NCT06798688

In This Study, the Sponsor Would Like to Collaborate with Institution and Investigator to Aggregate Participants Data and to Pilot Its Software Algorithm Using Machine Learning and Threshold Based Methods for Predicting Exacerbations and Deterioration Within a 60 Days Period Post-discharge

Software Algorithm Using Machine Learning and Threshold Based Methods for Predicting Exacerbations and Deterioration

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 20 (estimated)
• Sponsor: Respiree Pte Ltd · Industry
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

In this study, the sponsor would like to collaborate with Institution and Investigator to aggregate participants data and to pilot its software algorithm using machine learning and threshold based methods for predicting exacerbations and deterioration within a 60 days period post-discharge.

Conditions

• COPD

Interventions

Type	Name	Description
DEVICE	A non-invasive cardio-respiratory sensor will be applied on the subjects to measure parameters to identify exacerbations	This Study aims to pilot software algorithms based on respiratory features and hemodynamics for predicting exacerbations on a total of 20 participants with COPD. The end-points of this Study includes the following: 1. To validate respiratory-based biomarkers in models to predict exacerbations - benchmarking to be done versus physician assess exacerbations, emergency department visits, hospitalizations and any other visit. 2. To validate level of compliance, drop-out rate and if additional measures are required to get participants to follow-on

Timeline

• Start date: 2025-03-01
• Primary completion: 2025-07-01
• Completion: 2025-07-01
• First posted: 2025-01-29
• Last updated: 2025-01-29

Regulatory

• FDA-regulated device study