ICCA-Based ICU Physiological State Space Monitor

NCT07773571 | Active Not Recruiting

• 1 other identifier: B2026-484
• Study Type: observational
• Target: 60,000
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Critical Illness

Keywords

Intensive Care Unit; Physiological State Space; Trajectory; Electronic Health Records; Physiological Resilience

Outcome Measures

Primary Outcomes (1)

• 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.

  Within 72 hours following each eligible index patient-day

Secondary Outcomes (7)

• ICU mortality

  From ICU admission through ICU discharge, an average of approximately 5 days

• In-Hospital Mortality

  From hospital admission through hospital discharge, an average of approximately 30 days

• Duration of Invasive Mechanical Ventilation

  From ICU admission through ICU discharge, an average of approximately 5 days

• Duration of Continuous Renal Replacement Therapy

  From ICU admission through ICU discharge, an average of approximately 5 days

• Duration of Vasoactive Medication Exposure

  From ICU admission through ICU discharge, an average of approximately 5 days

Other Outcomes (12)

• Estimated 72-Hour Composite Deterioration Risk by Physiological State-Space Location

  Within 72 hours following each eligible index patient-day

• State-Space Displacement Between Consecutive Patient-Days

  Across consecutive eligible patient-days from ICU admission through ICU discharge, an average of approximately 5 days

• Physiological State-Space Trajectory Length

  From the first through the last eligible patient-day during the ICU stay, an average of approximately 5 days

Study Arms (2)

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.

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.

Other: Multidimensional ICU Physiological State and Trajectory Features

Interventions

Multidimensional ICU Physiological State and Trajectory Features OTHER

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.

Derivation Cohort; Temporal Validation Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Shanghai Zhongshan Hospital: lead

Study Sites (1)

Zhongshan Hospital

Shanghai, Shanghai Municipality, 200032, China

Location

Related Publications (8)

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  PMID: 38320842 BACKGROUND

• Xu 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.

  PMID: 35786445 BACKGROUND

• Soo 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.

  PMID: 31122276 BACKGROUND

• Scheffer 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.

  PMID: 30373844 BACKGROUND

• Scheffer 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.

  PMID: 23087241 BACKGROUND

• Thoral 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.

  PMID: 33625129 BACKGROUND

• Pollard 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.

  PMID: 30204154 BACKGROUND

• Johnson 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.

  PMID: 36596836 BACKGROUND

MeSH Terms

Conditions

Critical Illness

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director, Department of Critical Care Medicine

Study Record Dates

• First Submitted: August 4, 2026
• First Posted: August 19, 2026
• Study Start: June 15, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2029
• Last Updated: August 19, 2026

Record last verified: 2026-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)