A Diagnostic Model for Screening Diabetic Retinopathy Using the Hand-Held Electroretinogram Device RETeval

Abstract

PurposeTo construct a proper model to screen for diabetic retinopathy (DR) with the RETeval.MethodThis was a cross-sectional study. Two hundred thirty-two diabetic patients and seventy controls were recruited. The DR risk assessment protocol was performed to obtain subjects’ DR risk score using the RETeval. Afterwards, the receiver operating characteristic (ROC) curve was used to determine the best cutoff for diagnosing DR. Random forest and decision tree models were constructed.ResultsWith increasing DR severity, the DR score gradually increased. When the DR score was used to diagnose DR, the ROC curve had an area under the curve of 0.881 (95% confidence interval: 0.836-0.927, P < 0.001), with a best cutoff value of 22.95, a sensitivity of 74.3% (95 CI: 66.0%~82.6%), and a specificity of 90.6% (95 CI: 83.7% ~94.8%). The top four risk factors selected by the random forest were used to construct the decision tree for diagnosing DR, which had a sensitivity of 93.3% (95% CI: 86.3%~97.0%) and a specificity of 80.3% (95% CI: 72.1% ~86.6%).ConclusionsThe DR risk assessment protocol combined with the decision tree model was innovatively used to evaluate the risk of DR, improving the sensitivity of diagnosis, which makes this method more suitable than the current protocol for DR screening.

Electrophysiological findings in long-term type 1 diabetes patients without diabetic retinopathy using different ERG recording systems

“…The RETeval DR risk assessment protocol has proven to be a good tool for DR screening, with an increase in the score with the progression of DR. However, it can be less useful at detecting early stages of the disease 53 .…”

A Classification Tree Model with Optical Coherence Tomography Angiography Variables to Screen Early-Stage Diabetic Retinopathy in Diabetic Patients

“…In recent years, researchers have made efforts to update DR screening strategies besides using fundus photography. Deng [ 32 ] integrated a quantitative device called a handheld electroretinogram with the classification tree model and found good accuracy. Moreover, handheld smartphone-based retinal cameras were applied in urban primary healthcare settings and the accuracy maintained above 80% [ 33 , 34 ].…”

Advances in cell therapies using stem cells/progenitors as a novel approach for neurovascular repair of the diabetic retina

“…Due to technical advancements, currently available clinical diagnostic technologies including OCT, OCT-A (which allows evaluation of superficial and deep retinal capillary plexuses) [39], ultra-widefield imaging [40], rapid electrophysiology testing (e.g. with systems such as the RETeval) [172], macular microperimentry, and fundus-mapped visual field testing [173] allow more appropriate phenotyping of patients. However, imaging of the choroid and choriocapillaris remains challenging [174].…”