Multicentric Surveillance of Antimicrobial Resistance to Generate Data-Driven Regional Antibiograms: A Laboratory-Based Cross-Sectional Study in Pakistan
Bibliographic record
Abstract
Background: The escalating burden of antimicrobial resistance (AMR) poses a critical threat to public health in Pakistan, with rates of high antibiotic consumption and limited standardized surveillance on AMR rates. Our study aimed to carry out a multicentric surveillance of AMR to generate regional antibiograms for Northern Punjab, Pakistan, to guide empirical antimicrobial therapy and stewardship efforts. Methods: A laboratory-based, retrospective cross-sectional study was conducted over a six-month period across three tertiary care hospitals. Socio-demographic, clinical, and microbiological data (including specimen type and antibiotic prescription rates) were collected from N = 485 patients with confirmed bacterial infections. Antimicrobial susceptibility testing was performed based on Clinical Laboratory Standards Institute (CLSI) recommendations. Statistical analyses were carried out using SPSS v.22.0. Results: In our study setting, Gram-positive bacteria were common causes (60.0%) of infections, with Staphylococcus aureus (12.2%) and Streptococcus pneumoniae (10.3%) being the most relevant. Among Gram-negative bacteria (40.0%), Escherichia coli (14.0%) and Pseudomonas aeruginosa (5.8%) were shown to be important pathogens. Overall, 25.0% of S. aureus isolates were methicillin-resistant (MRSA), while ~30% of E. coli showed resistance to third-generation cephalosporins (3GCs). Enterobacterales species had highly variable susceptibility rates (40–70%) for fluoroquinolones. Meropenem and vancomycin/linezolid retained high efficacy (>90%) against most Gram-negative and Gram-positive isolates, respectively. In all healthcare settings studied, ceftriaxone was the most frequently prescribed antibiotic. Conclusions: High levels of resistance against first-line antibiotics were noted in our setting of Northern Punjab, Pakistan, underscoring the critical need for robust antimicrobial stewardship programs, tailored to local institutional contexts, capabilities, and needs. The regional antibiogram developed based on our data may provide vital evidence for informing local empirical treatment guidelines, which need to be continuously updated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".