Acute kidney injury and its predictors among HIV-positive patients in Africa: Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: cute kidney injury(AKI) is a rapid loss of the kidney's excretory function, resulting in an accumulation of end products of nitrogen metabolism. The causes of AKI in HIV-positive patients are not well investigated, but it may be associated with antiretroviral drug side effects and HIV itself. Even though there were studies that reported the prevalence of AKI among HIV-positive patients in Africa, their findings were inconsistent across the studies. METHODS: We searched on PubMed, Embas, Ebsco, OVID, Cochrane Library, and other supplementary search engines, including Google and Google Scholar. Articles published upto July 2023 were included in this review study. The quality of the study was assessed using the Newcastle-Ottawa Scale for cross-sectional, case-control, and cohort studies. The data were extracted using a Microsoft Excel spreadsheet and exported to Stata version 14 for analysis. A random effect meta-analysis model was used to estimate the pooled prevalence of AKI among HIV-positive patients. Heterogeneity was evaluated using Cochrane Q statistics and I squared (I2). Furthermore, the graphic asymmetric test of the funnel plot and/or Egger's tests were computed to detect publication bias. Sensitivity analysis was computed to see the effect of a single study on the summary effects. To treat the publication bias, a trim and fill analysis was carried out. The protocol of this review has been registered in an international database, the Prospective Register of Systematic Reviews (PROSPERO),with reference number CRD42023446078. RESULTS: A total of twenty-four original articles comprising 7913HIV-positive patients were included in the study. The pooled prevalence of AKI among HI-positive patients was found to be 23.35% (95% CI: 18.14-28.56%, I2 = 97.7%, p-value <0.001). Low hemoglobin (Hgb <8mg/dl) was found to be the determinant factor for AKI among HIV-positive patients (AOR = 2.4; 95% CI:1.69-3.4, I2 = 0.0%, p-value = 0.40). In meta-regression analysis, sample size was the possible source of variation among the included studies (AOR = 3.11, 95%CI: 2.399-3.83). CONCLUSIONS: The pooled prevalence of AKI among HIV-positive patients was high. HIV-positive patients with low hemoglobin levels are at risk of developing AKI. Hence, regular monitoring of kidney function tests is needed to prevent or delay the risk of AKI among HIV-positive patients. Healthcare workers should provide an integrated healthcare service to HIV-positive patients on the prevention, treatment, and reduction of the progression of AKI to advanced stages and complications.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".