Development and validation of a prognostic nomogram for Takotsubo syndrome patients in the intensive care units: a retrospective cohort study

Figures

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  Figure 1. Texture feature selection using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. (a) Each curve in the figure represents the change trajectory of each independent variable coefficient. The ordinate is the value of the coefficient, the lower abscissa is log(λ), and the upper abscissa is the number of non-zero coefficients in the model at this time. (b) tenfold cross-cross validation fitting and then selecting the model, and at the same time have a more accurate estimate of the performance of the model. For each λ value, around the mean value of the target parameter shown by the red dot, we can obtain a confidence interval for the target parameter. The two dashed lines indicate two special λ values:c (cvfit$lambda.min, cvfit$lambda.1se). The mean squared error was plotted vs. log(λ).

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  Figure 2. The nomogram for predicting the risk of in-hospital mortality in takotsubo syndrome patients. The top row of the ‘Points’ represents a scale for each risk factors, and points of each predictor were acquired by drawing a straight line upwards from the corresponding value to the “Points” line. Then, the points received from each predictor are summed, and the number is located on the “Total Points” axis. To conclude the patient’s sort of probability for in-hospital mortality, draw a straight line down to the corresponding “Risk of death” axis. resp_rate: respiratory rate. For example, a 74-years-old male without myocardial infarction history, his clinical data of admission was as followed: PT: 30 s, anion gap: 24 mEq/L, WBC: 10*10^9, hematocrit: 35% and SOFA score were 4 points. The corresponding score of each predictor were 6.8 points, 2.5 points, 1.0 points, 6.2 points, 1.3 points, 6.7 points, and 0.8 points respectively. Then his total score was about 24.5 points, and the risk of in-hospital mortality is 45.5%.

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  Figure 3. (A) Receiver operating characteristic (ROC) curve of the nomogram in the training set. The area under the curve (AUC) of ROC was (AUC: 0.779, 95% CI: 0.732–0.826). (B) Receiver operating characteristic (ROC) curve of the nomogram in the test set. The area under the curve (AUC) of ROC was (AUC: 0.775, 95% CI: 0.711–0.839). (C) Calibration curve of the nomogram in the training set. The x-axis represents the predicted probability of in-hospital mortality of TTS patients. The y-axis represents the actual in-hospital mortality of TTS patients. The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the nomogram, of which a closer fit to the diagonal dotted line represents a better prediction. The figure shows that the prediction model have a good predictive ability. (D) Calibration curve of the nomogram in the test set. The x-axis represents the predicted probability of in-hospital mortality of TTS patients. The y-axis represents the actual in-hospital mortality of TTS patients. The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the nomogram, of which…