Development and validation of a nomogram for evaluating the incident risk of carotid atherosclerosis in patients with type 2 diabetes

Figures

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  Figure 1. Flowchart of the participants.

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  Figure 2. Clinical index selection using LASSO regression analysis. (A) Log(lambda) and partial likelihood deviations are shown, and the dotted line shown at the smallest log(lambda) represents the best predictor. (B) LASSO coefficients for the 32 clinical indicators. Non-zero coefficients were determined based on the best logarithm (lambda).

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  Figure 3. Nomogram: A Nomogram was created to determine the incidence of carotid atherosclerosis. A total score was obtained from the values of each index, and the CAD risk rate corresponding to the total score was the predicted rate of the Nomogram.

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  Figure 4. The nomogram model predicted the receiver operating characteristic curve of carotid atherosclerosis in patients with T2DM. (A) The area under the curve of the training cohort is 0.763. (B) The area under the curve of the validation cohort is 0.717.

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  Figure 5. Calibration curve of nomogram model for predicting carotid atherosclerosis in patients with T2DM. (A) In the training cohort, B = 1000 repetitions, boot mean absolute error = 0.021, n = 733. (B) In the validation cohort, B = 1000 repetitions, boot mean absolute error = 0.032, n = 316. The X-axis represents the predicted risk of carotid atherosclerosis; Y-axis represents the actual diagnosed carotid atherosclerosis. Y-axis represents actually diagnosed carotid atherosclerosis. The diagonal dotted line represents the perfect prediction by the ideal model. The solid line represents the performance of the nomogram; the closer to the diagonal dotted line, the better the prediction.

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  Figure 6. The nomogram model predicted the decision curve of carotid atherosclerosis in patients with T2DM. The y-axis measures the net return. The dotted line represents the CAD risk nomogram. The thin solid line represents the assumption that all patients are CAD. The thick solid line represents the assumption that no patients are CAD. (A) Decision curves for the training cohort show that using this nomogram predicts more benefit for CAD risk than intervening with the all-patient scenario or the no-intervention scenario if the threshold probabilities for patients and physicians are >2% and <76%, respectively. (B) Decision curves for the validation cohort show that using this nomogram to predict CAD risk adds more benefit than intervening with an all-patient regimen or a no-intervention regimen if the threshold probabilities for patients and physicians are >3% and <80%, respectively.