Predictors of acute kidney injury in dengue patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Dengue infection poses a significant global health challenge, particularly in tropical and subtropical regions. Among its severe complications, Acute kidney injury (AKI) stands out due to its association with increased morbidity, mortality, and healthcare burdens. This Meta-analysis aim to identify and evaluate the predictors of AKI among dengue patients, facilitating early detection and management strategies to mitigate AKI's impact. METHODS: We searched PubMed, EMBASE, and Web of Science databases, covering literature up to February 2024. We included human observational studies reporting on AKI predictors in confirmed dengue cases. Nested-Knowledge software was used for screening and data extraction. The Newcastle-Ottawa Scale was used for quality assessment. R software (V 4.3) was utilized to compute pooled odds ratios (ORs) and 95% confidence intervals (CIs) for each predictor. RESULTS: Our search yielded nine studies involving diverse geographic locations and patient demographics. A total of 9,198 patients were included in the studies, with 542 diagnosed with AKI. in which key predictors of AKI identified include severe forms of dengue (OR: 2.22, 95% CI: 1.02-3.42), male gender (OR: 3.13, 95% CI: 1.82-4.44), comorbidities such as diabetes mellitus (OR: 3.298, 95% CI: 0.274-6.322), and chronic kidney disease (OR: 2.2, 95% CI: 0.42-11.24), as well as co-infections and clinical manifestations like rhabdomyolysis and major bleeding. CONCLUSION: Our study identifies several predictors of AKI in dengue patients. These findings indicate the importance of early identification and intervention for high-risk individuals. Future research should focus on standardizing AKI diagnostic criteria within the dengue context and exploring the mechanisms underlying these associations to improve patient care and outcomes.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".