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.
調査の概要
状態
招待による登録
詳細な説明
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.
研究の種類
観察的
入学 (推定)
10000
連絡先と場所
このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。
研究場所
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New York
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New York、New York、アメリカ、10016
- Truway Health, Inc.
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参加基準
研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
いいえ
サンプリング方法
非確率サンプル
調査対象母集団
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.
説明
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.
研究計画
このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。
研究はどのように設計されていますか？
デザインの詳細
コホートと介入
グループ/コホート |
介入・治療 |
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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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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
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Predictive Accuracy of Multimodal Digital Biomarker Fusion for Acute Clinical Deterioration Within 72 Hours
時間枠：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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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Time-Dependent Predictive Performance of Individual and Combined Digital Biomarkers
時間枠：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
時間枠：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
時間枠：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
時間枠：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
時間枠：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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協力者と研究者
ここでは、この調査に関係する人々や組織を見つけることができます。
スポンサー
捜査官
- 主任研究者：Gavin C Solomon, MD、Truway Health, Inc.
出版物と役立つリンク
研究に関する情報を入力する責任者は、自発的にこれらの出版物を提供します。これらは、研究に関連するあらゆるものに関するものである可能性があります。
一般刊行物
- 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.
便利なリンク
- 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.
研究記録日
これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。
主要日程の研究
研究開始 (実際)
2026年9月1日
一次修了 (推定)
2038年9月30日
研究の完了 (推定)
2039年3月31日
試験登録日
最初に提出
2026年9月1日
QC基準を満たした最初の提出物
2026年9月9日
最初の投稿 (実際)
2026年9月14日
学習記録の更新
投稿された最後の更新 (実際)
2026年9月14日
QC基準を満たした最後の更新が送信されました
2026年9月9日
最終確認日
2026年9月1日
詳しくは
本研究に関する用語
キーワード
その他の研究ID番号
- TH-FUSION72-2026-001
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
いいえ
IPD プランの説明
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.
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
いいえ
米国FDA規制機器製品の研究
いいえ
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。