Patterns and predictors of early mortality among emergency department patients in Addis Ababa, Ethiopia
Bibliographic record
Abstract
BACKGROUND: Ethiopian emergency department (ED) patients have a considerable burden of illness and injury for which all-cause mortality rates have not previously been published. This study sought to characterize the burden of and to identify predictors for early all-cause mortality among patients presenting to the Tikur Anbessa Specialized Hospital ED (TASH-ED) in Ethiopia. METHODS: Data was prospectively collected from the records of all patients who died within 72 h of ED presentation. Pearson's Chi square and Fisher's exact tests were used to investigate associations between two outcome variables: (a) time to death and (b) immediate cause of death in relation to specific demographic and clinical factors. Time from ED presentation to death was dichotomized as 'very early' mortality within ≤6 h and death >6-72 h and logistic regression was used to assess the adjusted impact of these demographic and clinical variables on the probability of dying within 6 h of ED presentation. RESULTS: Between October 2012 and May 2013, 9956 patients visited the ED and 220 patients died within 72 h of admission. After excluding patients dead on arrival (n = 34), the average age of death was 43.1 years and the overall mortality rate was 1.9 %. Head injury (21.5 %) and sepsis (18.8 %) were the most common causes of death. Relative to medical patients, trauma patients were more likely to be male (p < 0.01), less likely to have had prior recent ED visits (p < 0.01) and more likely to be triaged as higher acuity (p = 0.04). The sole statistically significant predictor of death within 6 h from our multivariable logistic regression model was symptom duration less than 4 h (4-48 h vs. <4 h: OR = 0.20, 95 % CI 0.07, 0.53, p < 0.01; >48 h vs. <4 h: OR = 0.27, 95 % CI 0.09, 0.81, p = 0.02). CONCLUSIONS: The mortality burden of trauma and sepsis in the TASH-ED is substantial, and mortality patterns differ between these groups. As emergency medicine develops as a specialty in the Ethiopian health system, the potential impact of context-specific clinical care protocol development, trauma prevention advocacy and ED care re-organization initiatives to reduce mortality among these young, previously well patients warrants exploration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".