Delayed Tracheostoma Healing Following Decannulation in Long-term Tracheostomy Patients
Conditions
Keywords
Stoma healing after decannulation, tracheostomy, predictive model, rehabilitation patients
Brief summary
Prolonged tracheostomy is prone to induce chronic airway mucosal injury and tracheal cartilage degeneration, which not only elevates the risks of post-decannulation complications such as persistent fistula non-healing, airway stenosis and recurrent infections, but also impairs ventilatory, phonatory and deglutitive functions, thereby exerting a severe adverse impact on patients' prognosis and quality of life.Accordingly, the retrospective observational study aimed to characterize stoma healing time after decannulation, identify key predictive factors, and develop and validate a clinically useful prediction model. These findings will offer evidence-based guidance for future researchers in selecting appropriate modeling methodologies and provide a scientific foundation for the development of targeted clinical intervention and prevention strategies.
Detailed description
Objective To develop and validate a predictive model for post-decannulation stoma healing in patients with prolonged tracheostomy, and screen high-risk predictors of poor wound healing. Methods This retrospective observational study will recruit eligible patients and split subjects into training and validation cohorts at a fixed ratio. Candidate clinical and stoma-related variables will be screened via LASSO regression. Multiple prediction models including logistic regression, decision tree and random forest will be built, and evaluated by AUC, Brier score and DCA. Expected Outcomes Core predictive indicators linked to delayed stoma healing will be identified, and the optimal model with robust predictive performance will be determined to stratify patients into high-risk groups. Expected Conclusions The optimal model can achieve accurate risk stratification for stoma healing disorders. Targeted interventions including personalized nutrition, standardized stoma care and activity management will be recommended for high-risk patients.
Interventions
No interventional measures were performed; patients were grouped according to natural stoma healing outcome.
Sponsors
Study design
Eligibility
Inclusion criteria
* Successful decannulation,it was defined as no need for tracheostomy tube reinsertion within 48 hours after decannulation * Complete clinical data * Voluntary participation in the study and signed informed consent form
Exclusion criteria
* Decannulation failure, with reinsertion of a tracheostomy tube or endotracheal tube * Discharge from hospital before the tracheostomy stoma healed * Refusal to participate in the study or withdrawal from the study midway
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Time to Complete Epithelial Closure of the Tracheostomy Stoma After Decannulation | From the date of tracheostomy tube decannulation to the date of spontaneous complete epithelial closure of the tracheostomy stoma, assessed up to 30 days after decannulation | Time to complete epithelial closure is defined as the number of days from tracheostomy tube decannulation to spontaneous complete epithelial closure of the tracheostomy stoma, as determined by clinical assessment. Participants who do not achieve complete epithelial closure within 30 days after decannulation will be censored at Day 30. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Correlates of delayed tracheostomy stoma healing after decannulation | Pre-decannulation baseline assessment through confirmation of complete epithelial closure of the tracheostomy stoma; all assessments will be completed within 30 days after decannulation | Baseline demographic characteristics, laboratory parameters, rehabilitation-related variables, and tracheostomy-related clinical indicators will be collected at the pre-decannulation baseline assessment. Delayed tracheostomy stoma healing is defined based on the time (in days) to spontaneous complete epithelial closure of the tracheostomy stoma after decannulation, assessed via routine clinical wound visual inspection. Machine learning algorithms will be applied to screen candidate predictive variables and quantify the relative predictive contribution of each baseline variable to the time to complete stoma epithelial closure. |
Countries
China
Contacts
Pulmonary Rehabilitation Center, Beijing Rehabilitation Hospital of Capital Medical University