Effect of Acute Kidney Injury on In-hospital Mortality in Non-critical Medical Patients in a Sub-Saharan African Country
Bibliographic record
Abstract
ABSTRACT Background AKI is a major global public health problem that affects millions of people each year and has been linked to poor prognosis in critically ill patients. As being a common complication in hospitalized patients, understanding its effect on non-critical patients is equally crucial, but there is a paucity of knowledge in this area, particularly in Africa. Therefore, the aim of this study was to assess the effect of AKI on in-hospital morality in non-critical medical patients admitted to a large tertiary hospital in Ethiopia. Methods A retrospective cohort study of 319 non-critical medical patients (113 with AKI and 206 without AKI) admitted between July 2019 and January 2022 was conducted. The in-hospital mortality rate was estimated using incidence density with a 95% CI. The two groups’ comparability was assessed using chi-square and Fisher’s exact tests. The effect of AKI on in-hospital mortality was analyzed using a log binomial regression model with a p-value of ≤ 0.05 determining a significant effect, and the effect was measured using adjusted relative risk (ARR) and its 95% CI. Results The in-hospital mortality rate was 6.8 per 1000 person-days of observation (95% CI=4.9-9.4). AKI did not show a significant effect on in-hospital mortality (ARR = 0.72, 95% CI=0.30-1.71, p=0.450). On the other hand, sepsis was found to be a significant predictor, with over a threefold increase in risk of in-hospital mortality (ARR=3.47, 95% CI=1.60-7.52, p=0.002). Conclusion With early detection and proper management, non-critical patients with AKI can have a similar prognosis as those without AKI, unlike the critical setting. However, sepsis was found to be a significant predictor of in-hospital mortality implying the need to pay special attention to the management of these cases.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".