Risk Factors for LRTI in Head and Neck Cancer Patients with Tracheostomy

This study retrospectively enrolled 235 head and neck cancer patients with intraoperative tracheotomy from June 2018 to November 2022. It analyzes clinical data including subglottic sputum aspiration volume (SSAV) to identify risk factors and construct a predictive model for lower respiratory tract infection (LRTI). The model was validated on another 66 patients recruited from March to May 2023.

Use Cases

• Construct a logistic regression model to predict LRTI using independent risk factors like PCT, CRP, tumor T stage, and SSAV Range.
• Analyze the association between SSAV changes (SSAV Max, SSAV Min) and the onset of postoperative LRTI.
• Validate a predictive Nomogram for LRTI risk using the 66-patient validation cohort.
• Perform pathogen analysis on lower respiratory tract infections to identify prevalent microbes like Pseudomonas aeruginosa.

Strengths

• Model validation on a separate cohort of 66 patients, achieving an AUC of 87.9%.
• Clinical data from 235 patients collected over a defined period from June 2018 to November 2022.
• Analysis includes specific, measured clinical parameters like WBC, PCT, CRP, and multiple SSAV metrics.

Limitations

• Dataset scope is limited to a single study's 235 primary and 66 validation patients.
• Data is presented in a research paper PDF (515.5 KB), not as a raw, machine-readable tabular dataset.
• Clinical focus is specific to head and neck cancer patients with tracheostomy, limiting generalizability.

Provenance

Source

Jie Zhang via figshare.

Collection Method

Retrospective clinical study.

Time Range

Primary cohort: June 2018 to November 2022. Validation cohort: March 2023 to May 2023.

Freshness

Last updated March 2026.