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Machine learning-based models for prediction of hypoxemia in deep sedation for endoscopic procedurs

Machine learning-based models for prediction of hypoxemia in deep sedation for endoscopic procedurs

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400082323
Enrollment
Unknown
Registered
2024-03-26
Start date
2023-10-12
Completion date
Unknown
Last updated
2024-04-01

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

Conditions

hypoxemia

Interventions

Case series:None

Sponsors

General Hospital of Ningxia Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: (1) Patients scheduled for painless gastroscopy treatment; (2) Patients who are informed, consent, and sign the informed consent form.

Exclusion criteria

Exclusion criteria: (1) Patients who cannot undergo anesthesia after assessment by an anesthesiologist; (2) Patients who refuse anesthesia after being informed of the related anesthesia risks by the anesthesiologist.

Design outcomes

Primary

MeasureTime frame
hypoxemia;Confusion Matrix;Accuracy;F1 Score;Area Under the ROC Curve, AUC;Recall;—

Secondary

MeasureTime frame
Precision;—

Countries

Chinese

Contacts

Public ContactYang Fan

General Hospital of Ningxia Medical University

f-yangfan@163.com+86 157 1958 3681

Outcome results

None listed

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