A Revised Approach for the Detection of Sight-Threatening Diabetic Macular Edema

IMPORTANCE Diabetic macular edema is one of the leading causes of vision loss among working-age adults in the United States. Telemedicine screening programs and epidemiological studies rely on monoscopic fundus photography for the detection of clinically significant macular edema (CSME). Improving the accuracy of detecting CSME from monoscopic images could be valuable while recognizing the limitations of such detection in an era of optical coherence tomography detection of diabetic macular edema. OBJECTIVE To evaluate the screening test accuracy of radially arranged sectors affected by hard exudates in the detection of CSME. DESIGN, SETTING, AND PARTICIPANTS This investigation was a cross-sectional study of CSME grading in monoscopic images using a sectors approach. The Early Treatment Diabetic Retinopathy Study criteria were used to confirm the presence of CSME by the following 2 methods: stereoscopic fundus photography (method 1) and dilated biomicroscopy in combination with optical coherence tomography (method 2). Participants were recruited at a university-based practice between June 14, 2014, and December 28, 2015. MAIN OUTCOMES AND MEASURES Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS A total of 207 eyes from an ethnically/racially diverse group of 207 patients (mean [SD] age, 53.6 [10.8] years; 58.9%[122 of 207] female) were included in the analysis. Twelve eyes (5.8%) were diagnosed as having CSME based on method 1. The intermethod and intergrader agreement for CSME diagnosis and sector count was substantial (. range, 0.66 [95% CI, 0.47-0.85] to 0.75 [95% CI, 0.53-0.97]; P < .001 for all). Area under the receiver operating characteristic curve was 93.2%(95% CI, 84.2%-100%) when evaluating a sectors approach against method 1 as a reference test and offered up to an 8.6%(95% CI, 3.0%-14.3%) increase in specificity compared with the existing methods of detection. The positive predictive value was 33.3%(95% CI, 25.6%-45.5%), and the negative predictive value was 98.1% (95% CI, 96.9%-100%). The results were similar when comparing a sectors approach with method 2 as a reference test. CONCLUSIONS AND RELEVANCE A sectors approach shows good screening test characteristics for the detection of CSME. Its implementation in the existing telemedicine programs would require minimal resources. This approach will have the greatest effect in a setting where implementation of optical coherence tomography, a more objective and sensitive way to detect retinal thickening, is not feasible. The proposed method also may be easily incorporated in the automated diabetic retinopathy detection algorithms.