The potential of a nomogram risk assessment model for the diagnosis of abdominal aortic aneurysm: a multicenter retrospective study

Figures

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  Fig. 1. LASSO regression analysis for analytical screening of predictor variables. (a) Tuning parameter (lambda) selection of deviance in the LASSO regression based on the minimum criteria (left dotted line) and the 1-SE criteria (right dotted line). (b) A coefficient profile plot was created against the log (lambda) sequence. In the present study, predictor’s selection was according to the 1-SE criteria (right dotted line), where 8 nonzero coefficients were selected. LASSO, least absolute shrinkage and selection operator; SE, standard error.

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  Fig. 2. Nomogram for predicting abdominal aortic aneurysm and its algorithm. First, a point was found for each variable of a patient on the uppermost rule; then all scores were added together and the total number of points were collected. Finally, the corresponding predicted probability of abdominal aortic aneurysm was found on the lowest rule.

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  Fig. 3. Evaluation of validity and reliability of the model. ROC curves of the nomogram prediction model in the training cohort (a), external validation cohort (b).

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  Fig. 4. Calibration curves: the horizontal coordinate is the probability of an event occurring as predicted by the model. The vertical coordinate is the proportion of events that actually occur within the predicted probability range. The calibration curve is used to assess the agreement between the predicted probability of an event and the actual probability of its occurrence. (a) Training cohort, (b) External validation cohort.

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  Fig. 5. DCA of the nomogram. The horizontal coordinate is the high risk threshold, referring to the thresholds selected for the different predictor variables. The vertical coordinate is the standard net benefit, which refers to the standard net benefit calculated for the different predictor variables constituting the model and the line strategy. The DCA plot of the two makes it possible to assess the extent to which each model outperforms or underperforms the baseline strategy under different decision scenarios. (a) Training cohort, (b) External validation cohort.

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  Fig. 6. CIC of the nomogram. The high-risk thresholds and cost–benefit ratios in the horizontal coordinate compare the costs of using the model with its benefits. The vertical coordinate represents the number of samples judged to be depressed at the selected high-risk threshold for a sample size of 1,000. The three form a net benefit curve that can be used to assess the benefits of each model across different predictor variables, helping policymakers to make optimal decisions. (a) Training cohort, (b) External validation cohort.