Bacterial etiology of bloodstream infections and antimicrobial resistance in Dhaka, Bangladesh, 2005–2014
Bibliographic record
Abstract
Bloodstream infections due to bacterial pathogens are a major cause of morbidity and mortality in Bangladesh and other developing countries. In these countries, most patients are treated empirically based on their clinical symptoms. Therefore, up to date etiological data for major pathogens causing bloodstream infections may play a positive role in better healthcare management. The aim of this study was to identify the bacterial pathogens causing major bloodstream infections in Dhaka, Bangladesh and determine their antibiotic susceptibility pattern. From January 2005 to December 2014, a total of 103,679 single bottle blood samples were collected from both hospitalized and domiciliary patients attending Dhaka hospital, icddrb, Bangladesh All the blood samples were processed for culture using a BACT/Alert blood culture machine. Further identification of bacterial pathogens and their antimicrobial susceptibility test were performed using standard microbiological procedures. Overall, 13.6% of the cultured blood samples were positive and Gram-negative (72.1%) bacteria were predominant throughout the study period. Salmonella Typhi was the most frequently isolated organism (36.9% of samples) in this study and a high percentage of those strains were multidrug-resistant (MDR). However, a decreasing trend in the S . Typhi isolation rate was observed and, noticeably, the percentage of MDR S. Typhi isolated declined sharply over the study period. An overall increase in the presence of Gram-positive bacteria was observed, but most significantly we observed the percentage of MDR Gram-positive bacteria to double over the study period. Overall, Gram positive bacteria were more resistant to most of the commonly used antibiotics than Gram-negative bacteria, but the MDR level was high in both groups. This study identified the major bacterial pathogens involved with BSI in Dhaka, Bangladesh and also revealed their antibiotic susceptibility patterns. We expect our findings to help healthcare professionals to make informed decisions and provide better care for their patients. Also, we hope this study will assist researchers and policy makers to prioritize their research options to face the future challenges of infectious diseases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".