Critical IllnessNCT07773571
ICCA-Based ICU Physiological State Space Monitor
An observational study of Critical Illness, run by Shanghai Zhongshan Hospital at 1 site in China.
Trial summary
Critically ill patients can deteriorate rapidly across multiple organ systems. Most intensive care unit (ICU) risk tools rely on measurements collected at a single time point and may not fully capture how physiology evolves or how quickly a patient recovers from disturbance. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Critical Illness
- What is being tested?
- Multidimensional ICU Physiological State and Trajectory Features
- How many people
- 60,000 participants
- Where
- 1 siteChina
- Study dates
- 15 Jun 2026to 31 Dec 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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: Adult patients admitted to an ICU at Zhongshan Hospital, Fudan University between September 2021 and May 31, 2026 who have an extractable, uniquely identifiable ICU encounter in the ICCA reporting-layer database and meet the prespecified eligibility criteria. The study will use a fixed-time-window census of all eligible records. The principal participant count will be based on unique patients; ICU encounters and patient-days will form the analytic units for longitudinal modeling.
Who may be able to join
- Age 18 years or older.
- Admission to an ICU at Zhongshan Hospital, Fudan University between September 2021 and May 31, 2026, with a uniquely identifiable ICU encounter in the ICCA reporting-layer database.
- ICU length of stay of at least 24 hours.
- Availability of core variables from at least four physiological domains and at least one patient-day suitable for modeling.
Who cannot join
- Duplicate import of the same ICU encounter that cannot be reliably deduplicated.
- Severe missingness, inconsistency, or irreconcilable error in key identifiers, ICU admission/discharge times, or core timestamps.
- Test data, demonstration data, or records determined through data governance to be non-genuine patient records.
- Inability to lock the primary source mapping for key variables, or overall data quality insufficient to support construction of the physiological state vector.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Development of an ICU Physiological State Space Monitor Based on the ICCA Database: A Single-Center Retrospective Observational Study
Critically ill patients can deteriorate rapidly across multiple organ systems. Most intensive care unit (ICU) risk tools rely on measurements collected at a single time point and may not fully capture how physiology evolves or how quickly a patient recovers from disturbance. This single-center retrospective observational study will use routinely collected data from the ICCA reporting database at Zhongshan Hospital, Fudan University to develop and internally validate a research prototype called the ICU Physiological State Space Monitor. Adult ICU admissions will be represented as daily state vectors across 10 physiological domains. The study will characterize each patient's position and movement in a multidimensional state space, identify high-risk regions and possible critical transitions, and quantify physiological resilience using trajectory features such as variability, autocorrelation, curvature, recovery slope, and cross-domain coupling. The primary validation outcome is a composite clinical deterioration event within 72 hours after an eligible index patient-day. The monitor is an analytic and visualization framework for retrospective research and will not be deployed for real-time clinical decision-making in this study.
Read the detailed description
This is a single-center, retrospective, non-interventional methodological cohort study using routinely collected structured data from the ICCA reporting-layer database at Zhongshan Hospital, Fudan University. The source population comprises adult ICU patients admitted between September 2021 and May 31, 2026 who have an extractable and uniquely identifiable ICU encounter. No intervention will be assigned, no clinical decision will be altered, and no additional examination, treatment, follow-up visit, or biospecimen collection will be performed.
The unit of participant-level registration is the patient, while the principal analytic structure is the ICU encounter and the patient-day. ICU admission time will serve as the common temporal anchor. Monitoring, laboratory, fluid, medication, organ-support, diagnostic, and demographic data will be extracted from the ICCA reporting layer and aggregated primarily into consecutive 24-hour windows. Alternative 12-hour or shorter windows may be evaluated in sensitivity analyses.
Daily physiological state vectors will be constructed across 10 domains: oxygenation; ventilation and respiratory drive; hemodynamic perfusion; renal-fluid balance; hepatic-metabolic clearance; inflammation-immune activation; coagulation-blood integrity; brain-autonomic regulation; bioenergetic and acid-base status; and treatment-support burden. Prespecified data-governance rules will be used for variable-source mapping, unit harmonization, time alignment, limited carry-forward, missing-data handling, and physiological-range checks.
Principal component analysis will provide the primary low-dimensional state-space representation. UMAP may be used for supplementary visualization and local-structure exploration but will not be the sole primary analytical framework. Enhanced trajectory features will include state position, displacement, trajectory length, speed, acceleration, turning angle, curvature, local variability, lag-1 autocorrelation, recovery slope, and cross-domain coupling. These features will be used to characterize high-risk regions, possible basin crossings and critical transitions, and physiological resilience.
Eligible records will be divided chronologically into an earlier derivation cohort and a later temporal validation cohort, with an intended split of approximately 70% and 30%, respectively. The exact cutoff will be locked before analysis. The primary analysis will evaluate the association between state-space and trajectory features and a composite clinical deterioration outcome occurring within 72 hours after each eligible index patient-day. Multivariable logistic regression or discrete-time risk models will be used for the primary outcome. ICU and in-hospital mortality may be evaluated using Cox regression or competing-risk methods, and other outcomes will be analyzed with appropriate regression or time-to-event models. Internal validation will use the temporal validation cohort and bootstrap resampling to assess discrimination, calibration, and robustness.
The source data will remain in the hospital-controlled information environment. Investigators will use a deidentified research dataset without direct identifiers and without access to the reidentification key. Only aggregated results, model parameters, and approved figures will be released. The study seeks a waiver of written informed consent because it is retrospective, non-interventional, uses deidentified existing data, and does not affect participants' current or future care.
- Conditions
- Critical Illness
- Being tested
- Multidimensional ICU Physiological State and Trajectory Features
- Keywords
- Intensive Care Unit, Physiological State Space, Trajectory, Electronic Health Records, Physiological Resilience
- Medical subject headings
- Critical Illness
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Multidimensional ICU Physiological State and Trajectory FeaturesOther
The exposure of interest is the participant's multidimensional physiological state and trajectory derived from routinely collected ICU monitoring, laboratory, fluid, medication, organ-support, diagnostic, and demographic data. Daily state vectors will cover 10 physiological domains. Enhanced trajectory features will include state position, displacement, trajectory length, speed, acceleration, turning angle, curvature, local variability, lag-1 autocorrelation, recovery slope, and cross-domain coupling. The investigators will not assign any exposure, treatment, or clinical intervention.
| Study group | Role | What they receive |
|---|---|---|
| Derivation Cohort | This cohort will be used to construct the physiological state vectors, estimate standardization parameters, develop the low-dimensional state-space representation, characterize trajectory features, and fit the primary prediction/association models. Receives: Other: Multidimensional ICU Physiological State and Trajectory Features | |
| Temporal Validation Cohort | This cohort will be held out for temporal internal validation of state-space features, trajectory measures, and associations with prespecified clinical outcomes. Receives: Other: Multidimensional ICU Physiological State and Trajectory Features |
What is being measured
The primary outcome is the main question the study is designed to answer.
Composite Clinical Deterioration Within 72 Hours
A binary composite outcome defined by the occurrence of at least one of the following events during the 72-hour window after an eligible index patient-day: ICU death; new initiation of invasive mechanical ventilation; new initiation of continuous renal replacement therapy (CRRT); new initiation of extracorporeal membrane oxygenation (ECMO); or a prespecified significant escalation in vasoactive medication support. A participant/index window meeting more than one component will be counted once for the composite outcome.
Measured: Within 72 hours following each eligible index patient-day
Secondary outcomes (7)
ICU mortality
Death from any cause before discharge from the ICU.
Measured: From ICU admission through ICU discharge, an average of approximately 5 days
In-Hospital Mortality
Death from any cause before discharge from the index hospitalization.
Measured: From hospital admission through hospital discharge, an average of approximately 30 days
Duration of Invasive Mechanical Ventilation
Total number of calendar days during which invasive mechanical ventilation is used during the ICU stay.
Measured: From ICU admission through ICU discharge, an average of approximately 5 days
Duration of Continuous Renal Replacement Therapy
Total number of calendar days during which CRRT is used during the ICU stay.
Measured: From ICU admission through ICU discharge, an average of approximately 5 days
Duration of Vasoactive Medication Exposure
Total number of calendar days with recorded vasoactive medication support during the ICU stay.
Measured: From ICU admission through ICU discharge, an average of approximately 5 days
ICU Length of Stay
Time from ICU admission to ICU discharge, reported in days.
Measured: From ICU admission through ICU discharge, an average of approximately 5 days
Hospital Length of Stay
Time from hospital admission to hospital discharge, reported in days.
Measured: From hospital admission through hospital discharge, an average of approximately 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Aug 2026
- Study start15 Jun 2026
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Critical IllnessStudies of the same condition, open and under way.
- More trials run by Shanghai Zhongshan HospitalThe 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
- Shanghai Zhongshan Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ming Zhong, Shanghai Zhongshan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07773571
- Sponsor's protocol ID
- B2026-484
- Status last verified
- Aug 2026
- First submitted
- 4 Aug 2026
- Last updated
- 19 Aug 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Deidentified participant-level data will not be made publicly available because the source data are governed by institutional privacy, ethics, and information-security requirements. The data will remain in the hospital-controlled environment. Aggregated results, model parameters, analytic methods, or approved code may be shared where permitted by the ethics committee and institutional data-governance procedures.
Publications
Duggal A, Scheraga R, Sacha GL, Wang X, Huang S, Krishnan S, Siuba MT, Torbic H, Dugar S, Mucha S, Veith J, Mireles-Cabodevila E, Bauer SR, Kethireddy S, Vachharajani V, Dalton JE. Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit. BMJ Open. 2024 Feb 6;14(2):e079243. doi: 10.1136/bmjopen-2023-079243.
PubMed 38320842Xu Z, Mao C, Su C, Zhang H, Siempos I, Torres LK, Pan D, Luo Y, Schenck EJ, Wang F. Sepsis subphenotyping based on organ dysfunction trajectory. Crit Care. 2022 Jul 3;26(1):197. doi: 10.1186/s13054-022-04071-4.
PubMed 35786445Soo A, Zuege DJ, Fick GH, Niven DJ, Berthiaume LR, Stelfox HT, Doig CJ. Describing organ dysfunction in the intensive care unit: a cohort study of 20,000 patients. Crit Care. 2019 May 23;23(1):186. doi: 10.1186/s13054-019-2459-9.
PubMed 31122276Scheffer M, Bolhuis JE, Borsboom D, Buchman TG, Gijzel SMW, Goulson D, Kammenga JE, Kemp B, van de Leemput IA, Levin S, Martin CM, Melis RJF, van Nes EH, Romero LM, Olde Rikkert MGM. Quantifying resilience of humans and other animals. Proc Natl Acad Sci U S A. 2018 Nov 20;115(47):11883-11890. doi: 10.1073/pnas.1810630115. Epub 2018 Oct 29.
PubMed 30373844Scheffer M, Carpenter SR, Lenton TM, Bascompte J, Brock W, Dakos V, van de Koppel J, van de Leemput IA, Levin SA, van Nes EH, Pascual M, Vandermeer J. Anticipating critical transitions. Science. 2012 Oct 19;338(6105):344-8. doi: 10.1126/science.1225244.
PubMed 23087241Thoral PJ, Peppink JM, Driessen RH, Sijbrands EJG, Kompanje EJO, Kaplan L, Bailey H, Kesecioglu J, Cecconi M, Churpek M, Clermont G, van der Schaar M, Ercole A, Girbes ARJ, Elbers PWG; Amsterdam University Medical Centers Database (AmsterdamUMCdb) Collaborators and the SCCM/ESICM Joint Data Science Task Force. Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example. Crit Care Med. 2021 Jun 1;49(6):e563-e577. doi: 10.1097/CCM.0000000000004916.
PubMed 33625129Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data. 2018 Sep 11;5:180178. doi: 10.1038/sdata.2018.178.
PubMed 30204154Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, Pollard TJ, Hao S, Moody B, Gow B, Lehman LH, Celi LA, Mark RG. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023 Jan 3;10(1):1. doi: 10.1038/s41597-022-01899-x.
PubMed 36596836
Study at a glance
From the registry record, last updated by the sponsor on 19 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of an ICU Physiological State Space Monitor Based on the ICCA Database: A Single-Center Retrospective Observational Study
- ClinicalTrials.gov ID
- NCT07773571
- Acronym / sponsor ID
- B2026-484
- Conditions
- Critical Illness
- Keywords
- Intensive Care Unit, Physiological State Space, Trajectory, Electronic Health Records, Physiological Resilience
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 60,000 participants (estimated)
- Lead sponsor
- Shanghai Zhongshan HospitalUniversity, hospital or other
- Study start
- 15 Jun 2026 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 19 Aug 2026 (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.
Critically ill patients can deteriorate rapidly across multiple organ systems. Most intensive care unit (ICU) risk tools rely on measurements collected at a single time point and may not fully capture how physiology evolves or how quickly a patient recovers from disturbance. This single-center retrospective observational study will use routinely collected data from the ICCA reporting database at Zhongshan Hospital, Fudan University to develop and internally validate a research prototype called the ICU Physiological State Space Monitor. Adult ICU admissions will be represented as daily state vectors across 10 physiological domains. The study will characterize each patient's position and movement in a multidimensional state space, identify high-risk regions and possible critical transitions, and quantify physiological resilience using trajectory features such as variability, autocorrelation, curvature, recovery slope, and cross-domain coupling. The primary validation outcome is a composite clinical deterioration event within 72 hours after an eligible index patient-day. The monitor is an analytic and visualization framework for retrospective research and will not be deployed for real-time clinical decision-making in this study.
Detailed description
This is a single-center, retrospective, non-interventional methodological cohort study using routinely collected structured data from the ICCA reporting-layer database at Zhongshan Hospital, Fudan University. The source population comprises adult ICU patients admitted between September 2021 and May 31, 2026 who have an extractable and uniquely identifiable ICU encounter. No intervention will be assigned, no clinical decision will be altered, and no additional examination, treatment, follow-up visit, or biospecimen collection will be performed.
The unit of participant-level registration is the patient, while the principal analytic structure is the ICU encounter and the patient-day. ICU admission time will serve as the common temporal anchor. Monitoring, laboratory, fluid, medication, organ-support, diagnostic, and demographic data will be extracted from the ICCA reporting layer and aggregated primarily into consecutive 24-hour windows. Alternative 12-hour or shorter windows may be evaluated in sensitivity analyses.
Daily physiological state vectors will be constructed across 10 domains: oxygenation; ventilation and respiratory drive; hemodynamic perfusion; renal-fluid balance; hepatic-metabolic clearance; inflammation-immune activation; coagulation-blood integrity; brain-autonomic regulation; bioenergetic and acid-base status; and treatment-support burden. Prespecified data-governance rules will be used for variable-source mapping, unit harmonization, time alignment, limited carry-forward, missing-data handling, and physiological-range checks.
Principal component analysis will provide the primary low-dimensional state-space representation. UMAP may be used for supplementary visualization and local-structure exploration but will not be the sole primary analytical framework. Enhanced trajectory features will include state position, displacement, trajectory length, speed, acceleration, turning angle, curvature, local variability, lag-1 autocorrelation, recovery slope, and cross-domain coupling. These features will be used to characterize high-risk regions, possible basin crossings and critical transitions, and physiological resilience.
Eligible records will be divided chronologically into an earlier derivation cohort and a later temporal validation cohort, with an intended split of approximately 70% and 30%, respectively. The exact cutoff will be locked before analysis. The primary analysis will evaluate the association between state-space and trajectory features and a composite clinical deterioration outcome occurring within 72 hours after each eligible index patient-day. Multivariable logistic regression or discrete-time risk models will be used for the primary outcome. ICU and in-hospital mortality may be evaluated using Cox regression or competing-risk methods, and other outcomes will be analyzed with appropriate regression or time-to-event models. Internal validation will use the temporal validation cohort and bootstrap resampling to assess discrimination, calibration, and robustness.
The source data will remain in the hospital-controlled information environment. Investigators will use a deidentified research dataset without direct identifiers and without access to the reidentification key. Only aggregated results, model parameters, and approved figures will be released. The study seeks a waiver of written informed consent because it is retrospective, non-interventional, uses deidentified existing data, and does not affect participants' current or future care.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Derivation Cohort | – | Other: Multidimensional ICU Physiological State and Trajectory FeaturesThis cohort will be used to construct the physiological state vectors, estimate standardization parameters, develop the low-dimensional state-space representation, characterize trajectory features, and fit the primary prediction/association models. |
| Temporal Validation Cohort | – | Other: Multidimensional ICU Physiological State and Trajectory FeaturesThis cohort will be held out for temporal internal validation of state-space features, trajectory measures, and associations with prespecified clinical outcomes. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients admitted to an ICU at Zhongshan Hospital, Fudan University between September 2021 and May 31, 2026 who have an extractable, uniquely identifiable ICU encounter in the ICCA reporting-layer database and meet the prespecified eligibility criteria. The study will use a fixed-time-window census of all eligible records. The principal participant count will be based on unique patients; ICU encounters and patient-days will form the analytic units for longitudinal modeling.
Key inclusion criteria (4 listed)
- Age 18 years or older.
- Admission to an ICU at Zhongshan Hospital, Fudan University between September 2021 and May 31, 2026, with a uniquely identifiable ICU encounter in the ICCA reporting-layer database.
- ICU length of stay of at least 24 hours.
- Availability of core variables from at least four physiological domains and at least one patient-day suitable for modeling.
Key exclusion criteria (4 listed)
- Duplicate import of the same ICU encounter that cannot be reliably deduplicated.
- Severe missingness, inconsistency, or irreconcilable error in key identifiers, ICU admission/discharge times, or core timestamps.
- Test data, demonstration data, or records determined through data governance to be non-genuine patient records.
- Inability to lock the primary source mapping for key variables, or overall data quality insufficient to support construction of the physiological state vector.
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 |
|---|---|---|
| Multidimensional ICU Physiological State and Trajectory FeaturesThe exposure of interest is the participant's multidimensional physiological state and trajectory derived from routinely collected ICU monitoring, laboratory, fluid, medication, organ-support, diagnostic, and demographic data. Daily state vectors will cover 10 physiological domains. Enhanced trajectory features will include state position, displacement, trajectory length, speed, acceleration, turning angle, curvature, local variability, lag-1 autocorrelation, recovery slope, and cross-domain coupling. The investigators will not assign any exposure, treatment, or clinical intervention. | Other | Derivation Cohort, Temporal Validation Cohort |
Primary outcome
Composite Clinical Deterioration Within 72 Hours
Time frame: Within 72 hours following each eligible index patient-day
A binary composite outcome defined by the occurrence of at least one of the following events during the 72-hour window after an eligible index patient-day: ICU death; new initiation of invasive mechanical ventilation; new initiation of continuous renal replacement therapy (CRRT); new initiation of extracorporeal membrane oxygenation (ECMO); or a prespecified significant escalation in vasoactive medication support. A participant/index window meeting more than one component will be counted once for the composite outcome.
7 secondary outcomes
ICU mortality
Time frame: From ICU admission through ICU discharge, an average of approximately 5 days
In-Hospital Mortality
Time frame: From hospital admission through hospital discharge, an average of approximately 30 days
Duration of Invasive Mechanical Ventilation
Time frame: From ICU admission through ICU discharge, an average of approximately 5 days
Duration of Continuous Renal Replacement Therapy
Time frame: From ICU admission through ICU discharge, an average of approximately 5 days
Duration of Vasoactive Medication Exposure
Time frame: From ICU admission through ICU discharge, an average of approximately 5 days
ICU Length of Stay
Time frame: From ICU admission through ICU discharge, an average of approximately 5 days
Hospital Length of Stay
Time frame: From hospital admission through hospital discharge, an average of approximately 30 days
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Aug 2026.
- Background: PubMed 38320842Duggal A, Scheraga R, Sacha GL, Wang X, Huang S, Krishnan S, Siuba MT, Torbic H, Dugar S, Mucha S, Veith J, Mireles-Cabodevila E, Bauer SR, Kethireddy S, Vachharajani V, Dalton JE. Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit. BMJ Open. 2024 Feb 6;14(2):e079243. doi: 10.1136/bmjopen-2023-079243.
- Background: PubMed 35786445Xu Z, Mao C, Su C, Zhang H, Siempos I, Torres LK, Pan D, Luo Y, Schenck EJ, Wang F. Sepsis subphenotyping based on organ dysfunction trajectory. Crit Care. 2022 Jul 3;26(1):197. doi: 10.1186/s13054-022-04071-4.
- Background: PubMed 31122276Soo A, Zuege DJ, Fick GH, Niven DJ, Berthiaume LR, Stelfox HT, Doig CJ. Describing organ dysfunction in the intensive care unit: a cohort study of 20,000 patients. Crit Care. 2019 May 23;23(1):186. doi: 10.1186/s13054-019-2459-9.
- Background: PubMed 30373844Scheffer M, Bolhuis JE, Borsboom D, Buchman TG, Gijzel SMW, Goulson D, Kammenga JE, Kemp B, van de Leemput IA, Levin S, Martin CM, Melis RJF, van Nes EH, Romero LM, Olde Rikkert MGM. Quantifying resilience of humans and other animals. Proc Natl Acad Sci U S A. 2018 Nov 20;115(47):11883-11890. doi: 10.1073/pnas.1810630115. Epub 2018 Oct 29.
- Background: PubMed 23087241Scheffer M, Carpenter SR, Lenton TM, Bascompte J, Brock W, Dakos V, van de Koppel J, van de Leemput IA, Levin SA, van Nes EH, Pascual M, Vandermeer J. Anticipating critical transitions. Science. 2012 Oct 19;338(6105):344-8. doi: 10.1126/science.1225244.
- Background: PubMed 33625129Thoral PJ, Peppink JM, Driessen RH, Sijbrands EJG, Kompanje EJO, Kaplan L, Bailey H, Kesecioglu J, Cecconi M, Churpek M, Clermont G, van der Schaar M, Ercole A, Girbes ARJ, Elbers PWG; Amsterdam University Medical Centers Database (AmsterdamUMCdb) Collaborators and the SCCM/ESICM Joint Data Science Task Force. Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example. Crit Care Med. 2021 Jun 1;49(6):e563-e577. doi: 10.1097/CCM.0000000000004916.
- Background: PubMed 30204154Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data. 2018 Sep 11;5:180178. doi: 10.1038/sdata.2018.178.
- Background: PubMed 36596836Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, Pollard TJ, Hao S, Moody B, Gow B, Lehman LH, Celi LA, Mark RG. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023 Jan 3;10(1):1. doi: 10.1038/s41597-022-01899-x.
- Individual participant dataSharing planned: No. Deidentified participant-level data will not be made publicly available because the source data are governed by institutional privacy, ethics, and information-security requirements. The data will remain in the hospital-controlled environment. Aggregated results, model parameters, analytic methods, or approved code may be shared where permitted by the ethics committee and institutional data-governance procedures.
Related trials for Critical Illness
RecruitingPhase 4
A Study to Compare the Efficacy and Safety of Extended and Intermittent Infusion of Beta-lactams in Critically Ill Paediatric Patients
Infection, Sepsis
Recruiting
Development and Preliminary Validation of Intelligent Video Laryngoscope for Emergency Use
Critically Ill Patients Requiring Mechanical Ventilation
Active, not recruiting
Weighted Blankets for Agitation in Adults in the Intensive Care Unit
Psychomotor Agitation, Critical Illness
Not yet recruitingObservational study
Core Competencies in General Intensive Care Ultrasound (GenIUS): a Delphi Study
Critical Illness, Ultrasound
RecruitingObservational study
Predicting Extubation Failure Using Machine Learning
Extubation Failure, Critical Illness
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.
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 Critical Illness, run by Shanghai Zhongshan Hospital at 1 site in China.
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 4 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: under way, not looking for participants, as of the sponsor's last update on 19 Aug 2026. 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 China. 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?
This study is still under way. Results are not available in the source record yet.
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?
Composite Clinical Deterioration Within 72 Hours (time frame: Within 72 hours following each eligible index patient-day). 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 NCT07773571, which also lists 8 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 19 Aug 2026.