Endogenous Fungal Endophthalmitis: Causative Organisms, Treatments, and Visual Outcomes.
Kuan-Jen ChenMing-Hui SunYen-Po ChenLinda Yi-Hsing ChenNan-Kai WangLaura LiuAn-Ning ChaoWei-Chi WuYih-Shiou HwangChi-Chun LaiPublished in: Journal of fungi (Basel, Switzerland) (2022)
Endogenous fungal endophthalmitis (EFE) is a vision-threatening intraocular infection and a rare complication of fungemia. Early diagnosis and prompt aggressive treatment are crucial to avoid vision loss. We retrospectively reviewed the data of 37 patients (49 eyes) with EFE who were treated at a tertiary referral hospital from January 2000 to April 2019. The most common risk factor was diabetes (24 patients; 65%), followed by recent hospitalization, urinary tract disease, liver disease, and immunosuppressive therapy. Two or more risk factors were detected in 24 patients (65%), and yeasts (29 patients; 78%) were more commonly detected than mold (8 patients; 22%). The most common fungal isolates were Candida spp. (78%), especially Candida albicans (70%). Moreover, 24 eyes in 21 patients underwent vitrectomy, and 2 eyes underwent evisceration. Retinal detachment (RD) occurred in 17 eyes (35%) in 14 patients, and eyes without RD exhibited significantly superior visual outcomes ( p = 0.001). A comparison of the initial VA between the better (20/200 or better) and worse groups (worse than 20/200) revealed that better initial VA was related to a superior visual outcome ( p = 0.003). Therefore, to achieve superior visual outcomes, early diagnosis and prompt treatment are necessary for patients with EFE.
Keyphrases
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- risk factors
- type diabetes
- candida albicans
- peritoneal dialysis
- prognostic factors
- healthcare
- cardiovascular disease
- primary care
- emergency department
- stem cells
- optical coherence tomography
- adipose tissue
- metabolic syndrome
- mesenchymal stem cells
- insulin resistance
- bone marrow
- escherichia coli
- saccharomyces cerevisiae
- urinary tract
- big data
- data analysis