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

Not Applicable

Recruiting

• Conditions: High-risk Patients; Machine Learning; Risk Reduction

• Registration Number: NCT05410171
• Lead Sponsor: Southeast University, China

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.

Study Timeline

• Study Start: 2022-06-01
• Study First Submitted: 2022-06-04
• Study First Submitted QC: 2022-06-04
• Study First Posted: 2022-06-08
• Status Verified: 2022-11
• Last Update Submitted: 2022-11-29
• Last Update Posted: 2022-12-01
• Primary Completion: 2023-06-01
• Study Completion: 2023-12-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000

Inclusion Criteria

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

Exclusion Criteria

• Pregnancy or lactation

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SEQUENTIAL

Primary Outcome Measures

Name	Time	Method
28-day all cause mortality	28 days	28-day all cause mortality

Secondary Outcome Measures

Name	Time	Method
Hospital mortality	through study completion, an average of 1 month	Hospital mortality

Trial Locations

Locations (1)

Zhongda Hospital, Southeast University; 🇨🇳 Nanjing, Jiangsu, China