The role of coagulopathy and subdural hematoma thickness at admission in predicting the prognoses of patients with severe traumatic brain injury: a multicenter retrospective cohort study from China
Authors: Chen Li , Xia Shaohuai , Lin Yinghong , Chen Yuhui , Xian Liang , Yang Yang , Qiu Xianshen , Xu Limei , Xingshu Zhu , Chen Dujuan , Xia Xuewei , Zuo Yi , Wang Shousen 
Published: May 2024

Background:
Traumatic brain injury (TBI) is one of the diseases with high disability and mortality worldwide. Recent studies have shown that TBI-related factors may change the complex balance between bleeding and thrombosis, leading to coagulation disorders. The aim of this retrospective study was to investigate the prediction of coagulopathy and subdural hematoma thickness at admission using the Glasgow Outcome Scale (GOS) in patients with severe TBI at 6 months after discharge.

Methods:
In this retrospective cohort study, a total of 1006 patients with severe TBI in large medical centers in three different provinces of China from June 2015 to June 2021 were enrolled after the exclusion criteria, and 800 patients who met the enrollment criteria were included. A receiver operating characteristic (ROC) curve was used to determine the best cut-off values of platelet (PLT), international normalized ratio (INR), activated partial thromboplastin time (APTT), and subdural hematoma (SDH) thickness. The ROC curve, nomogram, calibration curve, and the decision curve were used to evaluate the predictive effect of the coagulopathy and Coagulopathy-SDH(X1) models on the prognoses of patients with severe TBI, and the importance of predictive indicators was ranked by machine learning.

Results:
Among the patients with severe TBI on admission, 576/800 (72%) had coagulopathy, 494/800 (61%) had SDH thickness ≥14.05 mm, and 385/800 (48%) had coagulopathy combined with SDH thickness ≥14.05 mm. Multivariate logistic regression analyses showed that age, pupil, brain herniation, WBC, CRP, SDH, coagulopathy, and X1 were independent prognostic factors for GOS after severe TBI. Compared with other single indicators, X1 as a predictor of the prognosis of severe TBI was more accurate. The GOS of patients with coagulopathy and thick SDH (X1, 1 point) at 6 months after discharge was significantly worse than that of patients with coagulopathy and thin SDH (X1, 2 points), patients without coagulopathy and thick SDH (X1, 3 point), and patients without coagulopathy and thin SDH (X1, 4 points). In the training group, the C-index based on the coagulopathy nomogram was 0.900. The C-index of the X1-based nomogram was 0.912. In the validation group, the C-index based on the coagulopathy nomogram was 0.858. The C-index of the X1-based nomogram was 0.877. Decision curve analysis also confirmed that the X1-based model had a higher clinical net benefit of GOS at 6 months after discharge than the coagulopathy-based model in most cases, both in the training and validation groups. In addition, compared with the calibration curve based on the coagulopathy model, the prediction of the X1 model-based calibration curve for the probability of GOS at 6 months after discharge showed better agreement with actual observations. Machine learning compared the importance of each independent influencing factor in the evaluation of GOS prediction after TBI, with results showing that the importance of X1 was better than that of coagulopathy alone.

Conclusion:
Coagulopathy combined with SDH thickness could be used as a new, accurate, and objective clinical predictor, and X1, based on combining coagulopathy with SDH thickness could be used to improve the accuracy of GOS prediction in patients with TBI, 6 months after discharge.