Machine Learning Approach to Predict Tracheal Necrosis after Total Pharyngolaryngectomy and Free Jejunal Transfer
Not Applicable
- Conditions
- All patients underwent TPL and FJT for biopsy-proven squamous cell carcinoma (SCC) at our institution from April 2010 to April 2023 were included. Exclusion criteria were: (1) patients underwent TPL and FJT for non-SCC, (2) patients underwent TPL and FJT in conjunction with total esophagectomy and (3) patients with prior history of total esophagectomy.
- Registration Number
- JPRN-UMIN000051556
- Lead Sponsor
- Department of Plastic and Reconstructive Surgery, National Cancer Center Hospital East 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Complete: follow-up complete
- Sex
- All
- Target Recruitment
- 395
Inclusion Criteria
Not provided
Exclusion Criteria
Exclusion criteria were: (1) patients underwent TPL and FJT for non-SCC, (2) patients underwent TPL and FJT in conjunction with total esophagectomy and (3) patients with prior history of total esophagectomy.
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Primary endpoint was the incidence of tracheal necrosis.
- Secondary Outcome Measures
Name Time Method