Mortality in Intensive Care UnitsICURSNCT04661735
Intensive Care Unit Risk Score
An observational study of Mortality in Intensive Care Units and Complications Infection, run by Charite University, Berlin, Germany at 1 site in Germany.
Trial summary
Subject of the planned project is the retrospective analysis of routine data of digital patient files of the Department for Anaesthesiology and Surgical Intensive Care Medicine, to test whether the predictive values of intensive care scoring systems with regard to perioperative mortality and morbidity can be improved… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Mortality in Intensive Care Units, Complications Infection, Alarm Fatigue
- How many people
- 60,000 participants
- Where
- 1 siteGermany
- Study dates
- 1 Jan 2006to 31 Dec 2025
- 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
- Adult, Older adult
Study population: Age from 18 years. The respective intensive care department carries out approximately 5000 intensive care treatments per year on persons of each sex.
Who may be able to join
- Patients with admission between 01.01.2006 and 30.09.2023
Who cannot join
- Patients under 18 years of age.
- Incomplete patient records.
- Intensive stay of less than 24 hours.
Trial locations and contacts
1 site in 1 country.
Felix Balzer, Prof contact
Akira S Poncette, MD contact
Germany1 site, 1 recruiting
- Recruiting
Charite Universtitaetsmedizin
Berlin, 10117
Felix Balzer, Prof. Dr. Dr.
About this study
Official title: Retrospective Analysis - Scoring Systems in Intensive Care Medicine
Subject of the planned project is the retrospective analysis of routine data of digital patient files of the Department for Anaesthesiology and Surgical Intensive Care Medicine, to test whether the predictive values of intensive care scoring systems with regard to perioperative mortality and morbidity can be improved by continuous score calculation and by using machine learning and time series analysis methods.
Read the detailed description
A scoring system usually consists of two parts - a score (a number reflecting the severity of the disease) and a probability model (equation indicating the probability of an event, e.g. the death of the patient in hospital). Scoring systems have been used in intensive care medicine for decades and can help to assess the effectiveness of treatment or identify comparable patients for study purposes. Scoring systems that are used in intensive care medicine are for example
- Acute Physiology, Age, Chronic Health Evaluation II (APACHE II)
- Simplified Acute Physiology Score II (SAPS II)
- Multiple Organ Dysfunction Score (MODS)
- Sequential Organ Failure Assessment (SOFA)
- Logistic Organ Dysfunction System (LODS)
- MPM II-Admission (Mortality Probability Models (MPM II)
- Organ Dysfunction and Infection score (ODIN)
- Three-Day Recalibrating ICU Outcomes (TRIOS)
- Glasgow coma score (GCS)
- Discharge Readiness Score (DRS) The above-mentioned scoring systems are already being collected regularly in the respective hospital's departments. In a recent study by Badawi et al. it could be shown that scoring systems allow more accurate predictions when calculated continuously. However, due to the patient collectives investigated, these results can only be transferred to other patient groups to a limited extent. Furthermore, only the scoring systems APACHE, SOFA and DRS were analyzed.
Therefore, in the present study, all of the above scoring systems will be calculated continuously (once per minute) using routine data from the digital patient records and optimized by applying machine learning and methods of time series analysis.
On the anesthesiologically managed intensive care units of the respective hospital, there is no campus-wide standard with regard to alarm management. Accordingly, we estimate the rate of alarm fatigue (ignoring alarms due to many false alarms) to be very high. In order to optimize the alarm management, alarms from the patient monitoring devices will be evaluated retrospectively and combined with the data mentioned above to determine, for example, whether more frequent alarms are to be expected for certain types of diseases (e.g. sepsis), or scores (e.g., high APACHE score) and how the alarm limit setting can be optimized.
- Keywords
- scores, intensive care unit, patient monitoring, alarm management
- Medical subject headings
- Alert Fatigue, Health Personnel
What is being measured
The primary outcome is the main question the study is designed to answer.
Prediction of patient outcome
Identification of scores with a high on impact mortality, complications and length of stay in the intensive care unit
Measured: 2006 - 2023
Secondary outcomes (2)
Predictive model for alarm load
Identification of items leading to a high alarm load measured by number of alarm per day per bed in the intensive care unit
Measured: 2020 - 2023
Predictive model for actionable alarms
Identification of items leading to a high number of actionable alarms measured by number of actionable alarms per day per bed in the intensive care unit
Measured: 2020 - 2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Dec 2020
- Study start1 Jan 2006
- Primary completion30 Sep 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Mortality in Intensive Care UnitsStudies of the same condition, open and under way.
- More trials run by Charite University, Berlin, GermanyThe 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
- 60,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Charite University, Berlin, Germany
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Felix Balzer, Charite University, Berlin, Germany
- Investigators
- Felix Balzer, Prof, Principal investigator, Charite University, Berlin, Germany
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04661735
- Sponsor's protocol ID
- ICURS
- Acronym
- ICURS
- Status last verified
- Sep 2023
- First submitted
- 4 Dec 2020
- Last updated
- 8 Sep 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Retrospective Analysis - Scoring Systems in Intensive Care Medicine
- ClinicalTrials.gov ID
- NCT04661735
- Acronym / sponsor ID
- ICURS / ICURS
- Keywords
- scores, intensive care unit, patient monitoring, alarm management
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 60,000 participants (estimated)
- Lead sponsor
- Charite University, Berlin, GermanyUniversity, hospital or other
- Study start
- 1 Jan 2006 (actual)
- Primary completion
- 30 Sep 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 10 Dec 2020 (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.
Subject of the planned project is the retrospective analysis of routine data of digital patient files of the Department for Anaesthesiology and Surgical Intensive Care Medicine, to test whether the predictive values of intensive care scoring systems with regard to perioperative mortality and morbidity can be improved by continuous score calculation and by using machine learning and time series analysis methods.
Detailed description
A scoring system usually consists of two parts - a score (a number reflecting the severity of the disease) and a probability model (equation indicating the probability of an event, e.g. the death of the patient in hospital). Scoring systems have been used in intensive care medicine for decades and can help to assess the effectiveness of treatment or identify comparable patients for study purposes. Scoring systems that are used in intensive care medicine are for example
- Acute Physiology, Age, Chronic Health Evaluation II (APACHE II)
- Simplified Acute Physiology Score II (SAPS II)
- Multiple Organ Dysfunction Score (MODS)
- Sequential Organ Failure Assessment (SOFA)
- Logistic Organ Dysfunction System (LODS)
- MPM II-Admission (Mortality Probability Models (MPM II)
- Organ Dysfunction and Infection score (ODIN)
- Three-Day Recalibrating ICU Outcomes (TRIOS)
- Glasgow coma score (GCS)
- Discharge Readiness Score (DRS) The above-mentioned scoring systems are already being collected regularly in the respective hospital's departments. In a recent study by Badawi et al. it could be shown that scoring systems allow more accurate predictions when calculated continuously. However, due to the patient collectives investigated, these results can only be transferred to other patient groups to a limited extent. Furthermore, only the scoring systems APACHE, SOFA and DRS were analyzed.
Therefore, in the present study, all of the above scoring systems will be calculated continuously (once per minute) using routine data from the digital patient records and optimized by applying machine learning and methods of time series analysis.
On the anesthesiologically managed intensive care units of the respective hospital, there is no campus-wide standard with regard to alarm management. Accordingly, we estimate the rate of alarm fatigue (ignoring alarms due to many false alarms) to be very high. In order to optimize the alarm management, alarms from the patient monitoring devices will be evaluated retrospectively and combined with the data mentioned above to determine, for example, whether more frequent alarms are to be expected for certain types of diseases (e.g. sepsis), or scores (e.g., high APACHE score) and how the alarm limit setting can be optimized.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Age from 18 years. The respective intensive care department carries out approximately 5000 intensive care treatments per year on persons of each sex.
Key inclusion criteria (1 listed)
- Patients with admission between 01.01.2006 and 30.09.2023
Key exclusion criteria (3 listed)
- Patients under 18 years of age.
- Incomplete patient records.
- Intensive stay of less than 24 hours.
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
Prediction of patient outcome
Time frame: 2006 - 2023
Identification of scores with a high on impact mortality, complications and length of stay in the intensive care unit
2 secondary outcomes
Predictive model for alarm load
Time frame: 2020 - 2023
Predictive model for actionable alarms
Time frame: 2020 - 2023
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Sep 2023.
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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.
Felix Balzer, Prof contact
Akira S Poncette, MD contact
Felix Balzer, Prof principal investigator
Charite University, Berlin, Germany
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 Mortality in Intensive Care Units and Complications Infection, run by Charite University, Berlin, Germany at 1 site in Germany.
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 3 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: status not confirmed, as of the sponsor's last update on 8 Sep 2023. 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 Germany. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Felix Balzer, Prof (data-science@charite.de), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction of patient outcome (time frame: 2006 - 2023). 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 8 Sep 2023.