General ConditionNCT05639452
Development of an Early Warning Score for Detecting the Deterioration of a Patients' General Condition in an Acute Hospital
An observational study of General Condition, run by University Hospital, Basel, Switzerland at 1 site in Switzerland.
Trial summary
An acute deterioration of a patients' general condition is often preceded by changes in individual vital parameters. An early warning system (EWS) shall be developed with a reduced number of physiological and individual parameters, compared to conventional early warning systems; and an algorithm will be generated that… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- General Condition
- What is being tested?
- Data collection for developing an algorithm for an Early Warning Score
- How many people
- 210 participants
- Where
- 1 siteSwitzerland
- Study dates
- 5 Oct 2022to 31 Oct 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: Patients hospitalized in 2016-2022 in the surgical and medical wards of the University Hospital Basel
Who may be able to join
- Hospitalized patients of surgical and medical wards of University Hospital Basel
- Hospital stay longer than 24 hours
- Signed general consent
Who cannot join
- Patients admitted directly to the intensive care unit
- Rejection of general consent
Trial locations and contacts
1 site in 1 country.
Switzerland1 site
University Hospital Basel, Division of Internal Medicine
Basel, 4031
About this study
Official title: Investigator-initiated, Retrospective, Single-center Study for the Development of an Early Warning Score for Detecting the Deterioration of a Patients' General Condition in an Acute Hospital
An acute deterioration of a patients' general condition is often preceded by changes in individual vital parameters. An early warning system (EWS) shall be developed with a reduced number of physiological and individual parameters, compared to conventional early warning systems; and an algorithm will be generated that is able to predict clinical deterioration. Its predictive power and accuracy shall be investigated. In a second exploratory phase, different model variants will be analyzed and the applicability of the model variants in the context of continuous EWS on wearables will be examined.
Read the detailed description
An acute deterioration of a patients' general condition is often preceded by changes in individual vital parameters and may lead to adverse events, such as admission to the intensive care unit, heart attack or death. Some of them are potentially avoidable if appropriate measures are taken in a timely manner. Therefore early warning systems (Early Warning Scores= EWS) have been developed from a set of several physiological measurements, signs and symptoms. Individual parameters are weighted to sum up a score.
Based on this score, the deterioration of a patients' general condition may be indicated and a predetermined reaction from the professional staff be triggered (so-called track-and-trigger system). It is important to determine all parameters since missing values influence the informative value of an EWS. This requires a higher effort by the staff and is one of the reasons why early warning systems are not yet used systematically in Switzerland.
A reduction in the number of parameters to be measured could lower the hurdle for the use of these tools and enable a broader applicability. Therefore an early warning system shall be developed with a reduced number of physiological and individual parameters, compared to conventional early warning systems; and an algorithm will be generated that is able to predict clinical deterioration. Its predictive power and accuracy shall be investigated, based on various clinical outcomes such as mortality, cardiac arrest, transfer to the intensive care unit or sepsis. Retrospective, encrypted patient data (from 2016 until 2022) will be used to develop a statistical prediction model. In a second exploratory phase, different model variants will be analyzed and the applicability of the model variants in the context of continuous EWS on wearables will be examined.
- Conditions
- General Condition
- Being tested
- Data collection for developing an algorithm for an Early Warning Score
- Keywords
- Early Warning Score (EWS), Deterioration of Patients' General Condition
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Data collection for developing an algorithm for an Early Warning ScoreOther
Data collection of patient parameters (heart rate, respiratory rate, clinical outcomes (death, transfer to intensive care unit, adverse events like sepsis, infection, heart attack))
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of Early Warning Score
The accuracy of the early warning system in terms of its predictive power is measured by using the respective medical outcomes. Data models are used to create a relationship between the patient parameters (the predictors) and the clinical outcome (Condition stable vs. condition unstable: death, transfer to intensive care unit, heart attack, infection/sepsis, etc.) as a binary response variable (whether a deterioration occurs or not).
Measured: up to 72 hours after hospital admission
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Dec 2022
- Study start5 Oct 2022
- Primary completion31 Oct 2023
- Study completion31 Oct 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for General ConditionStudies of the same condition, open and under way.
- More trials run by University Hospital, Basel, SwitzerlandThe sponsor's other registered studies.
- 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
- 210 participants (actual)
Sponsor and investigators
- Lead sponsor
- University Hospital, Basel, Switzerland
- Sponsor type
- University, hospital or other
- Collaborators
- Innosuisse - Swiss Innovation Agency
- Responsible party
- Sponsor
- Investigators
- Jens Eckstein, Prof. Dr. med., Principal investigator, University Hospital Basel, Division of Internal Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05639452
- Sponsor's protocol ID
- 2022-01681; am22Eckstein3
- Status last verified
- Feb 2024
- First submitted
- 28 Nov 2022
- Last updated
- 16 Feb 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Investigator-initiated, Retrospective, Single-center Study for the Development of an Early Warning Score for Detecting the Deterioration of a Patients' General Condition in an Acute Hospital
- ClinicalTrials.gov ID
- NCT05639452
- Acronym / sponsor ID
- 2022-01681; am22Eckstein3
- Conditions
- General Condition
- Keywords
- Early Warning Score (EWS), Deterioration of Patients' General Condition
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 210 participants (actual)
- Lead sponsor
- University Hospital, Basel, SwitzerlandUniversity, hospital or other
- Collaborators
- Innosuisse - Swiss Innovation Agency
- Study start
- 5 Oct 2022 (actual)
- Primary completion
- 31 Oct 2023 (actual)
- Study completion
- 31 Oct 2023 (actual)
- First posted
- 6 Dec 2022 (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.
An acute deterioration of a patients' general condition is often preceded by changes in individual vital parameters. An early warning system (EWS) shall be developed with a reduced number of physiological and individual parameters, compared to conventional early warning systems; and an algorithm will be generated that is able to predict clinical deterioration. Its predictive power and accuracy shall be investigated. In a second exploratory phase, different model variants will be analyzed and the applicability of the model variants in the context of continuous EWS on wearables will be examined.
Detailed description
An acute deterioration of a patients' general condition is often preceded by changes in individual vital parameters and may lead to adverse events, such as admission to the intensive care unit, heart attack or death. Some of them are potentially avoidable if appropriate measures are taken in a timely manner. Therefore early warning systems (Early Warning Scores= EWS) have been developed from a set of several physiological measurements, signs and symptoms. Individual parameters are weighted to sum up a score.
Based on this score, the deterioration of a patients' general condition may be indicated and a predetermined reaction from the professional staff be triggered (so-called track-and-trigger system). It is important to determine all parameters since missing values influence the informative value of an EWS. This requires a higher effort by the staff and is one of the reasons why early warning systems are not yet used systematically in Switzerland.
A reduction in the number of parameters to be measured could lower the hurdle for the use of these tools and enable a broader applicability. Therefore an early warning system shall be developed with a reduced number of physiological and individual parameters, compared to conventional early warning systems; and an algorithm will be generated that is able to predict clinical deterioration. Its predictive power and accuracy shall be investigated, based on various clinical outcomes such as mortality, cardiac arrest, transfer to the intensive care unit or sepsis. Retrospective, encrypted patient data (from 2016 until 2022) will be used to develop a statistical prediction model. In a second exploratory phase, different model variants will be analyzed and the applicability of the model variants in the context of continuous EWS on wearables will be examined.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients hospitalized in 2016-2022 in the surgical and medical wards of the University Hospital Basel
Key inclusion criteria (3 listed)
- Hospitalized patients of surgical and medical wards of University Hospital Basel
- Hospital stay longer than 24 hours
- Signed general consent
Key exclusion criteria (2 listed)
- Patients admitted directly to the intensive care unit
- Rejection of general consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Data collection for developing an algorithm for an Early Warning ScoreData collection of patient parameters (heart rate, respiratory rate, clinical outcomes (death, transfer to intensive care unit, adverse events like sepsis, infection, heart attack)) | Other | – |
Primary outcome
Accuracy of Early Warning Score
Time frame: up to 72 hours after hospital admission
The accuracy of the early warning system in terms of its predictive power is measured by using the respective medical outcomes. Data models are used to create a relationship between the patient parameters (the predictors) and the clinical outcome (Condition stable vs. condition unstable: death, transfer to intensive care unit, heart attack, infection/sepsis, etc.) as a binary response variable (whether a deterioration occurs or not).
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 16 Feb 2024.
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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.
Jens Eckstein, Prof. Dr. med. principal investigator
University Hospital Basel, Division of Internal Medicine
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 General Condition, run by University Hospital, Basel, Switzerland at 1 site in Switzerland.
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 3 inclusion and 2 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 16 Feb 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 Switzerland. 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?
Accuracy of Early Warning Score (time frame: up to 72 hours after hospital admission). 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 16 Feb 2024.