Menu
Recruiting NCT05423860

Phase I Human Analytics (HALO) Study

— Observational Cardiovascular Diseases Cancer Dementia Traumatic Brain Injury

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: no interventions will be performed (observational).
Who it may be relevant to
Registry conditions: Cardiovascular Diseases, Cancer, Dementia, Traumatic Brain Injury. Basic parameters: 45 years — 90 years · Male.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
United States
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

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.

Overview

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.

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.

Interventions

  • Other no interventions will be performed (observational)
    Not applicable. (no interventions will be performed with this observational study

Primary outcome measures

  • Prostate cancer Gleason score [Time frame: Up to 5 years after treatment]
  • Prostate cancer ISUP grade group [Time frame: Up to 5 years after treatment]
  • Prostate cancer staging parameters [Time frame: Up to 5 years after treatment]
  • Prostate cancer specific mortality [Time frame: Up to 5 years]
Secondary outcome measures (5)
  • Lower urinary tract symptoms (LUTS) [Time frame: Up to 5 years after treatment]
  • Erectile function [Time frame: Up to 5 years after treatment]
  • Emotional well-being [Time frame: Up to 5 years after treatment]
  • Incontinence level [Time frame: Up to 5 years after treatment]
  • PI-RADS category [Time frame: Up to 5 years after treatment]

Eligibility criteria

Inclusion criteria

Treatment Naïve patients:

  • Male, 45 years of age or older.
  • Diagnosis of prostate adenocarcinoma.
  • Clinical stage T1c or T2a.
  • Gleason score of 7 (3+4 or 4+3) or less.
  • Three or fewer biopsy cores with prostate cancer.
  • PSA density not exceeding 0.375.
  • One, two, or three tumor suspicious regions identified on multiparametric MRI.
  • Negative radiographic indication of extra-capsular extent.
  • Karnofsky performance status of at least 70.
  • Estimated survival of 5 years or greater, as determined by treating physician.
  • Tolerance for anesthesia/sedation.
  • Ability to give informed consent.
  • At least 6 weeks since any previous prostate biopsy.
  • MR-guided biopsy confirmation of one or more MRI-visible prostate lesion(s) with Gleason score of 7 (3+4 or 4+3) or less.

Salvage candidates will be accepted upon physician referral.

Exclusion criteria

  • Presence of any condition (e.g., metal implant, shrapnel) not compatible with MRI.
  • Severe lower urinary tract symptoms as measured by an International Prostate Symptom Score (IPSS) of 20 or greater
  • History of other primary non-skin malignancy within previous three years.
  • Diabetes
  • Smoker

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

United States · 1 center
  • Desert Medical Imaging — Indian Wells

Publications

  • Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017 Apr 4;12(4):e0174944. doi: 10.1371/journal.pone.0174944. eCollection 2017. PMID 28376093
  • Wang X, Oldani MJ, Zhao X, Huang X, Qian D. A review of cancer risk prediction models with genetic variants. Cancer Inform. 2014 Sep 21;13(Suppl 2):19-28. doi: 10.4137/CIN.S13788. eCollection 2014. PMID 25288876

Identifiers

NCT: NCT05423860 · HALO Dx 001 · WIRB Pr. No.: 20213955

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗