A Clinical Validation Study to Evaluate the Diagnostic Ac... | NCT06082791

Studies
A Clinical Validation Study to Evaluate the Diagnostic Accuracy of a Cough-based Auxiliary Diagnosis Algorithm for COPD

NCT06082791

Sponsor

Ruijin Hospital

Status

Unknown status

Last Update Posted

Oct 13, 2023Actual

Enrollment

500Estimated

Phase

Not provided

Conditions

COPD
Cough
Algorithm

Interventions

Not provided

Countries

China

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Sep 2023

Overall Recruitment Status or Expanded Access Status

Unknown status

Last Known Status

Recruiting

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jul 1, 2023Actual

Primary Completion Date

Dec 30, 2023Estimated

Completion Date

Dec 30, 2024Estimated

First Submitted Date

Oct 8, 2023

First Submission Date that Met QC Criteria

Oct 8, 2023

First Posted Date

Oct 13, 2023Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Oct 8, 2023

Last Update Posted Date

Oct 13, 2023Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Not provided

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief Summary

Cough is one of the most common symptoms in COPD patients, and its different characteristics in amplitude, frequency and waveform can be analyzed by intelligent algorithms for the auxiliary diagnosis of COPD. The current study is to conduct external clinical validation of COPD auxiliary diagnosis algorithm and lung function prediction algorithm, and preliminatively verify the performance of the algorithm, with the ultimate goal of developing a portable and rapid COPD auxiliary diagnosis and condition assessment tool that can be used at home.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

500Estimated

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Positive Agreement Rate (PPA)	Positive Agreement Rate (PPA) of auxiliary Diagnosis Algorithm for COPD	12.30.2024

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

patients with stable COPD were recruited for this study. Inclusion criteria were age ≥40 years and a diagnosis of COPD

Exclusion Criteria:

Exclusion criteria were acute exacerbations of respiratory symptoms within the past 2 weeks or significant comorbid cardiovascular, neurological, musculoskeletal, immunological, or infectious diseases.

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

40 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

three cohorts are included COPD respiratory disease without cold healthy people

Sampling Method

Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Jiemin QU, pHD	Contact	15390460355		15390460355@163.com

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine	Recruiting	Shanghai	Shanghai Municipality	200000	China

References Module

Citations

PubMed Identifier	Type	Citation	Retractions
41896558	Derived	Zhou J, Huang J, Wang Q, Yan J, Cao H, Huang L, Chen S, Ruan X, Zhu W, Mao J, Liu Y, Bu Z, Yang M, Wang Q, Zhou Y, Fan E, Tong L, Sun X, Zhao D, Wang P, Zhou M, Qu J. A cough sound-based deep learning algorithm for accessible prompt detection of chronic obstructive pulmonary disease with smartphones. NPJ Prim Care Respir Med. 2026 Mar 28;36(1):32. doi: 10.1038/s41533-026-00486-6.

Label	Type	Description	Intervention Names
COPD
other respiratory disease without COPD
healthy people without respiratory disease

ID	Term
D029424	Pulmonary Disease, Chronic Obstructive
D003371	Cough

ID	Term
D008173	Lung Diseases, Obstructive
D008171	Lung Diseases
D012140	Respiratory Tract Diseases
D002908	Chronic Disease