Novel nomogram for predicting pulmonary complications in patients with blunt chest trauma with rib fractures: a retrospective cohort study

Abstract

The direct consequences of chest trauma may cause adverse outcomes. Therefore, the early detection of high-risk patients and appropriate interventions can improve patient outcomes. This study aimed to investigate the risk factor for overall pulmonary complications in patients with blunt traumatic rib fractures. Prospectively recorded data of patients with blunt chest trauma in a level 1 trauma center between January 2019 and October 2022 were retrospectively analyzed. The primary outcomes were one or more pulmonary complications. To minimize the overfitting of the prediction model, we used the least absolute shrinkage and selection operator (LASSO) logistic regression. We input selected features using LASSO regression into the multivariable logistic regression model (MLR). We also constructed a nomogram to calculate approximate individual probability. Altogether, 542 patients were included. The LASSO regression model identified age, injury severity score (ISS), and flail motion of the chest wall as significant risk factors. In the MLR analysis, age (adjusted OR [aOR] 1.06; 95% confidence interval [CI] 1.03-1.08; p < 0.001), ISS (aOR 1.10; 95% CI 1.05-1.16; p < 0.001), and flail motion (aOR 8.82; 95% CI 4.13-18.83; p < 0.001) were significant. An MLR-based nomogram predicted the individual risk, and the area under the receiver operating characteristic curve was 0.826. We suggest a novel nomogram with good performance for predicting adverse pulmonary outcomes. The flail motion of the chest wall may be the most significant risk factor for pulmonary complications.

Figure 1

Flowchart of patient selection process.

Figure 2

Clinical variables were selected using the least absolute shrinkage and selection operator (LASSO) logistic regression model. (A) Shrinkage of coefficients by hyperparameter (λ). (B) Hyperparameter selection (λ) using cross-validation. The dotted line indicates the value of the harmonic log (λ) when the error of the model is minimized. In the LASSO logistic regression model, three variables (age, ISS, and flail motion) were selected when log (λ) was − 3.2302.

Figure 3

Nomogram based on multivariable logistic regression model for predicting overall pulmonary complications. A point was identified for each risk factor for overall pulmonary complications. After adding all the scores, predicted probability can be identified on the lowest rule corresponding to calculated total point.

Figure 4

Accuracy of the multivariable logistic regression model for predicting overall pulmonary complications compared to conventional prediction models including Chest Trauma Score, Rib Fracture Score, Injury Severity Score, and Thorax Trauma Severity Score. Our proposed model yielded the highest area under the receiver operating characteristic curve.