Combined pathological, microbiological and virological evaluation of vitreous aspirates: a retrospective evaluation of 374 vitrectomy specimens with non-neoplastic disorders.

BACKGROUND

Intraocular infections pose substantial diagnostic challenges due to their varied aetiologies and complex inflammatory responses. The value of combining cytological, microbiological and virological assessments remains underexplored. This extensive retrospective study conducted at a German tertiary care university hospital aims to elucidate the correlation between different inflammatory patterns and detected pathogens in vitreous fluid samples.

METHODS

A retrospective analysis of 374 non-neoplastic vitreous samples from 353 patients was performed, integrating cytological, microbiological and virological data with final clinical diagnoses.

RESULTS

Cytological analysis showed 284 instances of lymphocytic uveitis and 76 of granulocytic endophthalmitis. Pathogens were identified in 46 out of 181 microbiologically tested samples; notable pathogens included Staphylococcus epidermidis, Staphylococcus aureus, Streptococcus pneumoniae, Tropheryma whipplei, Candida albicans and Toxoplasma gondii. Virological tests on 188 samples detected viral DNA in 42 cases, predominantly varicella zoster virus and Epstein-Barr virus, correlating well with clinical suspicions of retinitis. Interestingly, no pathogens were found in 67% of the lymphocytic uveitis cases, and multiple pathogens were detected simultaneously in seven instances, suggesting potential latent infections or reactivations. A significant pattern emerged correlating increased neutrophil counts with pathogen detection, highlighting a notable association (p = 0.03) in a subset analysed for both neutrophil levels and pathogen presence.

CONCLUSION

This study offers critical insights into the epidemiology of intraocular infections in Germany, underscoring the importance of comprehensive pathological assessments. It emphasizes the diagnostic value of the underlying inflammatory patterns for predicting pathogen presence and identifies notable cases of infections, including rare pathogens like Tropheryma whipplei.