Multimodal Digital Biomarker Prediction of Acute Clinical Deterioration

NCT07817550 | Enrolling By Invitation DMC

• 1 other identifier: TH-FUSION72-2026-001
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief Summary

Rather than relying on a single abnormal vital sign, the study will examine how multiple physiologic signals change together over time and whether changes in the relationships among these signals provide useful information about impending clinical deterioration. Individual participant physiologic patterns may be characterized longitudinally to account for differences between individuals and changes within the same individual over time. The primary objective is to evaluate the predictive accuracy of multimodal digital biomarkers for protocol-defined acute clinical deterioration occurring within 72 hours. The study is observational and does not assign participants to an investigational treatment.

Conditions

Acute Disease; Disease Progression; Critical Illness; Biomarkers; Aging; Wearable Electronic Devices; Remote Patient Monitoring; Digital Health

Keywords

Acute Clinical Deterioration; Acute Disease; Disease Progression; Critical Illness; Clinical Deterioration Prediction; Digital Biomarkers; Biomarkers; Multimodal Biomarkers; Physiologic Monitoring; Wearable Electronic Devices; Remote Patient Monitoring; Digital Health; Predictive Analytics; Machine Learning; Longitudinal Monitoring; ECG; Photoplethysmography; Oxygen Saturation; Gait; Sleep; Voice Biomarkers; Cough Biomarkers; 72-Hour Prediction

Outcome Measures

Primary Outcomes (1)

• Predictive Accuracy of Multimodal Digital Biomarker Fusion for Acute Clinical Deterioration Within 72 Hours

  Performance of the prespecified multimodal digital biomarker model in predicting protocol-defined acute clinical deterioration during the subsequent 72-hour prediction window. Model performance will be evaluated using the area under the receiver operating characteristic curve (AUROC), with additional assessment of the area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and calibration.

  At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode.

Secondary Outcomes (5)

• Time-Dependent Predictive Performance of Individual and Combined Digital Biomarkers

  At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode.

• Lead Time to Detection of Acute Clinical Deterioration

  For each protocol-defined acute clinical deterioration event, during the 72 hours preceding event occurrence and ending at the time of event occurrence.

• Incremental Predictive Value of Multimodal Signal Relationships

  At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode.

• Model Calibration for 72-Hour Clinical Deterioration Risk

  At 72 hours after initiation of each prespecified prediction episode.

• Robustness of Multimodal Prediction Across Participant and Clinical Subgroups

  At 72 hours after initiation of each prespecified prediction episode.

Study Arms (1)

FUSION-72 Prospective Multimodal Monitoring Cohort

Participants enrolled in a prospective longitudinal observational cohort undergoing multimodal digital biomarker monitoring. Participants will contribute wearable and digital measurements including electrocardiography (ECG), photoplethysmography (PPG), oxygen saturation, temperature, gait, sleep, voice, cough, and participant-reported symptoms. Data will be analyzed longitudinally to characterize temporal changes and relationships among physiologic and behavioral signals and to evaluate prediction of protocol-defined acute clinical deterioration within 72 hours. No investigational treatment or therapeutic intervention is assigned by the study.

Other: Multimodal Digital Biomarker Monitoring

Interventions

Multimodal Digital Biomarker Monitoring OTHER

Prospective collection and analysis of multimodal digital biomarkers derived from wearable and remote-monitoring technologies, including ECG, PPG, oxygen saturation, temperature, gait, sleep, voice, cough, and participant-reported symptom data. The monitoring is observational and does not direct or replace clinical care, does not assign treatment, and does not require modification of participants' usual medical management. Multimodal signals will be temporally aligned and evaluated for changes in inter-signal relationships associated with protocol-defined clinical deterioration occurring within the subsequent 72-hour prediction window.

FUSION-72 Prospective Multimodal Monitoring Cohort

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

FUSION-72 will enroll an adult longitudinal cohort for prospective evaluation of multimodal digital biomarkers associated with acute clinical deterioration. Participants will undergo protocol-specified collection of wearable and remote digital measurements, including ECG, PPG, oxygen saturation, temperature, gait, sleep, voice, cough, and participant-reported symptoms. The study is designed to evaluate temporal and cross-signal relationships and their ability to predict protocol-defined clinical deterioration within 72 hours.

You may qualify if:

• Adults aged 18 through 90 years at enrollment.
• Able to provide informed consent, or have a legally authorized representative provide consent when permitted by the applicable protocol and regulations.
• Able and willing to participate in prospective longitudinal digital biomarker monitoring.
• Able to use, or permit application of, study-authorized wearable or remote-monitoring technologies.
• Able and willing to provide protocol-specified physiologic, behavioral, acoustic, and symptom data.
• Expected to have sufficient follow-up to permit evaluation of the 72-hour prediction endpoint.
• Willing to comply with study procedures and data-collection requirements.

You may not qualify if:

• Inability to provide informed consent or otherwise participate through an approved consent process.
• Clinical or technical circumstances that prevent reliable collection of the required multimodal digital biomarker data.
• Inability or unwillingness to comply with study monitoring procedures.
• Enrollment in another study that, in the investigator's judgment, would materially interfere with FUSION-72 data collection or endpoint assessment.
• Any circumstance that, in the investigator's judgment, would make participation inappropriate or compromise the integrity of the study.

Sponsors & Collaborators

• Truway Health, Inc.: lead

Study Sites (1)

Truway Health, Inc.

New York, New York, 10016, United States

Location

Related Publications (10)

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• Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020 Sep;26(9):1364-1374. doi: 10.1038/s41591-020-1034-x. Epub 2020 Sep 9.

  PMID: 32908283 BACKGROUND

• Ibrahim H, Liu X, Rivera SC, Moher D, Chan AW, Sydes MR, Calvert MJ, Denniston AK. Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines. Trials. 2021 Jan 6;22(1):11. doi: 10.1186/s13063-020-04951-6.

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• 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 BACKGROUND

• Shaulian SY, Gala D, Makaryus AN. Integration of artificial intelligence into cardiac ultrasonography practice. Expert Rev Med Devices. 2025 Aug;22(8):869-879. doi: 10.1080/17434440.2025.2517171. Epub 2025 Jun 11.

  PMID: 40488666 BACKGROUND

• East SA, Wang Y, Yanamala N, Maganti K, Sengupta PP. Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside. Curr Atheroscler Rep. 2025 Jul 7;27(1):70. doi: 10.1007/s11883-025-01316-9.

  PMID: 40622521 BACKGROUND

• Alpert EA, Kwartz T, Hahn B, Abdulghani W, Nama A, Dadon Z. Artificial Intelligence in Cardiac Point-of-Care Ultrasound: A Narrative Review. Diagnostics (Basel). 2026 Jun 21;16(12):1921. doi: 10.3390/diagnostics16121921.

  PMID: 42351580 BACKGROUND

• Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, Liu A, Costa AB, Wood BJ, Tsai CS, Wang CH, Hsu CN, Lee CK, Ruan P, Xu D, Wu D, Huang E, Kitamura FC, Lacey G, de Antonio Corradi GC, Nino G, Shin HH, Obinata H, Ren H, Crane JC, Tetreault J, Guan J, Garrett JW, Kaggie JD, Park JG, Dreyer K, Juluru K, Kersten K, Rockenbach MABC, Linguraru MG, Haider MA, AbdelMaseeh M, Rieke N, Damasceno PF, E Silva PMC, Wang P, Xu S, Kawano S, Sriswasdi S, Park SY, Grist TM, Buch V, Jantarabenjakul W, Wang W, Tak WY, Li X, Lin X, Kwon YJ, Quraini A, Feng A, Priest AN, Turkbey B, Glicksberg B, Bizzo B, Kim BS, Tor-Diez C, Lee CC, Hsu CJ, Lin C, Lai CL, Hess CP, Compas C, Bhatia D, Oermann EK, Leibovitz E, Sasaki H, Mori H, Yang I, Sohn JH, Murthy KNK, Fu LC, de Mendonca MRF, Fralick M, Kang MK, Adil M, Gangai N, Vateekul P, Elnajjar P, Hickman S, Majumdar S, McLeod SL, Reed S, Graf S, Harmon S, Kodama T, Puthanakit T, Mazzulli T, de Lavor VL, Rakvongthai Y, Lee YR, Wen Y, Gilbert FJ, Flores MG, Li Q. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021 Oct;27(10):1735-1743. doi: 10.1038/s41591-021-01506-3. Epub 2021 Sep 15.

  PMID: 34526699 BACKGROUND

• Darzidehkalani E, Ghasemi-Rad M, van Ooijen PMA. Federated Learning in Medical Imaging: Part II: Methods, Challenges, and Considerations. J Am Coll Radiol. 2022 Aug;19(8):975-982. doi: 10.1016/j.jacr.2022.03.016. Epub 2022 Apr 25.

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• 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 BACKGROUND

Related Links

• Official U.S. National Library of Medicine registry providing public information about clinical research studies, including study design, eligibility, outcomes, contacts, and study status.
• National Library of Medicine resource supporting biomedical literature, terminology, and health research information relevant to the study.

MeSH Terms

Conditions

Acute Disease; Disease Progression; Critical Illness

Condition Hierarchy (Ancestors)

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

Study Officials

• Gavin C Solomon, MD

  Truway Health, Inc.

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 1, 2026
• First Posted: September 14, 2026
• Study Start: September 1, 2026
• Primary Completion (Estimated): September 30, 2038
• Study Completion (Estimated): March 31, 2039
• Last Updated: September 14, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)