Back

A Study Analysing The Distribution And Determinants Of Diabetic Macular Edema In A Tertiary Care Center

M S, P.; B, C.; L, S.; Chellakumar, V.; M, B.; Solasa, D.

2024-12-05 ophthalmology
10.1101/2024.11.23.24317493 medRxiv
Show abstract

AIMThis study aims to examine the correlation between the specific type of diabetic macular edema (DME) identified using Optical Coherence Tomography (OCT) and various factors, including patient age, gender, diabetic profile (fasting blood sugars, postprandial blood sugars, and HbA1c), duration of Type 2 Diabetes Mellitus, and central macular thickness on OCT. OBJECTIVEThe study intends to investigate the relationships between the age of patients and DME type, gender prevalence in DME, duration of Type 2 Diabetes Mellitus and DME type, diabetic profiles and DME type, central macular thickness and DME type, and severity of diabetic retinopathy and DME type. IntroductionDiabetic maculopathy is a major cause of vision impairment in diabetic retinopathy. This study explores the relationship between DME types as determined by OCT and factors such as age, gender, diabetic profile, and diabetes duration. Materials and MethodsConducted over one year at a tertiary health care center, the study evaluated 95 patients with diabetic maculopathy through comprehensive clinical assessments including OCT classification of DME types. ResultsThe findings indicated a higher prevalence of DME among males, predominantly in the 61- 70 age group. A significant association was found between diabetes duration and mixed-type DME. However, no significant correlations were observed between glycemic control measures (FBS, PPBS, HbA1c) and DME types. ConclusionThe study underscores the importance of demographic and clinical factors in understanding variations in DME types, highlighting the need for tailored management strategies to reduce vision loss risk in diabetic patients.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.