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ORIGINAL RESEARCH

Nomogram for Prediction of Diabetic Retinopathy Among Type 2 Diabetes Population in Xinjiang, China

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Pages 1077-1089 | Published online: 07 Apr 2022

Figures & data

Figure 1 Flow diagram of the data screening.

Notes: diagnosis unrecognized: Comprehensive eye examinations without mydriasis; incomplete clinical data: Delete samples missing more than 2 variables; DR did not meet the requirements: retinopathy caused by hypertension; abnormal data: data that cannot be included in the evaluation due to severe cataracts and other reasons; 76 patients in incomplete clinical data and DR did not meet the requirements were repeated.
Figure 1 Flow diagram of the data screening.

Table 1 Clinical Characteristics of the Study Population (N=13,980)

Table 2 Baseline Characteristics Were Analyzed by Multivariate Logistic Regression in the Training Group. (N= 10,485)

Figure 2 Demographic and clinical feature selection using the LASSO binary logistic regression model (figure was created by R software, “glmnet” package, version 2.0–18, https://CRAN.R-project.org/package=glmnet).

Notes: (A) Optimal candidate (lambda) selection in the LASSO model used 5-fold cross validation via minimum criteria. The area under the receiver operation characteristic curve was plotted versus log (lambda). Dotted vertical lines were drawn at the optimal values by using the minimum criteria and the 1 SE of the minimum criteria; (B) LASSO coefficient pro-files of the 21 candidates. A coefficient profile plot was produced against the log (lambda) sequence. Vertical line was drawn at the value selected using 5-fold cross validation, where optimal lambda resulted in 8 candidates with nonzero coefficients.
Figure 2 Demographic and clinical feature selection using the LASSO binary logistic regression model (figure was created by R software, “glmnet” package, version 2.0–18, https://CRAN.R-project.org/package=glmnet).

Figure 3 Nomogram to predict the risk of DR.

Notes: To use the nomogram, an individual participants value is located on each variable axis, and a line is drawn upward to determine the number of points received for each variable value. The sum of these numbers is located on the total points axis to determine the risk of DR.
Figure 3 Nomogram to predict the risk of DR.

Figure 4 According to Nomogram’s estimate, the probability of DR risk in patient no.1020 (A) and no.2089 (B) was 0.12 and 0.816, respectively, P < 0.001.

Notes: Figure a and b are the line segment dynamic nomogram, which is different from the line segment static nomogram in the paper. After optimization corrected, each variable starts with 0 (nomogram). In the line segment dynamic nomogram, it is uncorrected and reflects the score corresponding to each variable in the actual situation. Therefore, the score corresponding to each variable starts from the specific value rather than 0. Although the scores of the two are different, the prediction probability is the same.
Figure 4 According to Nomogram’s estimate, the probability of DR risk in patient no.1020 (A) and no.2089 (B) was 0.12 and 0.816, respectively, P < 0.001.

Figure 5 The ROC curves of the nomogram for DR risk (left, development group, right, validation group. including ROC curves of single risk factor model).

Notes: In the development group, the AUCs were 0.882 (95% CI: 0.875–0.888); In the validation group, the AUCs were 0.870 (95% CI: 0.856–0.881), respectively. ROC, receiver operating characteristics curve, AUC, area under curve.
Figure 5 The ROC curves of the nomogram for DR risk (left, development group, right, validation group. including ROC curves of single risk factor model).

Figure 6 Calibration curves for the validation and development group models (left, development group, right, validation group).

Notes: The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the nomogram, of which a closer fit to the diagonal dotted line represents a better prediction.
Figure 6 Calibration curves for the validation and development group models (left, development group, right, validation group).

Figure 7 The decision curve analysis of the nomogram for DR risk (left, development group, right, validation group).

Notes: The red line represents the net benefit when no participant was considered to exhibit DR, while the blue line represents the net benefit when all participants were considered to suffer from DR. The area among the model curve, “treat none line” (red line) and “treat all line” (blue line), represents the clinical usefulness of the model. The farther the model curve is to the blue and red lines, the better clinical value the nomogram holds.
Figure 7 The decision curve analysis of the nomogram for DR risk (left, development group, right, validation group).