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Prediction model for asymptomatic carotid atherosclerosis using retinal microvascular intelligent analysis: A
Yuqing Han1, Lihua Zhang2, Zhenjie Yu1
1Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin 300384, China.
Importance:
Early detection and timely diagnosis of asymptomatic carotid atherosclerosis significantly assist in the prevention of ischemic stroke for them.
Objective:
This observational study aimed to develop and validate a novel prediction model to assist in the early diagnosis of carotid atherosclerosis based on new characteristic variables screened by retinal microvascular intelligence analysis.
Main Outcome(S) And Method (S):
The least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation were screened for characteristic variables, and nomograms were plotted to demonstrate the prediction model. Receiver operating characteristic (ROC) curves and area under the curve (AUC), calibration plots and brier score (BS), and decision curve analysis (DCA) were used to evaluate the risk model's discrimination, calibration, and clinical applicability.
Results:
Age, gender, diabetes mellitus (DM), drinking history, vascular branching angle, mean vascular diameter within 0.5-1.0 papillary diameter (PD), curvature tortuosity arteriole in the inferior region of the optic disc, and vascular density in the nasal region of the optic disc were identified as characteristic variables for carotid atherosclerosis with retinal microvascular intelligence analysis. The predictive nomogram model presented good discrimination with AUCs of 0.790 (0.774-0.806), and the calibration curve displayed high consistency between predicted and actual probability. The DCA demonstrated that this nomogram model led to net benefits in a threshold probability range of 20 %-94 % and could be adapted for clinical decision-making. The results of the 100-bootstrap resampling strategy for internal validation also show that the risk model is well discriminated with an AUC of 0.789 and excellent calibration. External validation showed good discrimination with AUCs of 0.703 (0.627 - 0.779) and good calibration, the risk threshold is 10 %-92 % in terms of DCA.
Conclusions And Relevance:
The novel prediction model based on retinal microvascular intelligence analysis constructed in this study could be effective prognoses for predicting the risk of asymptomatic carotid atherosclerosis in a Chinese screening population.
