Machine Learning Approach to Predict Tracheal Necrosis after Total Pharyngolaryngectomy and Free Jejunal Transfer

Machine Learning Approach to Predict Tracheal Necrosis after Total Pharyngolaryngectomy and Free Jejunal Transfer - Machine Learning Approach to Predict Tracheal Necrosis after Total Pharyngolaryngectomy and Free Jejunal Transfer

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000051556

Enrollment

395

Registered

2023-07-08

Start date

2010-04-02

Completion date

Unknown

Last updated

2026-06-29

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

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.

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: 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

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.

Design outcomes

Primary

Measure	Time frame
Primary endpoint was the incidence of tracheal necrosis.	—

Countries

Japan

Contacts

Public ContactTakeaki Hidaka

National Cancer Center Hospital East, Kashiwa, Japan Department of Plastic and Reconstructive Surgery

tahidaka1986@gmail.com81471331111

Outcome results