May 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 13 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Creation, Implementation and Validation of Intra- and Postoperative Risk Prediction Models
- ClinicalTrials.gov ID
- NCT06411496
- Acronym / sponsor ID
- PI2023/029
- Conditions
- Postoperative Complications, Risk Factors
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 112,745 participants (actual)
- Lead sponsor
- Hospital Galdakao-UsansoloOther government
- Study start
- 1 Jun 2018 (actual)
- Primary completion
- 1 Jan 2019 (actual)
- Study completion
- 1 Jun 2023 (actual)
- First posted
- 13 May 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
A three-phase study has been designed:
- 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.
- 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.
- 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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Scheduled or urgent surgery | – | –This is a retrospective cohort study recruiting surgical patients at Galdakao-Usansolo Hospital between 2019 and 2022. We used anonymized patient level data from patients in waiting list to be intervened in four public hospitals in Basque Country. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Surgical patients at Galdakao-Usansolo Hospital between 2019 and 2022. We used anonymized patient level data from patients in waiting list to be intervened in four public hospitals in Basque Country. Participating hospitals serve a population of approximately 1.2 million and provide tertiary referral services to the surrounding region.
Key inclusion criteria (1 listed)
- Patients over 18 years of age pending scheduled or urgent surgery in non-cardiac surgery.
Key exclusion criteria (4 listed)
- Surgery performed under local anaesthesia
- Paediatric Surgery
- Obstetric Patient
- Cardiac Surgery
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Death intraoperatively and up to one month after surgery
Time frame: One-month
yes/not
3 secondary outcomes
Intensive care unit admission
Time frame: One month
intra-operative complications
Time frame: Complications during the intervention
readmission
Time frame: One month
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 May 2024.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Francisco Mendoza, MD principal investigator
Galdakao-Usansolo Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Postoperative Complications and Risk Factors, run by Hospital Galdakao-Usansolo at 1 site in Spain.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 May 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Death intraoperatively and up to one month after surgery (time frame: One-month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 May 2024.