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Multimodal Data Fusion-Based Model for Predicting Difficult Airway in Ankylosing Spondylitis: A Retrospective Study

Multimodal Data Fusion-Based Model for Predicting Difficult Airway in Ankylosing Spondylitis: A Retrospective Study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600115966
Enrollment
Unknown
Registered
2026-01-04
Start date
2026-01-10
Completion date
Unknown
Last updated
2026-01-05

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

Conditions

Ankylosing Spondylitis

Interventions

Gold Standard:The clinical diagnosis of difficult airway, based on the Cormack-Lehane grade assessed by an experienced anesthesiologist using direct laryngoscopy as documented in the anesthesia record
Index test:A machine learning prediction model built on multimodal data (including spinal imaging features, clinical physiological parameters, and laboratory indicators). The model will output a predi

Sponsors

The Third People's Hospital of Chengdu
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 65 Years

Inclusion criteria

Inclusion criteria: All patients diagnosed with ankylosing spondylitis who underwent elective surgery at Chengdu Third People's Hospital from December 2019 to August 2025 will be included in this study.

Exclusion criteria

Exclusion criteria: Patients with missing imaging data or incomplete airway management records will be excluded.

Design outcomes

Primary

MeasureTime frame
Area under the receiver operating characteristic curve (AUC) of the prediction model;

Secondary

MeasureTime frame
Calibration performance of the prediction model;Identification and ranking of key predictive features;Incremental predictive value of multimodal data fusion;

Countries

China

Contacts

Public ContactJuan Tan

The Third People's Hospital of Chengdu

1254866069@qq.com+86 155 2833 0706

Outcome results

None listed

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