Efficacy of Timolol Maleate (0.5%) in Preventing Rise of Intraocular Pressure (IOP) Post Neodymium: Yttrium Aluminum Garnet (Nd: YAG) Capsulotomy

Objective: To determine the efficacy of 0.5% Timolol eye drops in preventing rise of intraocular pressure following Neodymium: Yttrium-Aluminum-Garnet laser posterior capsulotomy. Study Design: Quasi-experimental study. Place and Duration of Study: Outpatient Department of Ophthalmology, Combined Military Hospital Multan Pakistan, from May to Nov 2019. Methodology: Patients presenting with posterior capsule opacification, undergoing elective Neodymium: Yttrium Aluminum Garnet (Nd: Yag) Capsulotomy were enrolled in the study. Patients were alternatively assigned to two groups; group-1 was administered 0.5% Timolol Maleate eye drops, while group-2 was administered placebo natural team eye drops. Intraocular pressure for all the patients was measured at three-time points a). First measurement was baseline intraocular pressure at presentation/before the treatment. Second measurement was 1-hour post laser capsulotomy and third measurement was 3 hours post laser capsulotomy. Results: At 1-hour follow up, the mean intraocular pressure was significantly raised in the patients belonging to control group as compared to treatment group (21.45 ± 8.33 mm Hg vs 15.33 ± 3.37 mm Hg, p<0.001). At 3-hours follow-up the mean intraocular pressure decreased a bit for control group but still was significantly higher than the treatment group (17.40 ± 3.00 mm Hg vs 15.60 ± 2.30 mm Hg, p<0.001). Conclusion: Prophylactic use of Timolol successfully reduces the post-procedural acute rise in intraocular pressure due to Neodymium: Yttrium Aluminum Garnet laser posterior capsulotomy.

UNILATERAL PROPTOSIS - A DIAGNOSTIC DILEMMA

Objective: To study epidemiology, presentation and management of cases with unilateral proptosis presenting at a tertiary care hospital. Study Design: Case series. Place and Duration of Study: Armed Forces Institute of Ophthalmology, Rawalpindi Pakistan, from Jan 2016 to Dec 2017. Methodology: Retrospective analysis of hospital record of patients with unilateral proptosis was carried out. Data was entered and analyzed in SPSS version 22. Chi square test was used for statistical analysis. Results: Thirty-three cases of unilateral proptosis with a mean age of 41.79 ± 3.87 years were included in the study. Pain was the most common association. Proptosis was mild in 42.4%, moderate in 48.5% and severe in 9.1% cases. In 61% cases, diagnosis was made on excisional or incisional biopsy and on the basis of radio imaging in 39% cases. Most common cause of proptosis found was pleomorphic adenoma (12%). Patients with axial and non-axial proptosis were 48.5% and 51.5% respectively. Fourteen cases (42.5%) were managed surgically and 2 cases (6%) were observed. Total of 22 cases (67%) improved after treatment, 8 cases (24%) went into remission, 2 (6%) deteriorated, and 1 (3%) died in the study period. Conclusion: Unilateral proptosis is a diagnostic challenge. Pertinent history taking and detail examination is the key, but associated signs or symptoms not always point towards correct diagnosis. Tissue biopsy provides a conclusive diagnosis especially where radio imaging is doubtful.

Arteriovenous ratio and papilledema based hybrid decision support system for detection and grading of hypertensive retinopathy

BACKGROUND AND OBJECTIVES

Hypertensive Retinopathy (HR) is a retinal disease which happened due to consistent high blood pressure (hypertension). In this paper, an automated system is presented that detects the HR at various stages using arteriovenous ratio and papilledema signs through fundus retinal images.

METHODS

The proposed system consists of two modules i.e. vascular analysis for calculation of arteriovenous ratio and optic nerve head (ONH) region analysis for papilledema.  First module uses a set of hybrid features in Artery or Vein (A/V) classification using support vector machine (SVM) along with its radial basis function (RBF) kernel for arteriovenous ratio. In second module, proposed system performs analysis of ONH region for possible signs of papilledema. This stage utilizes different features along with SVM and RBF for classification of papilledema.

RESULTS

The first module of proposed method shows average accuracies of 95.10%, 95.64% and 98.09%for images of INSPIRE-AVR, VICAVR, and local dataset respectively. The second module of proposed method achieves average accuracies of 95.93% and 97.50% on STARE and local dataset respectively.

CONCLUSIONS

The system finally utilizes results from both modules to grade HR with good results. The presented system is a novel step towards automated detection and grading of HR disease and can be used as clinical decision support system.