Incidence and Predictors of Mortality and the Effect of Tuberculosis Immune Reconstitution Inflammatory Syndrome in a Cohort of TB/HIV Patients Commencing Antiretroviral Therapy
Bibliographic record
Abstract
BACKGROUND: Tuberculosis-HIV (TB-HIV) coinfection remains an important cause of mortality in antiretroviral therapy (ART) programs. In a cohort of TB-HIV-coinfected patients starting ART, we examined the incidence and predictors of early mortality. METHODS: Consecutive TB-HIV-coinfected patients eligible for ART were enrolled in a cohort study at the Mulago National Tuberculosis and Leprosy Program clinic in Kampala, Uganda. Predictors of mortality were assessed using Cox proportional hazards analysis. RESULTS: Three hundred and two patients [median CD4 count 53 cells/μL (interquartile range, 20-134)] were enrolled. Fifty-three patients died, 36 (68%) of these died within the first 6 months of TB diagnosis. Male sex [hazard (HR): 2.19; 95% confidence interval (CI): 1.19 to 4.03; P = 0.011], anergy to tuberculin skin test [HR: 2.59 (1.10 to 6.12); P = 0.030], a positive serum cryptococcal antigen result at enrollment (HR: 4.27; 95% CI: 1.50 to 12.13; P = 0.006) and no ART use (HR: 4.63; 95% CI: 2. 37 to 9.03; P < 0.001) were independent predictors of mortality by multivariate analysis. Six (10%) patients with TB immune reconstitution inflammatory syndrome died, and in most, an alternative contributing cause of death was identified. CONCLUSIONS: Mortality among these TB-HIV-coinfected patients was high particularly when presenting with advanced HIV disease and not starting ART, reinforcing the need for timely and joint treatment for both infections. Screening for a concomitant cryptococcal infection and antifungal treatment for patients with cryptococcal antigenemia may further improve clinical outcome.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".