Machine Learning-based Early Clinical Warning of High-risk Patients
1 other identifier
interventional
1,000
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Jun 2022
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
June 1, 2022
CompletedFirst Submitted
Initial submission to the registry
June 4, 2022
CompletedFirst Posted
Study publicly available on registry
June 8, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 1, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
December 1, 2023
CompletedDecember 1, 2022
November 1, 2022
1 year
June 4, 2022
November 29, 2022
Conditions
Outcome Measures
Primary Outcomes (1)
28-day all cause mortality
28-day all cause mortality
28 days
Secondary Outcomes (1)
Hospital mortality
through study completion, an average of 1 month
Study Arms (2)
AI group
EXPERIMENTALpatients evaluated by early warning platform
usual care group
NO INTERVENTIONpatients not evaluated by early warning platform
Interventions
High risk inpatients will be evaluated by early warning platform
Eligibility Criteria
You may qualify if:
- Patients who use ECG monitoring
- Age ≥ 18 years old
- Understand and sign an informed consent form
You may not qualify if:
- Pregnancy or lactation
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Zhongda Hospital, Southeast University
Nanjing, Jiangsu, 210009, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Songqiao Liu, PhD.
Zhongda Hospital, Southeast University, China
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- NON RANDOMIZED
- Masking
- NONE
- Purpose
- HEALTH SERVICES RESEARCH
- Intervention Model
- SEQUENTIAL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Head of Information Division
Study Record Dates
First Submitted
June 4, 2022
First Posted
June 8, 2022
Study Start
June 1, 2022
Primary Completion
June 1, 2023
Study Completion
December 1, 2023
Last Updated
December 1, 2022
Record last verified: 2022-11