Multimodal Digital Biomarker Prediction of Acute Clinical Deterioration (FUSION-72)
2026年9月9日 更新者：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.
研究概览
地位
邀请报名
详细说明
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.
研究类型
观察性的
注册 (估计的)
10000
联系人和位置
本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。
学习地点
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New York
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New York、New York、美国、10016
- Truway Health, Inc.
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参与标准
研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。
资格标准
适合学习的年龄
- 成人
- 年长者
接受健康志愿者
不
取样方法
非概率样本
研究人群
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.
描述
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.
学习计划
本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。
研究是如何设计的？
设计细节
队列和干预
团体/队列 |
干预/治疗 |
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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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研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
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Predictive Accuracy of Multimodal Digital Biomarker Fusion for Acute Clinical Deterioration Within 72 Hours
大体时间：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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次要结果测量
结果测量 |
措施说明 |
大体时间 |
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Time-Dependent Predictive Performance of Individual and Combined Digital Biomarkers
大体时间：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
大体时间：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
大体时间：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
大体时间：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
大体时间：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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合作者和调查者
在这里您可以找到参与这项研究的人员和组织。
调查人员
- 首席研究员：Gavin C Solomon, MD、Truway Health, Inc.
出版物和有用的链接
负责输入研究信息的人员自愿提供这些出版物。这些可能与研究有关。
一般刊物
- 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.
有用的网址
- 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.
研究记录日期
这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。
研究主要日期
学习开始 (实际的)
2026年9月1日
初级完成 (估计的)
2038年9月30日
研究完成 (估计的)
2039年3月31日
研究注册日期
首次提交
2026年9月1日
首先提交符合 QC 标准的
2026年9月9日
首次发布 (实际的)
2026年9月14日
研究记录更新
最后更新发布 (实际的)
2026年9月14日
上次提交的符合 QC 标准的更新
2026年9月9日
最后验证
2026年9月1日
更多信息
与本研究相关的术语
关键字
其他研究编号
- TH-FUSION72-2026-001
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)？
不
IPD 计划说明
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.
药物和器械信息、研究文件
研究美国 FDA 监管的药品
不
研究美国 FDA 监管的设备产品
不
此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.