An observational study in Critical Illness, sponsored by Shanghai Zhongshan Hospital. Active, not recruiting at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-08-19.
Sponsored by Shanghai Zhongshan Hospital · Observational
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.
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.
1,780 studies on the registry are indexed under Critical Illness; 442 are open to participants now.
This study's planned enrollment of 60,000 is above the median of 140 across 814 observational studies indexed under Critical Illness.
Browse Critical Illness studies →Shanghai Zhongshan Hospital is the lead sponsor of 652 studies on the registry; 291 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
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
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
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.
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.
Time frame: Within 72 hours following each eligible index patient-day
ICU mortality
Death from any cause before discharge from the ICU.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: From hospital admission through hospital discharge, an average of approximately 30 days
Estimated 72-Hour Composite Deterioration Risk by Physiological State-Space Location
Estimated probability of the 72-hour composite deterioration outcome associated with locations in the low-dimensional physiological state space derived from the prespecified state-space model. The estimated probability will be reported as a percentage ranging from 0% to 100%. High-risk bands, pockets, and local risk landscapes will be considered descriptive features of the estimated risk surface rather than separate outcome measures.
Time frame: Within 72 hours following each eligible index patient-day
State-Space Displacement Between Consecutive Patient-Days
Magnitude of change in the participant's position in the PCA-derived low-dimensional physiological state space between two consecutive eligible patient-days, calculated using the prespecified trajectory algorithm and reported in standardized state-space units.
Time frame: Across consecutive eligible patient-days from ICU admission through ICU discharge, an average of approximately 5 days
Physiological State-Space Trajectory Length
Cumulative path length of the participant's trajectory across consecutive positions in the PCA-derived physiological state space, calculated as the accumulated state-space displacement and reported in standardized state-space units.
Time frame: From the first through the last eligible patient-day during the ICU stay, an average of approximately 5 days
Physiological State-Space Trajectory Speed
Rate of change in physiological state-space position between consecutive eligible patient-days, calculated from state-space displacement over elapsed time and reported in standardized state-space units per day.
Time frame: Across consecutive eligible patient-days from ICU admission through ICU discharge, an average of approximately 5 days
Physiological State-Space Trajectory Acceleration
Change in physiological state-space trajectory speed between successive eligible patient-day intervals, calculated using the prespecified trajectory algorithm and reported in standardized state-space units per day squared.
Time frame: Across consecutive eligible patient-days from ICU admission through ICU discharge, an average of approximately 5 days
Physiological State-Space Turning Angle
Angular change in trajectory direction between successive trajectory segments derived from consecutive physiological state-space positions, reported in degrees.
Time frame: Across consecutive eligible patient-days from ICU admission through ICU discharge, an average of approximately 5 days
Physiological State-Space Trajectory Curvature
Curvature of the participant's physiological state-space trajectory derived from consecutive state-space positions using the prespecified trajectory algorithm. Higher values indicate greater local directional change in the trajectory.
Time frame: Across consecutive eligible patient-days from ICU admission through ICU discharge, an average of approximately 5 days
Local Physiological State-Space Variability
Within-participant variability in physiological state-space position across consecutive eligible patient-days, calculated using the prespecified trajectory analysis and reported in standardized state-space units. Higher values indicate greater short-term physiological variability.
Time frame: Across consecutive eligible patient-days from ICU admission through ICU discharge, an average of approximately 5 days
Lag-1 Autocorrelation of Physiological State-Space Trajectory
Lag-1 autocorrelation coefficient quantifying the correlation between consecutive physiological state-space observations within the participant's trajectory. The coefficient ranges from -1 to 1, with higher positive values indicating greater persistence of the preceding physiological state.
Time frame: Across consecutive eligible patient-days from ICU admission through ICU discharge, an average of approximately 5 days
Area Under the Receiver Operating Characteristic Curve for 72-Hour Composite Deterioration
Discrimination of the prespecified state-space and trajectory prediction model in the temporal validation cohort, assessed using the area under the receiver operating characteristic curve (AUROC) for the 72-hour composite deterioration outcome. AUROC ranges from 0 to 1, with higher values indicating better discrimination.
Time frame: Within 72 hours following each eligible index patient-day in the temporal validation cohort
Calibration Slope for Predicted 72-Hour Composite Deterioration Risk
Calibration of the prespecified state-space and trajectory prediction model in the temporal validation cohort, assessed using the calibration slope comparing predicted probabilities with observed 72-hour composite deterioration outcomes. A calibration slope of 1 indicates ideal calibration.
Time frame: Within 72 hours following each eligible index patient-day in the temporal validation cohort
Brier Score for Prediction of 72-Hour Composite Deterioration
Overall prediction error of the prespecified state-space and trajectory prediction model in the temporal validation cohort, assessed using the Brier score, calculated as the mean squared difference between predicted probabilities and observed binary 72-hour composite deterioration outcomes. The Brier score ranges from 0 to 1, with lower values indicating better predictive accuracy.
Time frame: Within 72 hours following each eligible index patient-day in the temporal validation cohort
Plan to share: 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.
Started Jun 2026 (actual). Primary completion is estimated for Dec 2027, 18 months after the start.
How these figures are worked out, and which studies are left out of them.
This study is active, not recruiting, as verified in Aug 2026. You cannot join it, but the record below documents what was studied.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.