Staphylococcus aureus bacteremia at a referral medical center in Kenya: A retrospective review of cases from 2010 to 2018
Bibliographic record
Abstract
BACKGROUND: Many studies have shown that Staphylococcus aureus is a leading cause of both community onset and hospital onset bloodstream infections. However, relatively little is known about the occurrence and outcome of S. aureus bacteremia in sub-Saharan Africa. A previous report indicated that S. aureus accounts for 16% of community onset and 6% of hospital onset bloodstream infections at Aga Khan University Hospital Nairobi (AKUHN). Data about the etiology of S. aureus bacteremia in sub-Saharan Africa will help optimize recognition and treatment. This study was performed in order to understand the etiologies and risk factors for S. aureus bacteremia in a sub-Saharan location. MATERIALS AND METHODS: A review of the electronic record of laboratory results from September 2010 through December 2018 identified 201 patients seen at AKUHN with S. aureus bacteremia. The source and/or focus of infection was identified and in-hospital mortality was determined. Cases with bacteremia after three days of hospitalization were considered hospital acquired. Community onset cases were divided into community acquired and health care associated. RESULTS: Most cases (71%; 143/201) were community onset, but only 41% (83/201) of these cases were community acquired. The most commonly identified foci of infection for community acquired bacteremia were musculoskeletal (25%; 21/83) and skin and soft tissue (24%; 20/83). The majority of health care associated (70%; 40/57) and hospital acquired cases (74%; 43/58) were associated with invasive vascular devices, with peripheral IVs being the most common for hospital acquired and dialysis catheters being the most common for health care associated infections. In-hospital mortality rates were 23% (19/83) for community acquired, 19% (11/57) for health care associated and 33% (19/58) for hospital acquired infections. CONCLUSION: Invasive vascular devices were associated with a substantial portion of cases of S. aureus bacteremia and provide an important target for infection control efforts.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".