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NCT05132751
Machine Learning Ventilator Decision System VS. Standard Controlled Ventilation
NA trial testing Machine Learning Ventilator Decision System in Mechanical Ventilation in 300 participants. Status unknown.
1 January 2022
Quick facts
| Lead sponsor | Hu Anmin |
|---|---|
| Phase | NA |
| Status | Status unknown |
| Study type | INTERVENTIONAL |
| Allocation | randomized |
| Design | parallel |
| Masking | triple |
| Primary purpose | treatment |
| Enrollment | 300 |
| Start date | 1 January 2022 |
| Primary completion | 1 January 2022 |
| Estimated completion | 1 December 2024 |
Drugs / interventions tested
- Machine Learning Ventilator Decision System
Conditions studied
- Mechanical Ventilation — all drugs for Mechanical Ventilation →
- Critically Ill Patients — all drugs for Critically Ill Patients →
Sponsor
Hu Anmin
Who can join
18 and older, any sex, with Mechanical Ventilation or Critically Ill Patients. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Ventilator-induced lung injury is associated with increased morbidity and mortality. Despite intense efforts in basic and clinical research, an individualized ventilation strategy for critically ill patients remains a major challenge. However, an individualized mechanical ventilation approach remains a challenging task: A multitude of factors, e.g., lab values, vitals, comorbidities, disease progression, and other clinical data must be taken into consideration when choosing a patient's specific optimal ventilation regime. The aim of this work was to evaluate the machine learning ventilator decision system, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. Compare with standard controlled ventilation, to test whether the clinical application of the machine learning ventilator decision system reduces mechanical ventilation time and mortality.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT05132751
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT05132751 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Hu Anmin
- Last refreshed: 24 November 2021
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