Prolonged empirical antibiotic therapy is correlated with bloodstream infections and increased mortality in a tertiary care hospital in Ethiopia: bacteriology testing matters
Bibliographic record
Abstract
Abstract Background Hospital-associated infection (HAI) and antimicrobial resistance (AMR) are major health threats in low- and middle-income countries (LMICs). Because diagnostic capacity is lacking throughout most of Africa, patients are commonly managed with prolonged empirical antibiotic therapy. Our goal was to assess mortality in relation to HAI and empirical therapy in Ethiopia’s largest referral hospital. Methods Cohort study of patients with suspected HAI at Tikur Anbessa Specialized Hospital from October 2016 to October 2018. Blood culture testing was performed on an automated platform. Primary outcomes were proportion of patients with bloodstream infection (BSI), antibiotic resistance patterns and 14 day mortality. We also assessed days of therapy (DOT) pre- and post-blood culture testing. Results Of 978 enrolled patients, 777 had blood culture testing; 237 (30%) had a BSI. Enterobacteriaceae were isolated in 49%; 81% of these were cephalosporin resistant and 23% were also carbapenem resistant. Mortality at 14 days was 31% and 21% in those with and without BSI, respectively. Ceftriaxone resistance was strongly correlated with mortality. Patients with BSI had longer DOT pre-blood culture testing compared with those without BSI (median DOT 12 versus 3 days, respectively, P < 0.0001). After testing, DOT were comparable between the two groups (20 versus 18 days, respectively). Conclusions BSI are frequent and fatal among patients with suspected HAI in Ethiopia. Highly resistant blood isolates are alarmingly common. This study provides evidence that investing in systematic blood culture testing in LMICs identifies patients at highest risk of death and that empirical management is frequently inappropriate. Major investments in laboratory development are critical to achieve better outcomes.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".