Multimodal Digital Biomarker Prediction of Acute Clinical Deterioration (FUSION-72)
2026년 9월 9일 업데이트: 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.
연구 개요
상태
상태
초대로 등록
정황
정황
개입 / 치료
개입 / 치료
상세 설명
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.
연구 유형
연구 유형
관찰
등록 (추정된)
등록
10000
연락처 및 위치
이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.
연구 장소
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New York
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New York, New York, 미국, 10016
- Truway Health, Inc.
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참여기준
연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.
자격 기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
아니
샘플링 방법
비확률 샘플
연구 인구
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.
설명
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.
공부 계획
이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.
연구는 어떻게 설계됩니까?
디자인 세부사항
그룹/코호트 수
1
코호트 및 개입
그룹/코호트그룹/코호트 |
개입 / 치료개입 / 치료 |
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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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연구는 무엇을 측정합니까?
주요 결과 측정
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
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Predictive Accuracy of Multimodal Digital Biomarker Fusion for Acute Clinical Deterioration Within 72 Hours
기간: 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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2차 결과 측정
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Time-Dependent Predictive Performance of Individual and Combined Digital Biomarkers
기간: 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
기간: 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
기간: 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
기간: 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
기간: 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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공동 작업자 및 조사자
여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.
수사관
수사관
- 수석 연구원: Gavin C Solomon, MD, Truway Health, Inc.
간행물 및 유용한 링크
연구에 대한 정보 입력을 담당하는 사람이 자발적으로 이러한 간행물을 제공합니다. 이것은 연구와 관련된 모든 것에 관한 것일 수 있습니다.
일반 간행물
- 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.
유용한 링크
- 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.
연구 기록 날짜
이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.
연구 주요 날짜
연구 시작 (실제)
연구 시작
2026년 9월 1일
기본 완료 (추정된)
기본 완료
2038년 9월 30일
연구 완료 (추정된)
연구 완료
2039년 3월 31일
연구 등록 날짜
최초 제출
최초 제출
2026년 9월 1일
QC 기준을 충족하는 최초 제출
QC 기준을 충족하는 최초 제출
2026년 9월 9일
처음 게시됨 (실제)
처음 게시됨
2026년 9월 14일
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
마지막 업데이트 게시됨
2026년 9월 14일
QC 기준을 충족하는 마지막 업데이트 제출
QC 기준을 충족하는 마지막 업데이트 제출
2026년 9월 9일
마지막으로 확인됨
마지막으로 확인됨
2026년 9월 1일
추가 정보
이 연구와 관련된 용어
키워드
기타 연구 ID 번호
기타 연구 ID 번호
- TH-FUSION72-2026-001
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
아니요
IPD 계획 설명
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.
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
아니
미국 FDA 규제 기기 제품 연구
아니
이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .