Malaria-Associated Acute Kidney Injury in African Children: Prevalence, Pathophysiology, Impact, and Management Challenges
Bibliographic record
Abstract
Acute kidney injury (AKI) is emerging as a complication of increasing clinical importance associated with substantial morbidity and mortality in African children with severe malaria. Using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria to define AKI, an estimated 24-59% of African children with severe malaria have AKI with most AKI community-acquired. AKI is a risk factor for mortality in pediatric severe malaria with a stepwise increase in mortality across AKI stages. AKI is also a risk factor for post-discharge mortality and is associated with increased long-term risk of neurocognitive impairment and behavioral problems in survivors. Following injury, the kidney undergoes a process of recovery and repair. AKI is an established risk factor for chronic kidney disease and hypertension in survivors and is associated with an increased risk of chronic kidney disease in severe malaria survivors. The magnitude of the risk and contribution of malaria-associated AKI to chronic kidney disease in malaria-endemic areas remains undetermined. Pathways associated with AKI pathogenesis in the context of pediatric severe malaria are not well understood, but there is emerging evidence that immune activation, endothelial dysfunction, and hemolysis-mediated oxidative stress all directly contribute to kidney injury. In this review, we outline the KDIGO bundle of care and highlight how this could be applied in the context of severe malaria to improve kidney perfusion, reduce AKI progression, and improve survival. With increased recognition that AKI in severe malaria is associated with substantial post-discharge morbidity and long-term risk of chronic kidney disease, there is a need to increase AKI recognition through enhanced access to creatinine-based and next-generation biomarker diagnostics. Long-term studies to assess severe malaria-associated AKI's impact on long-term health in malaria-endemic areas are urgently needed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".