Nomogram for predicting in-hospital mortality in trauma patients undergoing resuscitative endovascular balloon occlusion of the aorta: a retrospective multicenter study

Abstract

Recently, resuscitative endovascular balloon occlusion of the aorta (REBOA) had been introduced as an innovative procedure for severe hemorrhage in the abdomen or pelvis. We aimed to investigate risk factors associated with mortality after REBOA and construct a model for predicting mortality. This multicenter retrospective study collected data from 251 patients admitted at five regional trauma centers across South Korea from 2015 to 2022. The indications for REBOA included patients experiencing hypovolemic shock due to hemorrhage in the abdomen, pelvis, or lower extremities, and those who were non-responders (systolic blood pressure (SBP) < 90 mmHg) to initial fluid treatment. The primary and secondary outcomes were mortality due to exsanguination and overall mortality, respectively. After feature selection using the least absolute shrinkage and selection operator (LASSO) logistic regression model to minimize overfitting, a multivariate logistic regression (MLR) model and nomogram were constructed. In the MLR model using risk factors selected in the LASSO, five risk factors, including initial heart rate (adjusted odds ratio [aOR], 0.99; 95% confidence interval [CI], 0.98-1.00; p = 0.030), initial Glasgow coma scale (aOR, 0.86; 95% CI 0.80-0.93; p < 0.001), RBC transfusion within 4 h (unit, aOR, 1.12; 95% CI 1.07-1.17; p < 0.001), balloon occlusion type (reference: partial occlusion; total occlusion, aOR, 2.53; 95% CI 1.27-5.02; p = 0.008; partial + total occlusion, aOR, 2.04; 95% CI 0.71-5.86; p = 0.187), and post-REBOA systolic blood pressure (SBP) (aOR, 0.98; 95% CI 0.97-0.99; p < 0.001) were significantly associated with mortality due to exsanguination. The prediction model showed an area under curve, sensitivity, and specificity of 0.855, 73.2%, and 83.6%, respectively. Decision curve analysis showed that the predictive model had increased net benefits across a wide range of threshold probabilities. This study developed a novel intuitive nomogram for predicting mortality in patients undergoing REBOA. Our proposed model exhibited excellent performance and revealed that total occlusion was associated with poor outcomes, with post-REBOA SBP potentially being an effective surrogate measure.

Keywords: Hemorrhage; Injuries; Resuscitative endovascular balloon occlusion of aorta; Trauma.

Figure 1

Clinical variables were selected using the LASSO logistic regression model. (A) In terms of mortality due to exsanguination, shrinkage of coefficients by hyperparameter (λ). (B) In terms of mortality due to exsanguination, hyperparameter selection (λ) using cross-validation. (C) In terms of overall mortality, shrinkage of coefficients by hyperparameter (λ). (D) In terms of overall mortality, hyperparameter selection (λ) using cross-validation. The dotted line indicates the value of the harmonic log (λ) when the model error is minimized.

Figure 2

The nomogram predicts the risk of mortality due to exsanguination (A) and overall mortality (B). Each variable is assigned a score on each axis. The sum of all points for all variables is computed and denoted as the total points. The predicted probability can be obtained on the lowest row corresponding to the sum of total points.

Figure 3

Accuracy of a multivariable logistic regression model for predicting mortality. (A) Mortality due to exsanguination. (0.855 AUROC, 0.552 probability as threshold, 0.836 specificity, and 0.732 sensitivity, respectively) (B) Overall mortality (0.892 AUROC, 0.755 probability as threshold, 0.889 specificity, and 0.756 sensitivity, respectively). Optimal cutoff value was presented using Youden’s index. 95% confidence interval was plotted by green. Decision curve analysis of (C) mortality due to exsanguination and (D) overall mortality.