A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multicenter retrospective cohort study in the United States

Figures

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  Figure 1. Screening process of included patients and the constructed model. HF, heart failure; ICUs, intensive care units; MIMIC-III, Medical Information Mart for Intensive Care III; ICD, International Classification of Diseases.

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  Figure 2. Predictors selected by the LASSO method. (A) The coefficients of all predictors gradually return to zeros through 10-fold cross-validation. (B) Eleven predictors with non-zero coefficients at the rightmost dashed line. LASSO, least absolute shrinkage and selection operator.

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  Figure 3. Nomogram to estimate the risk of mortality in elderly with heart failure. The blue histogram in (A) represents the distribution of variables, and the red dots represent specific values. (B) An interface display of the web-based nomogram. We describe the value score for each corresponding parameter and the probability of mortality for a specific instance. The numbers “1” and “0” in renal failure and deficiency anemias mean “No” and “Yes”, respectively. Urine output, urine output in first 24 h. BUN, serum blood urea nitrogen; INR, international normalized ratio.

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  Figure 4. Calibration of nomogram in the training set and the testing set. (A) Calibration belt in the training set did not cross the diagonal bisector line. (B) Calibration belt in the testing set did not cross the diagonal bisector line. Those suggesting that the prediction models had a strong concordance performance. The calibration curve demonstrated superior agreement between the estimated and actual observations.

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  Figure 5. Clinical usefulness of nomogram in the training set and the testing set. (A) Decision curve analysis curve in the training set and the testing set. (B) Clinical impact curve in the training set. (C) Clinical impact curve in the testing set. CIC and DCA showed that the nomogram had a superior overall net benefit within the wide and practical ranges of threshold probabilities and impacted clinical outcomes. Train, train; test, test.

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  Figure 6. ROC plot of nomogram in the training set, testing set, and external validation set. The AUC showed favorable accuracy of the nomogram model. Train, training set; test, testing set; ROC, receiver operating characteristic curves; AUROC, area under the receiver operating characteristic curves.

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  Figure 7. Variable collinearity analysis and RCS plot. (A) The correlation coefficient between variables; blue represents a positive correlation, red represents a negative correlation, and the darker the color, the stronger the correlation. (B) RCS plot shows the size of the cutoff value of the variable, the intersection of the red solid line, and the dotted line is the cutoff value for 24 h UOP.

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  Figure 8. Smooth curve. (A) For the relationship between blood oxygen saturation and predicted probability of death; the larger the value of blood oxygen saturation, the lower the predicted probability of death. (B) For the relationship between 24h UOP and predicted probability of death; the larger the value of 24h UOP, the higher the predicted probability of death small. (C) The relationship between serum calcium levels and predicted probability of death. (D) The relationship between systolic blood pressure and predicted probability of death. Red-filled shading represents confidence intervals.