Author: Rekha Ravutha Gounden
Ravutha Gounden, Rekha, 2025 Patient predictors of requiring a tracheostomy and associated effects on length of stay and mortality in the Intensive Care Unit, Flinders University, College of Medicine and Public Health
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Rationale
Limited clinical data exists at identifying patients who are more likely to receive a tracheostomy. Prolonged mechanical ventilation is the most common reason for patients receiving a tracheostomy in ICU. Many studies have assessed effects of tracheostomy in mechanically ventilated patients, however there is minimal research involving Australian and New Zealand patients around prolonged mechanically ventilated patients with a tracheostomy (defined as 7 days for this study). Understanding the potential predictors and outcomes of patients requiring a tracheostomy in; and the outcomes tracheostomy has on prolonged mechanically ventilated patients, potentially will assist clinicians treating this group of patients in management decisions.
Objectives
To identify the predictors of requiring a tracheostomy in Australian and NZ ICUs and to determine the associated mortality rates and ICU length of stay in prolonged mechanically ventilated patients with a tracheostomy.
Methods
A retrospective analysis was conducted using data from Australian and NZ Intensive Care Society (ANZICS) Centre of Outcome and Research Evaluation (CORE) database pertaining to patients >16 years that required tracheostomies and subsequently underwent mechanical ventilation 168 hours from 2017-2021.
Logistic regression assessed the contribution of patient factors on the likelihood of receiving tracheostomies in 3 patient cohorts – all ICU patients, mechanically ventilated and prolonged mechanically ventilated patients. Each model contained 20 variables (age, sex, chronic respiratory, cardiovascular, liver, renal-comorbidities, chronic immunosuppressive disease, chronic immunosuppressive therapy, AIDS, hepatic failure, lymphoma, metastatic malignancy, leukaemia, actively immunosuppressed, cirrhosis, diabetes, RRT, pre-ICU hours, ICU length of stay (LOS) and APACHE III score).
TELASSO regression analysis was performed on prolonged mechanically ventilated patients with a tracheostomy and their LOS and mortality. APACHE III score and 19 independent variables (age, sex, chronic respiratory, cardiovascular, liver, renal-comorbidities, chronic immunosuppressive disease, chronic immunosuppressive therapy, AIDS, hepatic failure, lymphoma, metastatic malignancy, leukaemia, actively immunosuppressed, cirrhosis, diabetes, frailty, indigenous, hospital BMI) were adjusted for in each model.
Results
All 3 models were statistically significant at distinguishing between patients more likely to receive a tracheostomy; All ICU patients 2 (32, N=430797) = 21716.03, p<0.00; mechanically ventilated patients 2 (32, N=133481) = 15413.9, p<0.000; and prolonged mechanically ventilated patients 2(32, N=11138) = 2923.9, p<0.000.
The strongest predictor of a patient requiring a tracheostomy was chronic immunosuppressive disease (OR 1.3). Increasing age was associated with decreased likelihood of a tracheostomy. There was a 3.6% increase in associated mortality in patients with a tracheostomy [95% CI 2.58, 4.75] when the separate APACHE III components were adjusted for, compared to no tracheostomy (11.8% mortality, [95%CI 11.6, 11.9]). Patients who required a tracheostomy had an increased ICU LOS by approximately 35 days in each group.
Conclusion
Prolonged mechanically ventilated patients with a tracheostomy are associated with increased mortality rates and extended ICU LOS compared to patients who did not require a tracheostomy. This study also identified comorbidities significantly associated with a critically ill patient requiring a tracheostomy. The increased rates of mortality in patients who are clinically selected as requiring a tracheostomy needs further research to determine a relationship with mortality and underlying medical comorbidities in these patients.
Keywords: tracheostomy, length of stay, ICU, Intensive Care Unit, patient predictors
Subject: Surgery thesis
Thesis type: Masters
Completed: 2025
School: College of Medicine and Public Health
Supervisor: Eng Ooi