P-1805. Identifying Hospital Antimicrobial Usage Trends Using Wastewater Based Surveillance
Bibliographic record
Abstract
Abstract Background Hospitals represent ideal locations for developing wastewater (WW) surveillance for antibiotics (Abx), owing to the high frequency of Abx use and robust record-keeping. To investigate this technology as a potentially useful stewardship tool, we compared the concentration of several Abx in WW from tertiary care hospitals to their corresponding levels in the surrounding municipality. Concentration of 4 common antimicrobials found in hospital wastewater effluent over a 4-month period Concentrations of 4 antimicrobials measured in WW from 3 tertiary care hospitals and the corresponding wastewater treatment plant (WWTP) in the same city. Displayed statistics represent the results of a Wilcoxon test, antimicrobials labelled “*” are commonly administered intravenously. Methods WW was collected bi-weekly from three tertiary care hospitals (two adult and one pediatric, with 600, 650 and 135 inpatient beds, respectively) and the associated municipal WW treatment plant (serving a population of ∼1,000,000) between February and May of 2024. Aliquots of WW were filtered and run directly on a liquid-chromatography paired triple quadrupole mass spectrometer (LC-QQQ) to quantify specific Abx (azithromycin, doxycycline, ciprofloxacin, levofloxacin, metronidazole, cefazolin, ceftriaxone, piperacillin, tazobactam, meropenem, vancomycin, and sulfamethoxazole). Spiked and replicate samples were randomly included to validate analyte recovery and reproducibility. Agilent MassHunter software (Version 10.1, 2019) was utilized to process and export raw data to R. Box plots and Wilcoxon tests were utilized to compare the concentration of each Abx. Results Validation experiments confirmed that filtering WW samples and directly running them on LC-QQQ yields reproducible and reliable results. Serial monitoring revealed that WW from hospitals generally exhibited a broader range of Abx concentrations than was observed city-wide (Figure 1). This variability was particularly evident among the most used Abx, consistent with the changing treatment needs of highly dynamic hospital populations. In addition to this, ceftriaxone, an IV-administered antimicrobial was found at significantly higher concentrations in all hospital sites when compared to the municipal WW treatment plant. Conclusion Validating Abx monitoring in WW from a range of scales will enable this approach to be applied across diverse environments as a tool to mitigate Abx resistance. This approach will be strengthened as it is integrated with clinical metadata and metagenomic assessment of antimicrobial resistance genes from the same samples. Disclosures All Authors: No reported disclosures
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".