TRENDS IN ANTIMICROBIAL RESISTANCE IN STAPHYLOCOCCUS AUREUS ISOLATES AMONG HOSPITALIZED PATIENTS IN THE WEST BANK (2020–2025) AND THE IMPACT OF DIABETES MELLITUS ON INFECTION PROGRESSION AND MULTIDRUG RESISTANCE
Date
2026-05-12
Authors
Samer Mohammad
Duha Bani Odeh
Mohammad Alayan
Naif Suleiman
Sara Hjeiji
Qais Abu Alia
Sundus Shalabi
Journal Title
Journal ISSN
Volume Title
Publisher
Deanship of Scientific Research - Al-Quds University
Abstract
Background: Antimicrobial resistance (AMR) in Staphylococcus aureus represents a growing clinical and public health challenge, particularly in low-resource and fragmented healthcare settings such as the West Bank. Current local evidence is limited to short-term, single-center studies, which are insufficient to describe long-term resistance trends. Meanwhile, the increasing prevalence of diabetes mellitus, a known risk factor for impaired immunity and infection susceptibility, may contribute to both infection progression and the emergence of multidrug-resistant organisms. There is a critical need for longitudinal, multi-center data to inform antibiotic stewardship and clinical decision-making. Study Purpose/
Objectives: This study aims to evaluate temporal trends in antimicrobial resistance among Staphylococcus aureus isolates from hospitalized patients in the West Bank between 2020 and 2025. It also investigates whether diabetes mellitus is associated with an increased risk of progression from colonization to active infection and a higher likelihood of multidrug-resistant isolates.
Methods: A retrospective cohort design is employed using microbiology laboratory databases and patient medical records from multiple hospitals. Data collected include patient demographics, comorbidities (with emphasis on diabetes mellitus), site of isolate, antimicrobial susceptibility testing (AST) profiles, and clinical classification of colonization versus infection. Data are de-identified and subjected to standardized quality control procedures. Statistical analysis is performed using SPSS, including descriptive statistics, trend analysis, logistic regression, and risk estimation to identify predictors of multidrug resistance and infection progression.