Weight loss and mortality in people living with HIV: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background In the first reported cases of human immunodeficiency virus (HIV) infection, people living with HIV (PLHIV) suffered weight loss, which was an independent predictor of mortality. Highly active antiretroviral therapy (HAART) has changed this scenario for ideal weight, overweight, and even obesity. However, some PLHIV, even on HAART, continue to lose weight. Thus, the guiding question of the study was: do PLHIV hospitalized using HAART with weight loss have higher mortality than hospitalized PLHIV using HAART without weight loss? Method A systematic review and meta-analysis of prospective cohort studies published in English, Spanish, or Portuguese, searched in the MedLine, Embase, and LILACS databases from March 2020, until October 2023, reported by MOOSE. We analyzed the methodological quality and risk of bias using the Joanna Briggs Institute Critical Appraisal Tool for Cohort Studies; used the risk ratio (RR) to calculate the probability of hospitalized PLWH who lost weight dying, applied the random effect model and created the funnel plot. We used the inverse variance test estimated by the Mantel-Haenszel method, considering a 95% confidence interval (CI), heterogeneity (I 2 ), total effect size (Z), and significance value of p < 0.05. We performed a sensitivity analysis with meta-regression and meta-analyses on subgroups to diagnose influence and outliers. The quality of evidence and strength of recommendation were analyzed using the Grading of Recommendations Assessment, Development, and Evaluation system (GRADE). Results We included 10 of the 711 studies identified, totaling 1,637 PLHIV. The studies were from South Africa (1), Canada (1), China (1), Brazil (1), Cameroon (1), Ethiopia (1), Thailand (1), Colombia (1), and Tanzania (2), from 1996 to 2017. The average age of the participants was 33.1 years old, and the male was predominant. The leading causes of hospital admission were related to co-infections, and the average hospitalization time was 20.5 days. The prevalence of death in hospitalized PLHIV using HAART who lost weight was 57.5%, with a 1.5 higher risk of dying (RR: 1.50, 95% CI: 1.03, 2.19, p = 0.04) than hospitalized PLHIV who did not lose weight. Conclusion We concluded, with a very low confidence level, that that weight loss significantly increased the risk of death in hospitalized PLWH using HAART. Trial Registration and funding PROSPERO International Prospective Register of Systematic Reviews CRD42020191246 https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020191246 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".