Emergency MedicineeCREAM-UC1NCT06354764
Propensity to Hospitalize Patients From the ED in European Centers.
An observational study of Emergency Medicine, run by Mario Negri Institute for Pharmacological Research at 2 sites in Italy.
Trial summary
The peer-to-peer comparison means center-to-center comparison, which requires adjusting for possible differences among centers to be fair and convincing. The first step to reach this goal is to develop a predictive model that accurately estimates each patient's probability of being admitted, starting from clinical… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Emergency Medicine
- How many people
- 162,000 participants
- Where
- 2 sitesItaly
- Study dates
- 22 Sep 2025to Feb 2027
- Phase
- Observational
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
- Adult, Older adult
Study population: All adult patients who arrived at participating emergency departments between January 1, 2021 and December 31, 2023
Who may be able to join
- Adult
- Arrived at emergency department between 1 January 2021 and 31 December 2023
Who cannot join
- None
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Italy2 sites, 2 recruiting
- Recruiting
Ospedale San Giovanni Bosco
Torino
Franco Aprà, +39 011.2402210, franco.apra@aslcittaditorino.it
- Recruiting
Ospedale Sant'Andrea
Vercelli
Sergio Tartaglia, +39 0161 593111, srtartaglia@gmail.com
About this study
Official title: Propensity to Hospitalize Patients From the ED in European Centers.An Observational Retrospective Quality-of-care Study
The peer-to-peer comparison means center-to-center comparison, which requires adjusting for possible differences among centers to be fair and convincing. The first step to reach this goal is to develop a predictive model that accurately estimates each patient's probability of being admitted, starting from clinical conditions and boundary variables. Such a model would make it possible to calculate, for each ED, the expected hospitalization rate; that is, the hospitalization rate that would have been observed if the ED had behaved like the average of the EDs that provided the data to build the model itself. Comparing the observed hospitalization rate in the single ED with the expected rate derived from the model provides a rigorous method of comparing the department with the average performance, taking into account the characteristics of the patients treated and the conditions under which the ED operated. In other words, the predictive model represents the benchmark against which each ED is evaluated.
- Conditions
- Emergency Medicine
- Being tested
- no intervention
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
no interventionOther
no intervention
| Study group | Role | What they receive |
|---|---|---|
| Adults who attended the emergency department | Not described Receives: Other: no intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Create two separate databases
To create two separate databases (one for each of the two subgroups considered) on all patients who presenteding to the participating EDs over a defined period, containing the information considered important to study both the propensity to hospitalize these patients and their 30-day mortality
Measured: September 2024 - September 2025
Multivariable models
To develop two multivariable models that predict the probability that patients presenting to the ED with dyspnea (first model) or after a TLoC (second model) will be admitted to the hospital
Measured: September 2025 - September 2026
Adjusted comparison
To provide the participating EDs with an adjusted comparison of the hospitalization rates for the patients with selected symptoms, to improve the quality of care.
Measured: September 2025 - September 2026
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Apr 2024
- Study start22 Sep 2025
- Primary completionJan 2027 (estimated)
- Study completionFeb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Emergency MedicineStudies 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
- Retrospective
- Enrolment
- 162,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Mario Negri Institute for Pharmacological Research
- Sponsor type
- University, hospital or other
- Collaborators
- Astir s.r.l., Centre Hospitalier Universitaire Vaudois, Orobix Life S.r.l., Fondazione Bruno Kessler
- Responsible party
- Sponsor
- Investigators
- Guido Bertolini, MD, Principal investigator, Istituto Di Ricerche Farmacologiche Mario Negri
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06354764
- Sponsor's protocol ID
- 8780-UC1
- Acronym
- eCREAM-UC1
- Status last verified
- May 2026
- First submitted
- 3 Apr 2024
- Last updated
- 9 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: eCREAM-UCI1 will join the MIP (Medical Informatics Platform), developed in the Human Brain Project (HBP), to federate clinical data. The MIP is an open-source, GDPR-compliant, privacy-aware platform, enabling remote and federated analyses from datasets physically distributed in various hospitals, research centers, and biobanks, without moving the data outside their original storage site to a central repository. Patient identification data will be deleted from the databases before these are uploaded to the MIP platform, and no pseudo-identifier will be available. See https://mip.ebrains.eu/ for more details on the MIP.