ComorbiditiesAANCT04109001
Method of Measuring Comorbidity to Predict Outcome After Intensive Care
An observational study of Comorbidities and Critical Care, run by Uppsala University.
Trial summary
In this study the investigators will validate the impact of comorbidity on readmission to intensive care unit (ICU) and mortality after ICU and which method of measuring comorbidity that is most predictive. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Comorbidities, Critical Care
- How many people
- 223,495 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2005to 31 Dec 2015
- How long will I take part?
- 5 Years
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: All critical care patients' registries in SIR during the years 2005 to 2012 with valid personal identity number.
Who may be able to join
- All critical care patients' registries in SIR during the years 2005 to 2012
- Valid personal identity number
Who cannot join
- Age 16 and older
- No valid personal identity number
About this study
Official title: Method of Measuring Comorbidity and Time-point to Predict Readmission and Mortality of Intensive Care Patients: an Observational Study Using Linked Data From National Registers of Hospital Care and Cause of Death
In this study the investigators will validate the impact of comorbidity on readmission to intensive care unit (ICU) and mortality after ICU and which method of measuring comorbidity that is most predictive.
The study population included all critical care patients' registries in Swedish intensive care registry (SIR) during the years 2005 to 2012 with valid personal identity number. Data from Statistics Sweden och National Board of Health and Welfare were linked to data from SIR and de-identified.
Read more
Hospital discharge diagnoses from five year preceding the index date for the ICU admission were extracted. A composite outcome of death and readmission will be analyzed.
Analyzes with cox proportional-hazards regression, time to event, on the training data set year 2005-2010 The study population will be split in a training data set (2005-10) and a test data set (2011-12) for validating our prognostic model. The predictive ability in the test data set were evaluated based on discrimination, AUC (C index), Calibration and Brier score.
Read the detailed description
In this study the investigators will validate the impact of comorbidity on readmission to intensive care unit (ICU) and mortality after ICU and which method of measuring comorbidity and in which time-point it is most predictive.
The study population includes all critical care patients' registries in Swedish intensive care registry (SIR) during the years 2005 to 2012 with valid personal identity number. SIR delivers the population to Statistics Sweden directly or via the client for further delivery to Statistics Sweden. For all individuals in the population, data are collected from registers at Statistics Sweden. Statistics Sweden supplies social security numbers and serial numbers to the National Board of Health for further data collection there. The National Board of Health and Welfare delivers the data sample to the client who sends it to Statistics Sweden for collaboration and anonymous. All data material is stored unidentified in the MONA database where only persons connected to the project have access to the material.
The data set containing 293 342 observations and 223 495 unique individuals. Observations which is totally covered in time by another observation are excluded. Two consecutive observations with less than 24 hours between them consider as the same visit. The final set of dates consists of 273 741 observations (223 495 individuals).
Patients with recurrent ICU stays during the study period were considered as recurrent events that are not independent of each other. The interval between ICU discharge and readmission was used both as an outcome variable and to characterize the patient at the time to admission. In the analyzes the dependency between multiple admissions for the same individual was handled using a robust sandwich estimator. Every ICU stay was included in the study but handled as a time-updated exposure.
A composite outcome of death and readmission will be analyzed. Death and readmission will also be analyzed separately. Follow-up starts at admission. A binary status variable (no/yes) is created reflecting if the outcome has happened or not together with a corresponding time variable. For each admission the follow-up ends with readmission, death or end of study (2016-12-31) whichever comes first.
Hospital discharge diagnoses from five years preceding the index date for the ICU admission were extracted from the National Board of Health and Welfare and linked to the SIR data using exact person-based linkage. The Charlson comorbidity index was calculated based on this information. A different categorization of comorbidity was also performed as modified based from the categorization proposed by Elixhauser. For each of 36 defined comorbidity categories the number of admissions with a primary diagnosis, the number of admissions with a secondary diagnosis, the total length of stay with a primary diagnosis, and the interval from the last admission with the comorbidity condition as a primary diagnosis, were calculated.
The underlying condition causing ICU admission was categorized according to diagnosis and admitting department.
Analyzes with cox proportional-hazards regression, time to event, on the training data set year 2005-2010 Charlson comorbidity index (CCI) categorical 0, 1, 2, 3-5, 6-9, 10-17 Elixhauser 36 categories
- Number of primary diagnosis (count)
- Number of secondary diagnosis (count)
- Total care time primary diagnosis (count)
- Time interval from latest primary diagnosis (missing, count)
Analyzes
A Age + sex B A + CCI C A + a D A + a + b E A + a + b + c F A + a + b + c + d
There was by definition no missing data in the comorbidity variables. Missing information concerning age and sex were minimal and did not require imputation. The proportional hazards assumption was checked using the Kaplan-Meier Curves.
The study population will be split in a training data set (2005-10) and a test data set (2011-12) for validating our prognostic model. The predictive ability in the test data set were evaluated based on discrimination, area under curve (AUC) (C index), Calibration and Brier score.
- Conditions
- Comorbidities, Critical Care
What is being measured
The primary outcome is the main question the study is designed to answer.
The ability of comorbidity to predict death after intensive care
A composite outcome of death and readmission will be analyzed. Death and readmission will also be analyzed separately. Follow-up starts at admission. A binary status variable (no/yes) is created reflecting if the outcome has happened or not together with a corresponding time variable.
Measured: For each admission the follow-up ends with readmission, death or end of study (2016-12-31) whichever comes first.
The ability of comorbidity to predict readmission after intensive care
A composite outcome of death and readmission will be analyzed. Death and readmission will also be analyzed separately. Follow-up starts at admission. A binary status variable (no/yes) is created reflecting if the outcome has happened or not together with a corresponding time variable.
Measured: For each admission the follow-up ends with readmission, death or end of study (2016-12-31) whichever comes first.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Sep 2019
- Study start1 Jan 2005
- Primary completion31 Dec 2015
- Study completion31 Dec 2015
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for ComorbiditiesStudies of the same condition, open and under way.
- More trials run by Uppsala UniversityThe 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
- Other
- Enrolment
- 223,495 participants (actual)
- Target duration
- 5 Years
Sponsor and investigators
- Lead sponsor
- Uppsala University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04109001
- Sponsor's protocol ID
- Anna Aronsson
- Acronym
- AA
- Status last verified
- Jan 2020
- First submitted
- 26 Sep 2019
- Last updated
- 10 Jan 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: To minimize the risk of data coming into the wrong hands, after linking the data sources personal numbers replaced with anonymous serial numbers. The data then does not leave statistic Sweden and the database MONA, and are only available to qualified researchers. All analysis and statistical processing is thus done at Statistics Sweden and only results can be obtained from there. All reporting is done at group level. If the study wants to be replicated, data from the same period can be extracted from the above mentioned register.
Publications
Christensen S, Johansen MB, Christiansen CF, Jensen R, Lemeshow S. Comparison of Charlson comorbidity index with SAPS and APACHE scores for prediction of mortality following intensive care. Clin Epidemiol. 2011;3:203-11. doi: 10.2147/CLEP.S20247. Epub 2011 Jun 17.
PubMed 21750629Cook RJ, Lawless JF. Analysis of repeated events. Stat Methods Med Res. 2002 Apr;11(2):141-66. doi: 10.1191/0962280202sm278ra.
PubMed 12040694Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998 Jan;36(1):8-27. doi: 10.1097/00005650-199801000-00004.
PubMed 9431328Aronsson Dannewitz A, Svennblad B, Michaelsson K, Lipcsey M, Gedeborg R. Optimized diagnosis-based comorbidity measures for all-cause mortality prediction in a national population-based ICU population. Crit Care. 2022 Oct 6;26(1):306. doi: 10.1186/s13054-022-04172-0.
PubMed 36203163
Study at a glance
From the registry record, last updated by the sponsor on 10 Jan 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Method of Measuring Comorbidity and Time-point to Predict Readmission and Mortality of Intensive Care Patients: an Observational Study Using Linked Data From National Registers of Hospital Care and Cause of Death
- ClinicalTrials.gov ID
- NCT04109001
- Acronym / sponsor ID
- AA / Anna Aronsson
- Conditions
- Comorbidities, Critical Care
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 223,495 participants (actual)
- Lead sponsor
- Uppsala UniversityUniversity, hospital or other
- Study start
- 1 Jan 2005 (actual)
- Primary completion
- 31 Dec 2015 (actual)
- Study completion
- 31 Dec 2015 (actual)
- First posted
- 30 Sep 2019 (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.
In this study the investigators will validate the impact of comorbidity on readmission to intensive care unit (ICU) and mortality after ICU and which method of measuring comorbidity that is most predictive.
The study population included all critical care patients' registries in Swedish intensive care registry (SIR) during the years 2005 to 2012 with valid personal identity number. Data from Statistics Sweden och National Board of Health and Welfare were linked to data from SIR and de-identified.
Hospital discharge diagnoses from five year preceding the index date for the ICU admission were extracted. A composite outcome of death and readmission will be analyzed.
Analyzes with cox proportional-hazards regression, time to event, on the training data set year 2005-2010 The study population will be split in a training data set (2005-10) and a test data set (2011-12) for validating our prognostic model. The predictive ability in the test data set were evaluated based on discrimination, AUC (C index), Calibration and Brier score.
Detailed description
In this study the investigators will validate the impact of comorbidity on readmission to intensive care unit (ICU) and mortality after ICU and which method of measuring comorbidity and in which time-point it is most predictive.
The study population includes all critical care patients' registries in Swedish intensive care registry (SIR) during the years 2005 to 2012 with valid personal identity number. SIR delivers the population to Statistics Sweden directly or via the client for further delivery to Statistics Sweden. For all individuals in the population, data are collected from registers at Statistics Sweden. Statistics Sweden supplies social security numbers and serial numbers to the National Board of Health for further data collection there. The National Board of Health and Welfare delivers the data sample to the client who sends it to Statistics Sweden for collaboration and anonymous. All data material is stored unidentified in the MONA database where only persons connected to the project have access to the material.
The data set containing 293 342 observations and 223 495 unique individuals. Observations which is totally covered in time by another observation are excluded. Two consecutive observations with less than 24 hours between them consider as the same visit. The final set of dates consists of 273 741 observations (223 495 individuals).
Patients with recurrent ICU stays during the study period were considered as recurrent events that are not independent of each other. The interval between ICU discharge and readmission was used both as an outcome variable and to characterize the patient at the time to admission. In the analyzes the dependency between multiple admissions for the same individual was handled using a robust sandwich estimator. Every ICU stay was included in the study but handled as a time-updated exposure.
A composite outcome of death and readmission will be analyzed. Death and readmission will also be analyzed separately. Follow-up starts at admission. A binary status variable (no/yes) is created reflecting if the outcome has happened or not together with a corresponding time variable. For each admission the follow-up ends with readmission, death or end of study (2016-12-31) whichever comes first.
Hospital discharge diagnoses from five years preceding the index date for the ICU admission were extracted from the National Board of Health and Welfare and linked to the SIR data using exact person-based linkage. The Charlson comorbidity index was calculated based on this information. A different categorization of comorbidity was also performed as modified based from the categorization proposed by Elixhauser. For each of 36 defined comorbidity categories the number of admissions with a primary diagnosis, the number of admissions with a secondary diagnosis, the total length of stay with a primary diagnosis, and the interval from the last admission with the comorbidity condition as a primary diagnosis, were calculated.
The underlying condition causing ICU admission was categorized according to diagnosis and admitting department.
Analyzes with cox proportional-hazards regression, time to event, on the training data set year 2005-2010 Charlson comorbidity index (CCI) categorical 0, 1, 2, 3-5, 6-9, 10-17 Elixhauser 36 categories
- Number of primary diagnosis (count)
- Number of secondary diagnosis (count)
- Total care time primary diagnosis (count)
- Time interval from latest primary diagnosis (missing, count)
Analyzes
A Age + sex B A + CCI C A + a D A + a + b E A + a + b + c F A + a + b + c + d
There was by definition no missing data in the comorbidity variables. Missing information concerning age and sex were minimal and did not require imputation. The proportional hazards assumption was checked using the Kaplan-Meier Curves.
The study population will be split in a training data set (2005-10) and a test data set (2011-12) for validating our prognostic model. The predictive ability in the test data set were evaluated based on discrimination, area under curve (AUC) (C index), Calibration and Brier score.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 5 Years
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All critical care patients' registries in SIR during the years 2005 to 2012 with valid personal identity number.
Key inclusion criteria (2 listed)
- All critical care patients' registries in SIR during the years 2005 to 2012
- Valid personal identity number
Key exclusion criteria (2 listed)
- Age 16 and older
- No valid personal identity number
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
The ability of comorbidity to predict death after intensive care
Time frame: For each admission the follow-up ends with readmission, death or end of study (2016-12-31) whichever comes first.
A composite outcome of death and readmission will be analyzed. Death and readmission will also be analyzed separately. Follow-up starts at admission. A binary status variable (no/yes) is created reflecting if the outcome has happened or not together with a corresponding time variable.
The ability of comorbidity to predict readmission after intensive care
Time frame: For each admission the follow-up ends with readmission, death or end of study (2016-12-31) whichever comes first.
A composite outcome of death and readmission will be analyzed. Death and readmission will also be analyzed separately. Follow-up starts at admission. A binary status variable (no/yes) is created reflecting if the outcome has happened or not together with a corresponding time variable.
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 10 Jan 2020.
- Background: PubMed 21750629Christensen S, Johansen MB, Christiansen CF, Jensen R, Lemeshow S. Comparison of Charlson comorbidity index with SAPS and APACHE scores for prediction of mortality following intensive care. Clin Epidemiol. 2011;3:203-11. doi: 10.2147/CLEP.S20247. Epub 2011 Jun 17.
- Background: PubMed 12040694Cook RJ, Lawless JF. Analysis of repeated events. Stat Methods Med Res. 2002 Apr;11(2):141-66. doi: 10.1191/0962280202sm278ra.
- Background: PubMed 9431328Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998 Jan;36(1):8-27. doi: 10.1097/00005650-199801000-00004.
- Derived from this study: PubMed 36203163Aronsson Dannewitz A, Svennblad B, Michaelsson K, Lipcsey M, Gedeborg R. Optimized diagnosis-based comorbidity measures for all-cause mortality prediction in a national population-based ICU population. Crit Care. 2022 Oct 6;26(1):306. doi: 10.1186/s13054-022-04172-0.
- Individual participant dataSharing planned: No. To minimize the risk of data coming into the wrong hands, after linking the data sources personal numbers replaced with anonymous serial numbers. The data then does not leave statistic Sweden and the database MONA, and are only available to qualified researchers. All analysis and statistical processing is thus done at Statistics Sweden and only results can be obtained from there. All reporting is done at group level. If the study wants to be replicated, data from the same period can be extracted from the above mentioned register.
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Comorbidities and Critical Care, run by Uppsala University.
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 2 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 10 Jan 2020. Status can change between updates, so confirm with the study contact before making plans.
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?
The ability of comorbidity to predict death after intensive care (time frame: For each admission the follow-up ends with readmission, death or end of study (2016-12-31) whichever comes first.). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04109001, which also lists 4 linked publications.
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 10 Jan 2020.