Multimodal Physiologic State Intelligence Study

NCT07814118 — Enrolling by invitation, 25000 participants, Truway Health, Inc.

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OMNIPHYS: A Prospective Multimodal Longitudinal Study of AI-Enabled Physiologic State Modeling and Early Detection of Clinical Deterioration

RegistryNCT07814118, acronym OMNIPHYS
StatusEnrolling by invitation
Study typeObservational
Enrollment25000 (Estimated)
SponsorTruway Health, Inc. (Industry)
ConditionsHeart Failure, Cardiomyopathies, Pulmonary Hypertension, Myocarditis; Acute or Subacute, Myocarditis Due to Drug, Myocarditis Viral, Pericardial Diseases, Valvular Heart Diseases, Hypertension, Sepsis, Acute Kidney Injury, Pulmonary Edema - Acute, Pulmonary Edema With Heart Failure, Pulmonary Edemas, Acute Respiratory Distress Syndrome, Pulmonary Embolism Acute, Pulmonary Embolism (Diagnosis), Pulmonary Embolism Acute Massive, Pulmonary Embolism Subacute Massive, Venous Thrombosis Pulmonary, Venous Thrombosis (Disorder), Venous Thrombosis, Deep, Peripheral Arterial Disease, Peripheral Arterial Disease (PAD), Aortic Diseases, Muskuloskeletal Diseases, Brain Aging, Brain Aneurysm, Brain Aging Related Conditions, Ultrasound, Ultrasound Exams, Ultrasound Guidance
InterventionsOMNIPHYS Multimodal Ultrasound Imaging System (Device)
Datesstart 2026-09-02, primary completion 2046-09-02 (estimated), completion 2046-09-15 (estimated), first posted 2026-09-10, last updated 2026-09-10
Locations1 site in United States

Related Papers Related Patents Related Grants Related Experts
All trials for Heart Failure All trials for Cardiomyopathies All trials of OMNIPHYS Multimodal Ultrasound Imaging System

-Summary

The OMNIPHYS study will investigate whether multimodal ultrasound and other routinely collected clinical and physiologic data can be combined with artificial intelligence to characterize changes in a person's physiologic state over time. The study will examine cardiac, vascular, pulmonary, and systemic physiologic measurements and develop longitudinal models that describe baseline physiology, physiologic perturbation, compensation, deterioration, treatment response, and recovery. The study is observational and will not assign experimental treatments. The goal is to determine whether changes in multimodal physiologic patterns can be identified and characterized earlier and more reliably than conventional single-time-point assessment.

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+Detailed description

OMNIPHYS: A Prospective Multimodal Longitudinal Study of AI-Enabled Physiologic State Modeling and Early Detection of Clinical Deterioration

The OMNIPHYS study is a prospective, longitudinal observational investigation designed to develop and evaluate a multimodal framework for characterizing human physiologic state and physiologic trajectory over time. The study is centered on the hypothesis that clinically meaningful deterioration may be preceded by measurable changes across multiple physiologic domains, including cardiac function, vascular flow, pulmonary findings, systemic venous congestion, and other routinely available clinical measurements.

The study will collect and analyze multimodal data obtained during clinically appropriate assessments. Ultrasound-derived data may include two-dimensional and three-dimensional imaging, color Doppler, spectral Doppler, cardiac motion and functional measurements, vascular flow characteristics, venous findings, and pulmonary ultrasound observations. Additional clinical data may include vital signs, electrocardiographic measurements, oxygen saturation, laboratory results, medication and intervention records, diagnoses, encounters, and other relevant longitudinal clinical observations.

The study will develop a Physiologic State Vector (PSV) representing multidimensional observations of an individual at defined points in time. Rather than treating disease status as a binary outcome, OMNIPHYS will investigate physiologic states and transitions between states.

The prespecified conceptual state framework includes:

S0 - Baseline: stable observed physiologic phenotype.

S1 - Physiologic Perturbation: measurable deviation from an individual's established baseline.

S2 - Compensation: persistent physiologic abnormality without defined acute clinical deterioration.

S3 - Pre-Decompensation: a longitudinal trajectory associated with increasing probability of clinically significant deterioration.

S4 - Acute Decompensation: clinically meaningful physiologic or clinical deterioration.

S5 - Intervention Response: measurable physiologic change following clinical intervention.

S6 - Recovery: movement toward the participant's prior or expected physiologic state.

S7 - Persistent Dysfunction: sustained deviation from baseline following an acute event or intervention.

The principal research construct is therefore:

Physiologic State → Longitudinal Trajectory → State Transition → Clinical Outcome

The primary investigational performance measure will be the Physiologic Transition Detection Time (PTDT), defined as the interval between a prospectively defined algorithmic detection of a clinically meaningful physiologic transition and the corresponding predefined clinical reference event. PTDT will be evaluated as a research endpoint and will not independently direct clinical care unless separately authorized under an applicable clinical protocol.

Secondary analyses will evaluate physiologic-state classification, trajectory prediction, treatment-response characterization, recovery prediction, longitudinal model calibration, uncertainty estimation, human-AI concordance, cross-site performance, cross-device robustness, and model stability over time.

The OMNIPHYS framework is intended to support multiple pathology-specific cohorts while maintaining a common physiologic modeling architecture. Potential cohorts may include heart failure, cardiomyopathy, pulmonary hypertension, valvular disease, myocarditis, pericardial disease, pulmonary edema, acute respiratory distress syndrome, pulmonary embolic disease, venous thrombosis, sepsis-associated cardiac dysfunction, systemic venous congestion, acute kidney injury, and other clinically appropriate conditions.

A designated research component will investigate federated learning, in which appropriately governed model-development processes may be evaluated across participating institutions without requiring centralized transfer of patient-level datasets. The study will assess whether multimodal physiologic models maintain performance across institutions, ultrasound platforms, acquisition environments, operators, and heterogeneous clinical populations.

The study may additionally investigate a research construct termed the TRUWAY Physiologic Digital Twin, defined as a computational longitudinal representation of observed physiologic measurements and their temporal relationships. This construct is intended for research into trajectory modeling and prediction and does not represent a clinical diagnosis or autonomous clinical decision-making system.

Data interoperability and research infrastructure may incorporate recognized clinical data representations, including DICOM for medical imaging and FHIR-compatible clinical data structures where available and appropriate. Data provenance, version control, quality assurance, model-version tracking, and longitudinal auditability will be incorporated into the research architecture.

The anticipated study horizon is approximately 20 years, from September 2026 through September 2046, permitting investigation of short-term physiologic transitions as well as long-term disease trajectories, recurrence, recovery, persistent dysfunction, technological evolution, and longitudinal model performance.

OMNIPHYS is designed as an observational research protocol. It does not by itself prescribe medical treatment, alter clinical management, or establish that an investigational algorithm is safe or effective for clinical use. Research findings will be evaluated according to the approved protocol, applicable institutional requirements, human-subject protections, data-governance requirements, and prospective statistical analysis plans.

+Arms and interventions

ArmTypeInterventionsDescription
OMNIPHYS Master Physiologic CohortDevice: OMNIPHYS Multimodal Ultrasound Imaging SystemA prospective longitudinal observational cohort of participants undergoing routine clinical evaluation and/or care. The cohort will be characterized using multimodal physiologic observations, including ultrasound-derived imaging, 2D and 3D imaging, color and spectral Doppler, cardiac and vascular measurements, pulmonary ultrasound findings, vital signs, ECG, oxygen saturation, laboratory data, medications, diagnoses, clinical encounters, and longitudinal outcomes. Participants will not be assigned to an experimental treatment. Disease-specific and physiologic subcohorts may be evaluated within the master cohort using a common longitudinal physiologic-state framework.
  • OMNIPHYS Multimodal Ultrasound Imaging System (Device): Observational physiologic and imaging data acquisition.

+Outcome measures

Primary outcomes

  • Physiologic Transition Detection Time (PTDT) [Continuous longitudinal assessment from baseline (Year 0) through Year 50; PTDT will be calculated separately for each predefined clinical reference event occurring during the 50-year follow-up period.]: Time interval, measured in days, between the first prespecified algorithmic detection of a clinically meaningful physiologic state transition and the corresponding predefined clinical reference event. Transitions may include progression from baseline or compensated physiology to pre-decompensation or acute decompensation. The analysis will evaluate the temporal relationship between multimodal physiologic signals and subsequent clinical events.

Secondary outcomes

  • Physiologic State Classification Accuracy [Baseline (Year 0) through Year 50; physiologic state-classification accuracy will be assessed using all evaluable longitudinal observations and predefined clinical reference events collected during the 50-year follow-up period.]: Accuracy of classification of predefined longitudinal physiologic states (S0 Baseline, S1 Physiologic Perturbation, S2 Compensation, S3 Pre-Decompensation, S4 Acute Decompensation, S5 Intervention Response, S6 Recovery, and S7 Persistent Dysfunction) using multimodal clinical, physiologic, and ultrasound-derived data.
  • Physiologic Trajectory Prediction [Baseline (Year 0) through Year 50 of longitudinal follow-up.]: Performance of longitudinal models in predicting subsequent physiologic state transitions and clinically meaningful deterioration from prior multimodal physiologic observations.
  • Time to Clinical Deterioration [Baseline (Year 0) through Year 50 of longitudinal follow-up.]: Time from enrollment or a predefined physiologic assessment to the occurrence of a prespecified clinically meaningful deterioration event, evaluated in relation to longitudinal multimodal physiologic trajectories.
  • Treatment Response Characterization [Baseline (Year 0) through Year 50 of longitudinal follow-up.]: Association between observed clinical interventions and subsequent changes in multimodal physiologic state, including quantitative characterization of physiologic response, non-response, delayed response, and recurrence.
  • Physiologic Recovery Trajectory [Baseline (Year 0) through Year 50 of longitudinal follow-up.]: Magnitude and rate of movement from an abnormal or decompensated physiologic state toward a predefined recovery state following a clinical event or intervention.
  • Multimodal AI Discrimination Performance [Baseline (Year 0) through Year 50 of longitudinal follow-up.]: Discrimination, calibration, sensitivity, specificity, and predictive performance of multimodal artificial-intelligence models for identifying predefined physiologic states and clinically meaningful state transitions.
  • Cross-Site and Cross-Device Generalizability [Baseline (Year 0) through Year 50 of longitudinal follow-up.]: Performance consistency of physiologic-state models across participating clinical sites, ultrasound systems, transducer configurations, acquisition protocols, patient populations, and other relevant data domains.
  • Model Drift and Temporal Stability [Baseline (Year 0) through Year 50 of longitudinal follow-up.]: Longitudinal assessment of changes in model performance, calibration, data distributions, and physiologic feature distributions over time to characterize temporal robustness and potential model drift.
  • Human-AI Concordance [Baseline (Year 0) through Year 50 of longitudinal follow-up.]: Agreement between AI-derived physiologic-state classifications and qualified human clinical or imaging assessments using prespecified agreement and concordance measures.

+Eligibility

Sex: All, ages: 18 Years to any, accepts healthy volunteers

Inclusion Criteria:

1. Adults aged 18 years or older who are capable of providing informed consent, or who meet applicable legally authorized representative requirements where permitted.
2. Individuals undergoing routine clinical evaluation, diagnostic imaging, monitoring, treatment, or longitudinal follow-up, including individuals with relevant cardiovascular, vascular, pulmonary, renal, systemic, or other conditions represented within the OMNIPHYS research framework.
3. Healthy volunteers without the conditions under study may participate as reference participants.
4. Ability to undergo at least one protocol-relevant physiologic or imaging assessment and/or contribute eligible routinely collected clinical data.
5. Availability of sufficient clinical, physiologic, imaging, or longitudinal outcome data for the applicable analysis.
6. Willingness to permit collection and longitudinal analysis of protocol-defined research data in accordance with informed consent and applicable privacy and data-governance requirements.

Exclusion Criteria:

1. Inability or unwillingness to provide informed consent or otherwise meet applicable consent requirements.
2. Inability to obtain interpretable protocol-relevant physiologic or imaging data when such data are required for the applicable analysis.
3. Conditions or circumstances that, in the judgment of the responsible clinical investigator, make participation inappropriate or prevent meaningful longitudinal follow-up.
4. Withdrawal of consent or request for discontinuation of research participation where applicable.
5. Any circumstance that would prevent collection, secure handling, or analysis of study data in accordance with the approved protocol and applicable requirements.

+Locations (1)

FacilityCityCountryStatus
Truway Health, Inc.New York, New YorkUnited States

+Publications

  • Kayarian F, Patel D, O'Brien JR, Schraft EK, Gottlieb M. Artificial intelligence and point-of-care ultrasound: Benefits, limitations, and implications for the future. Am J Emerg Med. 2024 Jun;80:119-122. doi: 10.1016/j.ajem.2024.03.023. Epub 2024 Mar 25. PMID 38555712
  • Bennett S, Johnson CL, Fisher G, Erskine F, Krasner S, Fletcher AJ, Leeson P. Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review. J Clin Med. 2025 Oct 7;14(19):7066. doi: 10.3390/jcm14197066. PMID 41096146
  • Koutsoubis N, Waqas A, Yilmaz Y, Ramachandran RP, Schabath MB, Rasool G. Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging. Radiol Artif Intell. 2025 Jul;7(4):e240637. doi: 10.1148/ryai.240637. PMID 40366260
  • Rehman MHU, Hugo Lopez Pinaya W, Nachev P, Teo JT, Ourselin S, Cardoso MJ. Federated learning for medical imaging radiology. Br J Radiol. 2023 Oct;96(1150):20220890. doi: 10.1259/bjr.20220890. PMID 38011227
  • Guan H, Yap PT, Bozoki A, Liu M. Federated learning for medical image analysis: A survey. Pattern Recognit. 2024 Jul;151:110424. doi: 10.1016/j.patcog.2024.110424. Epub 2024 Mar 12. PMID 38559674
  • G S, Gopalakrishnan U, Parthinarupothi RK, Madathil T. Deep learning supported echocardiogram analysis: A comprehensive review. Artif Intell Med. 2024 May;151:102866. doi: 10.1016/j.artmed.2024.102866. Epub 2024 Apr 4. PMID 38593684
  • Kim J, Maranna S, Watson C, Parange N. A scoping review on the integration of artificial intelligence in point-of-care ultrasound: Current clinical applications. Am J Emerg Med. 2025 Jun;92:172-181. doi: 10.1016/j.ajem.2025.03.029. Epub 2025 Mar 17. PMID 40117961
  • Myhre PL, Grenne B, Asch FM, Delgado V, Khera R, Lafitte S, Lang RM, Pellikka PA, Sengupta PP, Vemulapalli S, Lam CSP. Artificial intelligence-enhanced echocardiography in cardiovascular disease management. Nat Rev Cardiol. 2026 Mar;23(3):164-182. doi: 10.1038/s41569-025-01197-0. Epub 2025 Aug 5. PMID 40764834
  • Sahashi Y, Ouyang D, Okura H, Kagiyama N. AI-echocardiography: Current status and future direction. J Cardiol. 2025 Jun;85(6):458-464. doi: 10.1016/j.jjcc.2025.02.005. Epub 2025 Mar 1. PMID 40023671
  • Wu D, Arntfield R, Millington SJ. Artificial Intelligence in Point-of-Care Ultrasound. J Intensive Care Med. 2026 Mar 2:8850666261427329. doi: 10.1177/08850666261427329. Online ahead of print. PMID 41771537

+Investigators