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A risk prediction model for high-flow nasal cannula therapy failure in patients with new-defined ARDS based on machine learning

A risk prediction model for high-flow nasal cannula therapy failure in patients with new-defined ARDS based on machine learning

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600122410
Enrollment
Unknown
Registered
2026-04-13
Start date
2026-04-13
Completion date
Unknown
Last updated
2026-04-20

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

Conditions

Acute Respiratory Distress Syndrone

Interventions

Case Group :None

Sponsors

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

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1.Age = 18 years; 2.Having received HFNC therapy with a flow rate =30 L/min; 3.Meeting the diagnostic criteria for ARDS under the new 2023 definition;

Exclusion criteria

Exclusion criteria: 1.Patients who received invasive mechanical ventilation or non-invasive mechanical ventilation within 72 hours phttp://47.105.96.75:58008/jh/qa_ba.htmlrior to HFNC; 2.Patients who declined intubation or cardiopulmonary resuscitation before the initiation of HFNC; 3.Patients who underwent intubation or died within 2 hours after the initiation of HFNC; 4.excessive missing data for key predictive variables; 5.Patients who are pregnant or breastfeeding;

Design outcomes

Primary

MeasureTime frame
HFNC failure rate;—

Secondary

MeasureTime frame
else clinical outcome;Laboratory parameters;—

Countries

China

Contacts

Public ContactHongping Qu

Ruijin Hospital, Shanghai Jiao Tong University School of Medicine

hongpingqu0412@hotmail.com+86 21 6437 0045

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Apr 23, 2026