Point-of-care Ultrasound to Assess Hemodynamic Contributors to Acute Kidney Injury in Pediatric Patients With Cerebral Malaria: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury is common in severe malaria and is independently associated with mortality. The pathogenesis of acute kidney injury (AKI) in severe malaria remains incompletely understood. Ultrasound-based tools such as point-of-care ultrasound (POCUS), ultrasound cardiac output monitors (USCOMs) and renal arterial resistive index (RRI) can be used to detect hemodynamic and renal blood flow abnormalities contributing to AKI in malaria. METHODS: We conducted a prospective study of Malawian children with cerebral malaria to determine the feasibility of using POCUS and USCOM to characterize hemodynamic contributors to severe AKI (Kidney Disease: Improving Global Outcomes stage 2 or 3). The primary outcome was feasibility (completion rate of study procedures). We also assessed for differences in POCUS and hemodynamic variables for patients with or without severe AKI. RESULTS: We enrolled 27 patients who had admission cardiac and renal ultrasounds and USCOM. Completion rates were high for cardiac (96%), renal (100%) and USCOM studies (96%). Severe AKI occurred in 13 of 27 patients (48%). No patients had ventricular dysfunction. Only 1 patient in the severe AKI group was determined to be hypovolemic ( P = 0.64). No significant differences in USCOM, RRI or venous congestion parameters were detected among patients with and without severe AKI. Mortality was 11% (3/27) with the 3 deaths occurring in the severe AKI group ( P = 0.056). CONCLUSIONS: Ultrasound-based cardiac, hemodynamic and renal blood flow measurements appear to be feasible in pediatric patients with cerebral malaria. We were unable to detect hemodynamic or renal blood flow abnormalities contributing to severe AKI in cerebral malaria. Larger studies are needed to corroborate these findings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".