Mediaboss Marketing

In emergency situations, the collection of precise clinical information from trauma patients can be challenging, with accurate data often being elusive. Accurately assessing the risk of progression in traumatic intracranial hemorrhage (ICH) is essential, particularly for patients who are relatively stable or exhibit minimal traumatic brain hemorrhage, compared to those immediately identified for emergency surgical intervention among the TBI cohort13,14. Additionally, accurately discerning details regarding the mechanism of head injury frequently proves difficult15.

In this study, the objective is to create a predictive model for the short-term prognosis of patients with traumatic brain injury. This model emphasizes the use of clear and readily accessible information from the emergency department setting. Specifically, it relies on data from initial head CT scans and findings from physical examinations, both of which are readily available and easily obtained in the emergency room.

Previous literature has explored the analysis of various traumatic ICH types16. While lvarez-Sabn et al. have reported on the phenomenon of delayed traumatic ICH17, studies that demonstrate a variance in the frequency of ICH progression according to the type of ICH are lacking. Additionally, systematic clinical analyses on the influence of each ICH type on patient prognosis remain unexplored. The ICH type characterized in this study as petechial hemorrhage has also been referred to as blossomed or exhibiting a salt and pepper appearance in prior research16,18. Pathologically, this phenotype signifies a severe manifestation of traumatic subarachnoid hemorrhage that extends into the brain parenchyma, arising from progressive microvascular rupture and consequent bleeding. We hypothesized that PH type would have the worst prognosis due to these pathological differences, and this was confirmed by the XGboost model's feature importance analysis.

The clinical significance of counter coup head injury, characterized by brain injury occurring on the side opposite to the point of impact, has been suggested as a potential indicator of the severity of head trauma19. This perspective is based on the understanding that counter coup injuries are frequently associated with a higher risk of complications, including brain swelling and bleeding, compared to injuries that occur solely at the site of impact, known as coup injuries9.

In this study, we observed that the incidence of counter coup ICH was 17.9% in patients with occipital fractures, a rate higher than in patients with skull fractures at other locations (3.7% in frontal fractures, 7.2% in temporal fractures, and 3.7% in parietal fractures). This led to a notably increased frequency of ICH in the frontal lobe among patients whose initial impact was on the occipital skull. This observed trend may be linked to brain contusions that occur on the irregular surfaces of the anterior cranial fossa of the skull and structures like the anterior clinoid process. This could account for the prevalent association of counter coup ICH in the frontal lobe with TBIs involving occipital skull impacts9.

In our study, we successfully developed an algorithm capable of predicting an individual's prognosis using CT findings and clinical information. By integrating both clinical and radiological factors, such as counter coup injury and the specific type of ICH, we achieved high accuracy in predicting ICH progression among patients with mild to moderate traumatic brain injury (TBI).

The proposed XGBoost model demonstrated an average accuracy of 91% in predicting ICH progression, surpassing the logistic regression model, which achieved an AUC of 0.82. This enhanced performance emphasizes the efficacy of the XGBoost model in predicting ICH progression, highlighting the benefits of applying advanced machine learning techniques over traditional statistical methods for clinical predictions. Furthermore, our analysis validated the significant utility of SHAP values derived from the XGBoost model in assessing individual ICH progression risks. The incorporation of SHAP values enhances the visualization of individual risk factors, offering clinicians a crucial tool for interpreting the effects of various predictors on ICH progression at a personalized level. This capability facilitates more precise and tailored clinical decision-making.

To the best of our knowledge, this study represents the first attempt to develop a machine-learning model specifically for predicting ICH progression using image data from CT scans. We anticipate that our findings will contribute to the early identification of patients at risk for ICH progression, thereby informing treatment decisions and monitoring strategies. This approach has the potential to mitigate the risk of complications and enhance overall outcomes in patients with traumatic brain injury (TBI).

The current study is subject to several limitations. Firstly, due to the limited number of patients in each age group, we were unable to analyze the risk of ICH progression across different age demographics. Secondly, we did not account for the potential impact of variables such as current medication use and underlying health conditions on ICH progression in TBI patients. Due to the challenges in obtaining a complete medical history from patients presenting to the emergency room with traumatic brain injury, our study focused primarily on factors that can be quickly and readily obtained in the ER, particularly radiological factors, to investigate their association with ICH progression. Although we investigated the history of antiplatelet and anticoagulation medication use, only a small proportion of patients (27 out of 650, or 4.2%) were confirmed to have used these medications. This limited number of patients was insufficient to establish a statistical correlation with ICH progression. This likely reflects the unreliability of initial medical history investigations and suggests that patients who were on antiplatelet or anticoagulation therapy might have presented with more severe ICH, thus potentially excluding them from this study due to their immediate need for surgical intervention.

Thirdly, our machine learning model was developed using data from a single institution, highlighting the need for future studies to perform general validation of the models with external datasets.

In forthcoming research, we aim to enhance the accuracy of our algorithm in predicting the progression of TBI. To improve the predictability of our current machine learning algorithm, it will be crucial to gather more comprehensive individual information from patient medical records. Furthermore, future research should investigate the factors influencing the necessity of surgery among patients exhibiting ICH progression, particularly focusing on changes in the Glasgow Coma Scale (GCS) following follow-up and the subsequent need for surgical intervention. Such analysis is anticipated to hold substantial clinical significance.

Follow this link:
Machine learning models for predicting early hemorrhage progression in traumatic brain injury | Scientific Reports - Nature.com