Acute Disease, Aging, Biomarkers, Critical Illness, Digital Health, Disease Progression, Remote Patient Monitoring, Wearable Electronic Devices
Conditions
Keywords
Acute Clinical Deterioration, Acute Disease, Disease Progression, Critical Illness, Clinical Deterioration Prediction, Digital Biomarkers, Biomarkers, Multimodal Biomarkers, Physiologic Monitoring, Wearable Electronic Devices, Remote Patient Monitoring, Digital Health, Predictive Analytics, Machine Learning, Longitudinal Monitoring, ECG, Photoplethysmography, Oxygen Saturation, Gait, Sleep, Voice Biomarkers, Cough Biomarkers, 72-Hour Prediction
Brief summary
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.
Detailed description
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.
Interventions
OTHERMultimodal Digital Biomarker Monitoring
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.
Sponsors
Truway Health, Inc.
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Predictive Accuracy of Multimodal Digital Biomarker Fusion for Acute Clinical Deterioration Within 72 Hours | At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode. | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Time-Dependent Predictive Performance of Individual and Combined Digital Biomarkers | At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode. | 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. |
| Lead Time to Detection of Acute Clinical Deterioration | For each protocol-defined acute clinical deterioration event, during the 72 hours preceding event occurrence and ending at the time of event occurrence. | 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. |
| Incremental Predictive Value of Multimodal Signal Relationships | At initiation of each prespecified prediction episode and 72 hours after initiation of each prespecified prediction episode. | 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. |
| Model Calibration for 72-Hour Clinical Deterioration Risk | At 72 hours after initiation of each prespecified prediction episode. | 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. |
| Robustness of Multimodal Prediction Across Participant and Clinical Subgroups | At 72 hours after initiation of each prespecified prediction episode. | 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. |
Countries
United States
Contacts
PRINCIPAL_INVESTIGATORGavin C Solomon, MD
Truway Health, Inc.
Outcome results
None listed