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. 2025 Nov 21;20(1):104. doi: 10.5334/gh.1493

Figure 7.

Prediction model for cardiovascular disease risk using retinal traits and biomarkers. (A) LASSO coefficient profiles of all candidate features. (B) Ten-fold cross-validation for optimal parameter (lambda) selection in the LASSO model. (C) Nomogram developed from the selected predictors, incorporating retinal layer thickness and circulating biomarkers. (D) Receiver operating characteristic (ROC) curve for the nomogram, showing discrimination performance (AUC = 0.878). (E) Decision curve analysis (DCA) evaluating the net clinical benefit across threshold probabilities. (F) Calibration plot comparing predicted vs. observed risk, demonstrating good model calibration. Overall, the LASSO-based nomogram shows strong discrimination, clinical utility, and calibration, supporting the predictive value of combining retinal imaging features with systemic biomarkers in assessing cardiovascular disease risk

Prediction model for cardiovascular disease risk using retinal traits and biomarkers. (A) LASSO coefficient profiles of all candidate features. (B) Ten-fold cross-validation for optimal parameter (lambda) selection in the LASSO model. (C) Nomogram developed from the selected predictors, incorporating retinal layer thickness and circulating biomarkers. (D) Receiver operating characteristic (ROC) curve for the nomogram, showing discrimination performance (AUC = 0.878). (E) Decision curve analysis (DCA) evaluating the net clinical benefit across threshold probabilities. (F) Calibration plot comparing predicted vs. observed risk, demonstrating good model calibration. Overall, the LASSO-based nomogram shows strong discrimination, clinical utility, and calibration, supporting the predictive value of combining retinal imaging features with systemic biomarkers in assessing cardiovascular disease risk.