Predictors of ICU Mortality among Mechanically Ventilated Patients: An Inception Cohort Study from a Tertiary Care Center in Addis Ababa, Ethiopia
Bibliographic record
Abstract
Background. Mechanical ventilation is a life-saving intervention for patients with critical illnesses, yet it is associated with higher mortality in resource-constrained settings. This study intended to determine factors associated with the mortality of mechanically ventilated adult intensive care unit (ICU) patients. Methods. A one-year retrospective inception cohort study was conducted using manual chart review in ICU patients (age >13) admitted to Tikur Anbessa Specialized Hospital (Addis Ababa, Ethiopia) from September 2019 to September 2020; mechanically ventilated patients were followed to hospital discharge. Demographic, clinical, and outcome data were collected; logistic regression was used to determine mortality predictors in the ICU. Result. A total of 160 patients were included; 85/160 (53.1%) were females and the mean (SD) age was 38.9 (16.2) years. The commonest indication for ICU admission was a respiratory problem (n = 97/160, 60.7%). ICU and hospital mortality were 60.7% (n = 97/160) and 63.1% (n = 101/160), respectively. Coma (Glasgow Coma Score <8 or 7 with an endotracheal tube (7T)) (adjusted odds ratio [AOR] 6.3, 95% confidence interval 1.19–33.00), cardiovascular diagnosis (AOR 5.05 [1.80–14.15]), and a very low serum albumin level (<2 g/dl) (AOR 4.9 [1.73–13.93]) were independent predictors of mortality ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>P</a:mi> <a:mo><</a:mo> <a:mn>0.05</a:mn> </a:math> ). The most commonly observed complication was ICU acquired infection (n = 48, 30%). Conclusions. ICU mortality in ventilated patients is high. Coma, a very low serum albumin level (<2 g/dl), and cardiovascular diagnosis were independent predictors of mortality. A multifaceted approach focused on developing and implementing context appropriate guidelines and improving skilled healthcare worker availability may prove effective in reducing mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".