Microbial Characteristics and Etiologic Patterns of Endophthalmitis at a Tertiary Referral Center: A Retrospective Cohort Study
DOI:
https://doi.org/10.17161/kjm.vol19.24297Keywords:
Endophthalmitis, antibiotic resistance, intravitreal injection, microbial spectrumAbstract
Introduction: Endophthalmitis is a serious vision-threatening intraocular infection. Its etiology, microbiologic profile, and antimicrobial resistance patterns vary by region. Understanding local patterns is essential for guiding empiric therapy and improving patient outcomes. Authors of this study aimed to identify the most common etiologies, causative organisms, and resistance patterns of endophthalmitis at a Midwestern tertiary referral center.
Methods: Authors conducted a retrospective chart review of patients diagnosed and treated for infectious endophthalmitis at the University of Kansas between 2008 and 2022. Adult patients with infectious endophthalmitis who underwent vitreous biopsy with microbiologic testing were included. Clinical findings, microbiology results, antimicrobial resistance patterns, empiric treatment regimens, and patient characteristics were collected and analyzed.
Results: A total of 149 patients met the inclusion criteria, of whom 52 had positive microbiologic cultures, yielding 64 microbial isolates. The most common etiologies were endogenous infection (n = 43), postoperative infection (n = 32), infection secondary to corneal ulcer (n = 19), and trauma (n = 14). Among culture-positive cases, most isolates were bacterial (61/64). Staphylococcus epidermidis and Staphylococcus aureus were the most frequently isolated organisms, accounting for 12 and 8 isolates, respectively. Resistance was most commonly observed to erythromycin (58%) and clindamycin (33%), whereas vancomycin resistance was rare (3%).
Conclusions: Endogenous infection was the most common cause of endophthalmitis in this Midwestern cohort. Regional variation in causative organisms and antimicrobial resistance patterns highlights the importance of local surveillance and tailored empiric treatment strategies to optimize patient outcomes.
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Copyright (c) 2026 Shamir Khan, M.D., Robert Boyle, M.D., Maram El-Geneidy, M.D., Amin Karadaghy, M.D., Nathaniel Cameron, M.D., Maggie Malmberg, M.D., Vivek Velagapudi, M.D., Dante Pennipede, M.D., Radwan S. Ajlan, MBBCh

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
All articles in the Kansas Journal of Medicine are licensed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND 4.0).
