Community-acquired Pneumonia in People With HIV During the Current Era of Effective Antiretroviral Therapy: A Multicenter Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: It is unclear if human immunodeficiency virus (HIV) affects the prognosis for community-acquired pneumonia (CAP) in the current era of effective antiretroviral therapy. In this multicenter retrospective cohort study of patients admitted for CAP, we compared the in-hospital mortality rate between people with HIV (PWH) and those without. METHODS: The study included consecutive patients admitted with a diagnosis of CAP across 31 hospitals in Ontario, Canada, from 2015 to 2022. HIV infection was based on discharge diagnoses and antiretroviral prescription. The primary outcome was in-hospital mortality. Competing risk models were used to describe time to death in hospital or discharge. Potential confounders were balanced using overlap weighting of propensity scores. RESULTS: Of 82 822 patients admitted with CAP, 1518 (1.8%) had a diagnosis of HIV. PWH were more likely to be younger, male, and have fewer comorbidities. In the hospital, 67 (4.4%) PWH and 6873 (8.5%) people without HIV died. HIV status had an adjusted subdistribution hazard ratio of 1.02 (95% confidence interval, .80-1.31; P = .8440) for dying in the hospital. Of 1518 PWH, 440 (29.0%) patients had a diagnosis of AIDS. AIDS diagnosis had an adjusted subdistribution hazard ratio of 3.04 (95% CI, 1.69-5.45; P = .0002) for dying in the hospital compared to HIV without AIDS. CONCLUSIONS: People with and without HIV admitted for CAP had a similar in-hospital mortality rate. For PWH, AIDS significantly increased the mortality risk. HIV infection by itself without AIDS should not be considered a poor prognostic factor for CAP.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".