A nomogram for predicting atrial fibrillation detected after acute ischemic stroke

Figures

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  Figure 1. The flow chart of developing and validating the nomogram.

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  Figure 2. Predictor selection using the least absolute shrinkage and selection operator (LASSO) logistic regression model. (A) Identification of the optimal penalization coefficient lambda (λ) in the LASSO model. The dotted vertical line was plotted at the value selected using 10-fold cross-validation, for which the optimal λ resulted in 10 non-zero coefficients. (B) LASSO coefficient profiles of the 45 predictors. A coefficient profile plot was produced against the log (λ) sequence.

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  Figure 3. Forest plot of risk predictors in AFDAS with AIS.

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  Figure 4. The nomograms to predict the probability of AFDAS after AIS patients from the training cohort. The patient was 60 years old with a history of stroke and no history of heart failure. During hospitalization the NIHSS score was 18, the C-reactive protein was 30.4 mg/L and the BNP was 84.3 pg/mL. The patient had a total score of 222 and a probability of atrial fibrillation of 25.9% (model 1) (A). When the neuroimaging variable (Cortical infarction) was added, the patient had a total score of 262 and a 38.4% probability of atrial fibrillation (model 2) (B).

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  Figure 5. Calibration curves of the prediction models in each cohort. Calibration curves depict the calibration of prediction models in terms of the agreement between the predicted risks of AFDAS and observed outcomes of AFDAS. The x-axis represents the predicted AFDAS risk and the y-axis represents the actual AFDAS rate. The diagonal dotted line represents a perfect prediction by an ideal model. The red solid line represents the performance of our prediction models. A closer fit to the diagonal dotted line represents a better prediction. (A,B) represents the calibration curve of Model 1 in the training cohort and validation cohort; (C,D) represents the calibration curve of Model 2 in the training cohort and validation cohort.

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  Figure 6. Receiver operating characteristic (ROC) curve of the prediction models in each cohort. The blue line and red line represent Model 1 and Model 2, respectively. (A) Represents ROC curve of our prediction models in the training cohort; (B) Represents the ROC curve of models in the validation cohort.

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  Figure 7. (A,D) Decision curve analysis for the Model 1 and Model 2 nomogram in the training and validation cohort. The y and x axis means the true and false positive rate of the risk prediction of AFDAS patients, respectively. The blue line represents the Model 1 nomogram. The red line represents the Model 2 nomogram. (B,C) Clinical impact curve in the training cohort; (E,F) Clinical impact curve in the validation cohort.

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  Figure 8. Model 1 and Model 2 comparison based on NRI in training cohort (A) and validation cohort (B). Model 2 is 0.105 better than Model 1 in training cohort. Model 2 is 0.086 better than Model 1 in validation cohort.

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  Figure 9. Kaplan-Meier curves showing the cumulative mortality of all the patients of AFDAS (A), and the comparison of cumulative mortality in patients with and without AFDAS (B).