Prevalence and correlates of chronic kidney disease (CKD) among ART-naive HIV patients in the Niger-Delta region of Nigeria
Bibliographic record
Abstract
Widespread use of antiretroviral therapy (ART) in human immunodeficiency virus (HIV) patients has led to improved longevity with the attendant increase in noncommunicable disease prevalence including chronic kidney disease (CKD). This study documents the prevalence of CKD in a large HIV population in Southern Nigeria.This is a single center, 15-year analysis in ART-naïve patients. CKD was defined as the occurrence of estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m on 2 consecutive occasions 3 to 12 months apart using the chronic kidney disease epidemiology collaboration (CKD-EPI) equation. The Cochran-Armitage and Cuzick tests were employed to assess for trend across the years for CKD prevalence and CD4 count, respectively. Multivariable logistic regression models were used to identify independent associations with CKD.In all, 1317 patients (62.2% females) with mean age of 34.5 years and median CD4 count of 194 cells/μL were included. CKD prevalence was 13.4% (95%CI 11.6%-15.4%) using the CKD-EPI equation (without the race factor). Multivariable analysis identified increasing age and CD4 count <200 cells/μL as being independently associated with CKD occurrence.This study reports a high prevalence of CKD in ART-naïve HIV-infected patients. Measures to improve diagnosis of kidney disease and ensure early initiation of treatment should be integrated in HIV treatment programmes in this setting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".