Multimodal Digital Biomarker Prediction of Acute Clinical Deterioration (FUSION-72)
9 de septiembre de 2026 actualizado por: Truway Health, Inc.
Multimodal Physiologic Signal Fusion for 72-Hour Prediction of Acute Clinical Deterioration
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.
Descripción general del estudio
Estado
Inscripción por invitación
Condiciones
Intervención / Tratamiento
Descripción detallada
FUSION-72 (Multimodal Physiologic Signal Fusion for 72-Hour Prediction of Acute Clinical Deterioration) is a prospective, longitudinal, observational study designed to investigate multimodal digital biomarkers associated with acute clinical deterioration. The central scientific premise is that clinically meaningful deterioration may be preceded not only by an abnormal value in an individual physiologic parameter, but also by coordinated or discordant changes occurring across multiple physiologic, behavioral, and symptom domains.
The study will collect longitudinal data from multiple digital and physiologic sources, which may include wearable electrocardiography (ECG), photoplethysmography (PPG), peripheral oxygen saturation (SpO2), temperature, gait and activity characteristics, sleep measures, voice characteristics, cough characteristics, and participant-reported symptoms.
These multimodal measurements will be temporally aligned and analyzed to characterize changes within individual participants and relationships among physiologic, behavioral, acoustic, and symptom-derived signals. Analyses will evaluate whether multimodal signal fusion provides additional predictive information beyond individual digital biomarker streams or conventional single-parameter measurements.
The primary objective is to evaluate the predictive accuracy of a prespecified multimodal digital biomarker model for protocol-defined acute clinical deterioration occurring within a subsequent 72-hour prediction window. Secondary objectives include evaluation of time-dependent predictive performance, lead time to detection, incremental predictive value of multimodal signal relationships, model calibration, and robustness of predictive performance across prespecified participant and clinical subgroups.
The study is observational. No investigational treatment is assigned, and participation does not direct, replace, or require modification of usual clinical care or medical management.
Tipo de estudio
De observación
Inscripción (Estimado)
10000
Contactos y Ubicaciones
Esta sección proporciona los datos de contacto de quienes realizan el estudio e información sobre dónde se lleva a cabo este estudio.
Ubicaciones de estudio
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New York
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New York, New York, Estados Unidos, 10016
- Truway Health, Inc.
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Criterios de participación
Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.
Criterio de elegibilidad
Edades elegibles para estudiar
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
No
Método de muestreo
Muestra no probabilística
Población de estudio
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.
Descripción
Inclusion Criteria:
- 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.
Exclusion Criteria:
- 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.
Plan de estudios
Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.
¿Cómo está diseñado el estudio?
Detalles de diseño
Cohortes e Intervenciones
Grupo / Cohorte |
Intervención / Tratamiento |
|---|---|
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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.
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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.
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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Predictive Accuracy of Multimodal Digital Biomarker Fusion for Acute Clinical Deterioration Within 72 Hours
Periodo de tiempo: At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode.
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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.
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At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode.
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Time-Dependent Predictive Performance of Individual and Combined Digital Biomarkers
Periodo de tiempo: At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode.
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Comparison of predictive performance associated with individual and multimodal combinations of electrocardiography (ECG), photoplethysmography (PPG), peripheral oxygen saturation (SpO2), temperature, gait, sleep, voice, cough, and symptom-derived features for prediction of protocol-defined acute clinical deterioration.
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At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode.
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Lead Time to Detection of Acute Clinical Deterioration
Periodo de tiempo: For each protocol-defined acute clinical deterioration event, during the 72 hours preceding event occurrence and ending at the time of event occurrence.
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Time interval, measured in hours, between the earliest prespecified model-generated deterioration signal meeting protocol-defined prediction criteria and occurrence of the corresponding protocol-defined acute clinical deterioration event.
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For each protocol-defined acute clinical deterioration event, during the 72 hours preceding event occurrence and ending at the time of event occurrence.
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Incremental Predictive Value of Multimodal Signal Relationships
Periodo de tiempo: At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode.
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Assessment of whether incorporating prespecified temporal relationships and interactions among multiple physiologic, behavioral, acoustic, and symptom-derived signals improves prediction of protocol-defined acute clinical deterioration compared with models based on individual signal domains or conventional single-parameter measurements.
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At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode.
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Model Calibration for 72-Hour Clinical Deterioration Risk
Periodo de tiempo: At 72 hours after initiation of each prespecified prediction episode.
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Agreement between predicted probabilities of protocol-defined acute clinical deterioration and observed event frequencies within the subsequent 72-hour prediction horizon, assessed using prespecified calibration metrics.
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At 72 hours after initiation of each prespecified prediction episode.
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Robustness of Multimodal Prediction Across Participant and Clinical Subgroups
Periodo de tiempo: At 72 hours after initiation of each prespecified prediction episode.
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Evaluation of predictive performance across prespecified participant and clinical subgroups, including age category, sex, baseline health status, comorbidity burden, care setting, and availability or completeness of individual digital biomarker streams.
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At 72 hours after initiation of each prespecified prediction episode.
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Colaboradores e Investigadores
Aquí es donde encontrará personas y organizaciones involucradas en este estudio.
Patrocinador
Investigadores
- Investigador principal: Gavin C Solomon, MD, Truway Health, Inc.
Publicaciones y enlaces útiles
La persona responsable de ingresar información sobre el estudio proporciona voluntariamente estas publicaciones. Estos pueden ser sobre cualquier cosa relacionada con el estudio.
Publicaciones Generales
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
Enlaces Útiles
- 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.
Fechas de registro del estudio
Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.
Fechas importantes del estudio
Inicio del estudio (Actual)
1 de septiembre de 2026
Finalización primaria (Estimado)
30 de septiembre de 2038
Finalización del estudio (Estimado)
31 de marzo de 2039
Fechas de registro del estudio
Enviado por primera vez
1 de septiembre de 2026
Primero enviado que cumplió con los criterios de control de calidad
9 de septiembre de 2026
Publicado por primera vez (Actual)
14 de septiembre de 2026
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
14 de septiembre de 2026
Última actualización enviada que cumplió con los criterios de control de calidad
9 de septiembre de 2026
Última verificación
1 de septiembre de 2026
Más información
Términos relacionados con este estudio
Palabras clave
- Paso
- Dormir
- Enfermedad progresiva
- Biomarcadores
- Enfermedad crítica
- Fotopletismografía
- Salud digital
- Electrocardiograma
- Aprendizaje automático
- Dispositivos electrónicos portátiles
- Enfermedad aguda
- Saturación de oxígeno
- Monitoreo remoto de pacientes
- Biomarcadores digitales
- Análisis predictivo
- Monitoreo fisiológico
- Biomarcadores multimodales
- Biomarcadores de voz
- Longitudinal Monitoring
- Acute Clinical Deterioration
- Clinical Deterioration Prediction
- Cough Biomarkers
- 72-Hour Prediction
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- TH-FUSION72-2026-001
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
NO
Descripción del plan IPD
Individual participant data will not be shared with other researchers.
Individual-level clinical, imaging, and other participant data will remain under the control of the participating research sites and will not be transferred to external investigators or deposited in a publicly accessible repository.
The study may use privacy-preserving federated analysis in which participating sites retain source data locally and transmit only approved, non-identifying computational outputs, such as model parameters, aggregate statistics, or other de-identified results, as permitted by the protocol and applicable IRB requirements.
Any sharing of aggregate or de-identified study findings will be conducted in accordance with the approved protocol, participant consent/authorization, applicable law, and institutional data-use policies.
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
No
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
No
Esta información se obtuvo directamente del sitio web clinicaltrials.gov sin cambios. Si tiene alguna solicitud para cambiar, eliminar o actualizar los detalles de su estudio, comuníquese con register@clinicaltrials.gov. Tan pronto como se implemente un cambio en clinicaltrials.gov, también se actualizará automáticamente en nuestro sitio web. .