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http://repository.iiitd.edu.in/xmlui/handle/123456789/2006Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tiju, Noel Abraham | - |
| dc.contributor.author | Sethi, Tavpritesh (Advisor) | - |
| dc.date.accessioned | 2026-08-21T14:01:41Z | - |
| dc.date.available | 2026-08-21T14:01:41Z | - |
| dc.date.issued | 2024-12-12 | - |
| dc.identifier.uri | http://repository.iiitd.edu.in/xmlui/handle/123456789/2006 | - |
| dc.description.abstract | Antimicrobial resistance (AMR) is one of the most significant global health threats of the 21st century, driven by the overuse and misuse of antibiotics and the lack of effective surveillance systems. This study leverages the Pfizer–ATLAS dataset to analyze resistance trends, focusing on identifying critical pathogens, exploring resistance patterns, and developing actionable tools to guide healthcare decision-making. The research emphasizes three core objectives. Firstly, it evaluates isolation rates to identify pathogens of concern, with a particular focus on ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aerugi nosa, Enterobacter species) and Escherichia coli, which are known for their resilience to multiple drug classes. Secondly, it examines resistance patterns across demographic and geographic variables, revealing significant trends, such as the rise in resistance to first-line antibiotics like Carbapenems and Fluoroquinolones in pathogens like Klebsiella pneumoniae and Acinetobacter baumannii. Lastly, it facilitates data visualization through AMR scorecards, offering healthcare professionals actionable insights to guide antibiotic stewardship. To ensure reliability, the study adopts a robust methodology for data preparation, statistical analysis, and trend exploration. Isolation rates revealed that specimens from blood, wounds, urine, and sputum accounted for approximately 75% of the dataset. Confidence interval plots for resistance percentages demonstrated excellent precision, particularly for antibiotics like Tige cycline and Linezolid, which maintain low resistance levels across multiple pathogens. Temporal narrowing of these plots underscores the improved accuracy of AMR surveillance over time. The findings are contextualized within existing literature, addressing limitations and challenges in AMR analysis. By integrating dynamic tools and data-driven approaches, this research con tributes valuable insights to combat AMR, highlighting the urgent need for enhanced surveillance and global collaboration to mitigate this growing health crisis. | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | IIIT-Delhi | en_US |
| dc.subject | Antimicrobial resistance | en_US |
| dc.subject | Pfizer–ATLAS dataset | en_US |
| dc.subject | isolation rates | en_US |
| dc.subject | AMR scorecards | en_US |
| dc.title | AMR sense | en_US |
| dc.type | Other | en_US |
| Appears in Collections: | Year-2024 | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| BTP_Report_2022338 - Noel Abraham Tiju.pdf Restricted Access | 12.25 MB | Adobe PDF | View/Open Request a copy |
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