Machine Learning-based Early Clinical Warning of High-ris... | NCT05410171

Studies
Machine Learning-based Early Clinical Warning of High-risk Patients

NCT05410171

Sponsor

Southeast University, China

Status

Unknown status

Last Update Posted

Dec 1, 2022Actual

Enrollment

1,000Estimated

Phase

Not Applicable

Conditions

High-risk Patients
Risk Reduction
Machine Learning

Interventions

early warning platform

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

Nov 2022

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

Jun 1, 2022Actual

Primary Completion Date

Jun 1, 2023Estimated

Completion Date

Dec 1, 2023Estimated

First Submitted Date

Jun 4, 2022

First Submission Date that Met QC Criteria

Jun 4, 2022

First Posted Date

Jun 8, 2022Actual

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

Nov 29, 2022

Last Update Posted Date

Dec 1, 2022Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Songqiao Liu, Head of Information Division, Southeast University, ChinaPrincipal Investigator

Lead Sponsor

Southeast University, ChinaOTHER

Collaborators

Not provided

Oversight Module

Has Data Monitoring Committee (DMC)

Yes

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

Through the early warning platform for inpatients established by our hospital, the various indicators of patients collected in real time are carried out for automated intelligent evaluation and analysis, early warning of high-risk patients to assess the impact on patient prognosis and the impact on the occurrence of adverse events in inpatients.

Detailed Description

Build the early warning system.

Design Module

Study Type

Interventional

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Not provided

Target Follow-Up Duration

Not provided

Phases

Not Applicable

Interventional Study Design

Allocation

Biospecimen

No data available

No data is available for this block.

Enrollment

1,000Estimated

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
AI group	Experimental	patients evaluated by early warning platform	Device: early warning platform
usual care group	No Intervention	patients not evaluated by early warning platform

Interventions

Name	Type	Description	Arm Group Labels	Other Names
early warning platform	Device	High risk inpatients will be evaluated by early warning platform	AI group

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
28-day all cause mortality	28-day all cause mortality	28 days

Secondary Outcomes

Measure	Description	Time Frame
Hospital mortality	Hospital mortality	through study completion, an average of 1 month

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Patients who use ECG monitoring
Age ≥ 18 years old
Understand and sign an informed consent form

Exclusion Criteria:

Pregnancy or lactation

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

80 Years

Standard Ages

AdultOlder Adult

Study Population

Not provided

Sampling Method

Not provided

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Changde Wu	Contact	086-02583262550		liusongqiao@ymail.com

Overall Officials

Name	Affiliation	Role
Songqiao Liu, PhD.	Zhongda Hospital, Southeast University, China	Principal Investigator

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Zhongda Hospital, Southeast University	Recruiting	Nanjing	Jiangsu	210009	China