Prediction Model of Delayed Hemothorax in Patients with Traumatic Occult Hemothorax Using a Novel Nomogram

Delayed hemothorax (dHTX) can occur unexpectedly, even in patients who initially present without signs of hemothorax (HTX), potentially leading to death. We aimed to develop a predictive model for dHTX requiring intervention, specifically targeting those with no or occult HTX.

This retrospective study was conducted at a level 1 trauma center. The primary outcome was the occurrence of dHTX requiring intervention in patients who had no HTX or occult HTX and did not undergo closed thoracostomy post-injury. To minimize overfitting, we employed the least absolute shrinkage and selection operator (LASSO) logistic regression model for feature selection. Thereafter, we developed a multivariable logistic regression (MLR) model and a nomogram.

In total, 688 patients were included in the study, with 64 cases of dHTX (9.3%). The LASSO and MLR analyses revealed that the depth of HTX (adjusted odds ratio [aOR], 3.79; 95% confidence interval [CI], 2.10-6.85; p<0.001) and the number of totally displaced rib fractures (RFX) (aOR, 1.90; 95% CI, 1.56-2.32; p<0.001) were significant predictors. Based on these parameters, we developed a nomogram to predict dHTX, with a sensitivity of 78.1%, a specificity of 76.0%, a positive predictive value of 25.0%, and a negative predictive value of 97.1% at the optimal cut-off value. The area under the receiver operating characteristic curve was 0.832.

The depth of HTX on initial chest computed tomography and the number of totally displaced RFX emerged as significant risk factors for dHTX. We propose a novel nomogram that is easily applicable in clinical settings.