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Integration of Electrical Impedance Tomography with Machine Learning for Weaning Prediction: A Multi-center Retrospective Study

Integration of Electrical Impedance Tomography with Machine Learning for Weaning Prediction: A Multi-center Retrospective Study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500101979
Enrollment
Unknown
Registered
2025-05-06
Start date
2025-05-07
Completion date
Unknown
Last updated
2025-05-12

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

Conditions

respiratory failure receiving mechanical ventilation

Interventions

Pendelluft group:Not applicable
none Pendelluft group:Not applicable

Sponsors

Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. acute respiratory failure with pressure support ventilation (PSV) expected within 48 hours of SBT. 2. prolonged mechanical ventilation (>= 7 days of ventilation).

Exclusion criteria

Exclusion criteria: 1. cardiorespiratory instability. 2. severe neurologic deficits. 3. bilateral phrenic nerve injury. 4. previous ICU admission and a life expectancy of less than three months. 5. contraindication to EIT examination.

Design outcomes

Primary

MeasureTime frame
Ventilator-free days at day 28;ICU length of stay;28-day mortality;Reintubation;ventilator-associated pneumonia;Weaning percent days at day 28;—

Secondary

MeasureTime frame
Successful weaning;Respiratory rate;PaO2/FiO2 at day 7;Quality of life score;—

Countries

China

Contacts

Public ContactHongping Qu

Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine

qhp10516@rjh.com.cn+86 21 6437 0045

Outcome results

None listed

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