Hospital-Acquired Acute Kidney Injury in Non-critical Medical Patients in a Developing Country Tertiary Hospital: Incidence and Predictors
Bibliographic record
Abstract
ABSTRACT Background Acute kidney injury (AKI) is a frequent complication in critical patients leading to worse prognosis. Although the consequences of AKI are worse among critical patients, AKI is also associated with less favorable outcomes in non-critical patients. Hence, understanding the magnitude of the problem in these patients is crucial, yet there is a scarcity of evidence in non-critical settings, especially in resource limited countries. Hence, the study aimed at determining the incidence and predictors of hospital acquired acute kidney injury (HAAKI) in non-critical medical patients who were admitted at a large tertiary hospital in Ethiopia. Methods A retrospective chart review study was conducted among 232 hospitalized non-critical medical patients admitted to St. Paul’s Hospital Millennium Medical College between January 2020 and January 2022. Data was characterized using frequency and median with interquartile range. To identify predictors of HAAKI, a log binomial regression model was fitted at a p value of ≤ 0.05. The magnitude of association was measured using adjusted relative risk (ARR) with its 95% CI. Results During the median follow-up duration of 11 days (IQR, 6-19 days), the incidence of HAAKI was estimated to be 6.0 per 100 person-day observation (95% CI= 5.5 to 7.2). Significant predictors of HAAKI were found to be having type 2 diabetes mellitus (ARR=2.36, 95% CI= 1.03, 5.39, p-value=0.042), and taking vancomycin (ARR=3.04, 95% CI= 1.38, 6.72, p-value=0.006) and proton pump inhibitors (ARR=3.80, 95% CI = 1.34,10.82, p-value=0.012). Conclusions HAAKI is a common complication in hospitalized non-critical medical patients, and is associated with a common medical condition and commonly prescribed medications. Therefore, it is important to remain vigilant in the prevention and timely identification of these cases and to establish a system of rational prescribing habits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".