Ignite Creation Date:
2025-12-25 @ 2:01 AM
Ignite Modification Date:
2026-01-04 @ 4:41 AM
Study NCT ID:
NCT05423860
Status:
RECRUITING
Last Update Posted:
2022-10-14
First Post:
2022-04-29
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Phase I Human Analytics (HALO) Study
Sponsor:
HALO Diagnostics
Organization:
Study Overview
Official Title:
This is a Human Analytics Longitudinal Observational (HALO) Study. A Phase I Study to Analyze All Available Biomarkers and Determinants of Health to Increase Diagnostic Accuracy While Reducing the Time to Diagnosis of Disease.
Status:
RECRUITING
Status Verified Date:
2022-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
HALO
Brief Summary:
Discover, optimize, standardize, and validate clinical-trial measures and biomarkers used to diagnose and differentiate cardiovascular, oncologic, neurologic, and other diseases and disorders. Specifically, our research study endeavors to improve disease and disorder diagnosis to the earliest clinical states, in preclinical states, and to develop ensemble multivariate biomarker risk scores leading to cardiovascular, oncologic, neurologic, and other diseases and disorders.
Additionally, the study aims to:
* Evaluate data analysis techniques to improve diagnostic accuracy and reduce time to diagnosis.
* Evaluate data analysis techniques to improve risk stratification for participants through machine learning algorithms.
* Direct participants to relevant and applicable clinical trials.
Additionally, the study aims to:
* Evaluate data analysis techniques to improve diagnostic accuracy and reduce time to diagnosis.
* Evaluate data analysis techniques to improve risk stratification for participants through machine learning algorithms.
* Direct participants to relevant and applicable clinical trials.
Detailed Description:
Electronic medical records contain data that may indicate increased risk for certain diseases and disorders, but clinicians cannot easily discern the subtle patterns required to change their diagnostic and treatment patterns. This study seeks to use machine learning and data analysis techniques to increase diagnostic confidence and reduce time-to-diagnosis related to cardiovascular, oncologic, neurologic, and other diseases and disorders.
The study endeavors to develop ensemble multivariate biomarker risk scores to predict future development of diseases and disorders, improve diagnosis in preclinical states and increase diagnostic accuracy in the earliest clinical states. We also aim to evaluate data analysis techniques to improve diagnostic accuracy and reduce time to diagnosis, improve risk stratification for participants through machine learning algorithms and direct participants to relevant and applicable clinical trials upon physician review, approval and recommendation.
The study endeavors to develop ensemble multivariate biomarker risk scores to predict future development of diseases and disorders, improve diagnosis in preclinical states and increase diagnostic accuracy in the earliest clinical states. We also aim to evaluate data analysis techniques to improve diagnostic accuracy and reduce time to diagnosis, improve risk stratification for participants through machine learning algorithms and direct participants to relevant and applicable clinical trials upon physician review, approval and recommendation.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
True
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
Secondary ID Infos
| Secondary ID | Type | Domain | Link | View |
|---|---|---|---|---|
| WIRB Pr. No.: 20213955 | OTHER | WIRB Copernicus Group (WCG) | View |