Construction and Validation of a Prediction Model for Sustained Smoking Cessation in Patients with Chronic Obstructive Pulmonary Disease

Figures

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  Figure 1. Nomogram for predicting sustained smoking cessation in COPD patients.

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  Figure 2. Calibration plot for the prediction model of sustained smoking cessation in COPD patients. In the calibration plot, the blue curve represents the apparent prediction (uncorrected curve) of the model. The purple curve represents the bias-corrected prediction (calibration curve). The black dashed line at a 45-degree angle represents the ideal prediction (ideal line), indicating perfect agreement between predicted and observed probabilities.

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  Figure 3. Clinical Decision Curve for the Prediction Model of Sustained Smoking Cessation in COPD Patients. The clinical decision curve is used to assess the clinical net benefit of the prediction model. The vertical axis represents the net benefit standardized, and the top horizontal axis represents the high-risk threshold. The bottom horizontal axis represents the cost-benefit ratio. The graph contains three curves: the purple line is the prediction model, indicating the net benefit if all patients underwent the prediction mo the gray line is the “intervention-all” line, indicating the net benefit if all patients received the intervention; the black line is the “intervention-none” line, indicating the net benefit if no patients received any intervention.

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  Figure 4. Clinical Impact Curve of the Sustained Smoking Cessation Prediction Model in COPD Patients. The CIC illustrates the relationship between the number of patients predicted to be at high risk and the number of patients who actually experienced the event (smoking cessation failure). The solid blue line represents the total number of patients predicted to be at high risk for smoking cessation failure across different high-risk thresholds. The dashed purple line represents the number of patients who actually experienced the event at the same high-risk thresholds. The x-axis represents the high-risk threshold, and the y-axis represents the number of high-risk patients (per 1000 patients). This graph is used to evaluate the clinical utility of the prediction model and its ability to identify high-risk populations.