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Identification of ARDS subphenotypes in mechanical ventilation based on machine learning method and the prediction of weaning in subphenotypes by P0.1 and RSBI

Identification of ARDS subphenotypes in mechanical ventilation based on machine learning method and the prediction of weaning in subphenotypes by P0.1 and RSBI

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500098642
Enrollment
Unknown
Registered
2025-03-11
Start date
2025-03-16
Completion date
Unknown
Last updated
2025-03-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

ARDS

Interventions

Observation group:None

Sponsors

The Affiliated Hospital of Guizhou Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 89 Years

Inclusion criteria

Inclusion criteria: Inclusion Criteria: 1. ARDS diagnosed according to the Berlin definition; 2. ICU stay duration greater than 24 hours; 3. Invasive mechanical ventilation; 4. Enrollment limited to patients admitted to the ICU for the first time.

Exclusion criteria

Exclusion criteria: Exclusion Criteria: 1. Patients under the age of 18 years; 2. Patients with ABGP/F value greater than 300 mmHg. Variables with missing values exceeding 30% are removed.

Design outcomes

Primary

MeasureTime frame
Weaning Success Rate;ICU in-hospital mortality;—

Countries

China

Contacts

Public ContactTangyan; Yang Changyan

The Affiliated Hospital of Guizhou Medical University

doctorshenfeng@163.com+86 135 1199 9117

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026