Longitudinal evaluation of risk factors and outcomes of blood stream infections due to Staphylococcus species in persons with HIV: An observational cohort study
Bibliographic record
Abstract
Background Staphylococcal blood stream infections (SBSI) are a significant cause of morbidity and mortality, however there is little data on such infections in persons with HIV (PWH) in the combination antiretroviral therapy era, particularly when divided by species; methicillin-sensitive (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) and coagulase-negative Staphylococcus (CoNS). Methods Using linked longitudinal clinical and microbiologic databases, all cases of SBSI in PWH accessing care at Southern Alberta Clinic were identified and demographic features and outcomes characterized. We compared participants with SBSI to those with no SBSI and determined the 1-year all-cause mortality following SBSI and longitudinally over the study period. Findings From 2000 to 2018, 130 SBSI occurred in 95 PWH over 21,526 patient-years follow-up. MSSA caused 38.4%, MRSA 26.1% and CoNS 35.3% of SBSI. Highest risks for SSBI were in Hepatitis C coinfection, low CD4 nadir, Indigenous/Metis ethnicity and in persons who use injection drugs (PWID). During follow-up, 423 deaths occurred in all PWH. Mortality rates for PWH with SBSI was 74.9/1000 patient-years (95% CI 59.2–94.9) compared with no SBSI 16.0/1000 patient-years (95% CI 14.4–17.7). The mortality Hazard Ratio was 2.61(95% CI 1.95–3.49, P = <0.001) for SBSI compared to no SBSI, following adjusting for confounding. Seventy deaths occurred in persons with SBSI with 40% in the first year. Higher 1-year mortality rates occurred in hospital-acquired infections. Interpretation Incidence rates of SBSI are high in PWH, with identified characteristics that further increase this risk. PWH who experience SBSI have a significant mortality risk within the first year of follow-up, however they also have greater long-term all-cause mortality compared to those with no SBSI. Further investigation is needed in PWH evaluating host, environment and pathogen differences that lead to differing rates of SBSI and mortality seen here. Funding No funding was received for this work.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".