English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Aim. To establish a classification tree model in DR screening and to compare the DR screening accuracy between the classification tree model and the logistic regression model in type 2 diabetes mellitus (T2DM) patients based on OCTA variables. Methods. Two hundred forty-one eyes of 241 T2DM patients were included and divided into two groups: the development cohort and the validation cohort. Optical coherence tomography angiography (OCTA) images were acquired in these patients. The data of foveal avascular zone area, superficial capillary plexus (SCP) density, and deep capillary plexus (DCP) density were exported after automatically analyzing the macular 6 × 6 mm OCTA images, while the data of radial peripapillary capillary plexus (RPCP) density was exported after automatically analyzing the optic nerve head 4.5 × 4.5 mm OCTA images. These OCTA variables were adopted to establish and validate the logistic regression model and the classification tree model. The area under the curve (AUC), sensitivity, specificity, and statistical power for receiver operating characteristic curves of two models were calculated. Results. In the logistic regression model, best-corrected visual acuity (BCVA) (LogMAR) and SCP density were entered (BVCA : OR= 60.30, 95% CI= [2.40, 1513.82],  p  = 0.013; SCP density: OR= 0.86, 95% CI= [0.78, 0.96],  p  = 0.006). The AUC, sensitivity, and specificity for detecting early-stage DR (mild to moderate NPDR) in the development cohort were 0.75 (95% CI: [0.66, 0.85]), 63%, and 83%, respectively. The AUC, sensitivity, and specificity in the validation cohort were 0.75 (95% CI: [0.66, 0.84]), 79%, and 72%, respectively. In the classification tree model, BVCA (LogMAR), DM duration, SCP density, and DCP density were entered. The AUC, sensitivity, and specificity for detecting early-stage DR were 0.72 (95% CI: [0.60, 0.84]), 66%, and 76%, respectively. The AUC, sensitivity, and specificity in the validation cohort were 0.74 (95% CI: [0.65, 0.83]), 74%, and 72%, respectively. The statistical power of the development and validation cohorts in two models was all more than 99%. Conclusions. Compared to the logistic regression model, the classification tree model has similar accuracy in predicting early-stage DR. The classification tree model with OCTA variables may be a simple tool for clinical practitioners to identify early-stage DR in T2DM patients. Moreover, SCP density is significantly reduced in mild-to-moderate NPDR eyes and might be a biomarker in early-stage DR detection. Further improvement and validation of the DR diagnostic model are awaiting to be performed.

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