Multimodal Digital Biomarker Prediction of Acute Clinical Deterioration (FUSION-72)
9 de setembro de 2026 atualizado 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.
Visão geral do estudo
Status
Status
Inscrevendo-se por convite
Condições
Condições
Intervenção / Tratamento
Intervenção / Tratamento
Descrição detalhada
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 estudo
Tipo de estudo
Observacional
Inscrição (Estimado)
Inscrição
10000
Contactos e Locais
Esta seção fornece os detalhes de contato para aqueles que conduzem o estudo e informações sobre onde este estudo está sendo realizado.
Locais de estudo
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New York
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New York, New York, Estados Unidos, 10016
- Truway Health, Inc.
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Critérios de participação
Os pesquisadores procuram pessoas que se encaixem em uma determinada descrição, chamada de critérios de elegibilidade. Alguns exemplos desses critérios são a condição geral de saúde de uma pessoa ou tratamentos anteriores.
Critérios de elegibilidade
Critérios de elegibilidade
Idades elegíveis para estudo
- Adulto
- Adulto mais velho
Aceita Voluntários Saudáveis
Não
Método de amostragem
Amostra Não Probabilística
População do estudo
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.
Descrição
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.
Plano de estudo
Esta seção fornece detalhes do plano de estudo, incluindo como o estudo é projetado e o que o estudo está medindo.
Como o estudo é projetado?
Detalhes do projeto
Número de grupos/coortes
1
Coortes e Intervenções
Grupo / CoorteGrupo / Coorte |
Intervenção / TratamentoIntervenção / Tratamento |
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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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O que o estudo está medindo?
Medidas de resultados primários
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
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Predictive Accuracy of Multimodal Digital Biomarker Fusion for Acute Clinical Deterioration Within 72 Hours
Prazo: 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 resultados secundários
Medidas de resultados secundários
Medida de resultado |
Descrição da medida |
Prazo |
|---|---|---|
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Time-Dependent Predictive Performance of Individual and Combined Digital Biomarkers
Prazo: 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
Prazo: 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
Prazo: 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
Prazo: 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
Prazo: 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
É aqui que você encontrará pessoas e organizações envolvidas com este estudo.
Patrocinador
Patrocinador
Investigadores
Investigadores
- Investigador principal: Gavin C Solomon, MD, Truway Health, Inc.
Publicações e links úteis
A pessoa responsável por inserir informações sobre o estudo fornece voluntariamente essas publicações. Estes podem ser sobre qualquer coisa relacionada ao estudo.
Publicações Gerais
- 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.
Links úteis
- 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.
Datas de registro do estudo
Essas datas acompanham o progresso do registro do estudo e os envios de resumo dos resultados para ClinicalTrials.gov. Os registros do estudo e os resultados relatados são revisados pela National Library of Medicine (NLM) para garantir que atendam aos padrões específicos de controle de qualidade antes de serem publicados no site público.
Datas Principais do Estudo
Início do estudo (Real)
Início do estudo
1 de setembro de 2026
Conclusão Primária (Estimado)
Conclusão Primária
30 de setembro de 2038
Conclusão do estudo (Estimado)
Conclusão do estudo
31 de março de 2039
Datas de inscrição no estudo
Enviado pela primeira vez
Enviado pela primeira vez
1 de setembro de 2026
Enviado pela primeira vez que atendeu aos critérios de CQ
Enviado pela primeira vez que atendeu aos critérios de CQ
9 de setembro de 2026
Primeira postagem (Real)
Primeira postagem
14 de setembro de 2026
Atualizações de registro de estudo
Última Atualização Postada (Real)
Última Atualização Postada
14 de setembro de 2026
Última atualização enviada que atendeu aos critérios de controle de qualidade
Última atualização enviada que atendeu aos critérios de controle de qualidade
9 de setembro de 2026
Última verificação
Última verificação
1 de setembro de 2026
Mais Informações
Termos relacionados a este estudo
Palavras-chave
- Maneira de andar
- Dorme
- Progressão da doença
- Biomarcadores
- Doença grave
- Fotopletismografia
- Saúde digital
- ECG
- Aprendizado de máquina
- Dispositivos eletrônicos vestíveis
- Doença Aguda
- Saturação de oxigênio
- Monitoramento Remoto do Paciente
- Biomarcadores digitais
- Análise preditiva
- Monitoramento Fisiológico
- Biomarcadores multimodais
- Biomarcadores de voz
- Longitudinal Monitoring
- Acute Clinical Deterioration
- Clinical Deterioration Prediction
- Cough Biomarkers
- 72-Hour Prediction
Termos MeSH relevantes adicionais
Outros números de identificação do estudo
Outros números de identificação do estudo
- TH-FUSION72-2026-001
Plano para dados de participantes individuais (IPD)
Planeja compartilhar dados de participantes individuais (IPD)?
NÃO
Descrição do plano 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.
Informações sobre medicamentos e dispositivos, documentos de estudo
Estuda um medicamento regulamentado pela FDA dos EUA
Não
Estuda um produto de dispositivo regulamentado pela FDA dos EUA
Não
Essas informações foram obtidas diretamente do site clinicaltrials.gov sem nenhuma alteração. Se você tiver alguma solicitação para alterar, remover ou atualizar os detalhes do seu estudo, entre em contato com register@clinicaltrials.gov. Assim que uma alteração for implementada em clinicaltrials.gov, ela também será atualizada automaticamente em nosso site .