Clinical Characteristics and Outcomes of Chronic Kidney Disease in People Living with HIV in a Resource-Limited Center of Central China
Bibliographic record
Abstract
Clinical management and optimal treatment are essential to improving outcomes for people living with HIV (PLWH). We assessed trends and outcomes of chronic kidney disease (CKD) in PLWH in a resource-limited center of central China. All PLWH who were followed up in a tertiary referral center in Wuhan, China, from July 2016 to June 2021 were evaluated. CKD was defined as glomerular filtration rate (GFR) <60 mL/min/1.73 m2 during two consecutive measurements 3 months apart. Baseline characteristics of the participants were extracted from the hospital medical records. The prevalence rate and associated risk factors of CKD were analyzed. A total of 863 PLWH with normal kidney function at baseline were analyzed. The median age was 33 (interquartile ranges: 26–49) years, and 778 (90.2%) were male and 85 (9.8%) were female. Among them, 50 (5.8%) had their GFR falling below 60 mL/min/1.73 m2 after a median of 54 months. Adjusted multivariate logistic regression revealed older age [adjusted odds ratio (aOR) = 1.04, 95% confidence interval (95% CI): 1.01–1.07], female sex (aOR = 3.17, 95% CI: 1.14–8.84), lower body weight (aOR = 0.95, 95% CI: 0.91–1.00), lower hemoglobin (aOR = 3.54, 95% CI: 1.51–8.30), longer duration of antiretroviral therapy exposure (aOR = 1.02, 95% CI: 1.00–1.04), and a baseline GFR between 60 and 90 mL/min/1.73 m2 (aOR = 3.89, 95% CI: 1.21–12.46) were associated with the development of CKD. Our findings showed that CKD is not infrequent in PLWH with a combination of traditional and HIV-specific risk factors for kidney disease, highlighting the suboptimal monitoring and treatment options of CKD in PLWH in resource-limited settings. Scalable monitoring strategy to improve care for this population is warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".