Analysis of changes in the volume of edema around brain contusions and the influencing factors: A single-center, retrospective, observational study

Association Between Monocyte-to-Lymphocyte Ratio and Hematoma Progression After Cerebral Contusion

BACKGROUND

The objective of this research was to examine the impact of the monocyte-to-lymphocyte ratio (MLR) on the advancement of hematoma after cerebral contusion.

METHODS

The clinical information and laboratory test findings of people with cerebral contusion were retrospectively analyzed. Using the tertiles of MLR, the study participants were categorized into three groups, enabling the evaluation of the correlation between MLR and the advancement of hematoma after cerebral contusion.

RESULTS

Among the cohort of patients showing progression, MLR levels were significantly higher compared with the nonprogress group (P < 0.001). The high MLR group had a significantly higher proportion of patients with hematoma progression compared with the medium and low MLR groups. However, the medium MLR group had a lower proportion of patients with hematoma progression compared with the low MLR group. High MLR levels were independently linked to a higher risk of hematoma progression (Odds Ratio 3.546, 95% Confidence Interval 1.187-10.597, P = 0.024). By incorporating factors such as Glasgow Coma Scale score on admission, anticoagulant/antiplatelet therapy, white blood cell count, and MLR into the model, the predictive performance of the model significantly improved (area under the curve 0.754).

CONCLUSIONS

Our study suggests that MLR may serve as a potential indicator for predicting the progression of hematoma after cerebral contusion. Further research is necessary to investigate the underlying pathological and physiological mechanisms that contribute to the association between MLR and the progression of hematoma after cerebral contusion and to explore its clinical implications.

A dynamic nomogram for predicting intraoperative brain bulge during decompressive craniectomy in patients with traumatic brain injury: a retrospective study

OBJECTIVE

The aim of this paper is to investigate the risk factors associated with intraoperative brain bulge (IOBB), especially the computed tomography (CT) value of the diseased lateral transverse sinus, and to develop a reliable predictive model to alert neurosurgeons to the possibility of IOBB.

METHODS

A retrospective analysis was performed on 937 patients undergoing traumatic decompressive craniectomy. A total of 644 patients from Fuzong Clinical Medical College of Fujian Medical University were included in the development cohort, and 293 patients from the First Affiliated Hospital of Shantou University Medical College were included in the external validation cohort. Univariate and multifactorial logistic regression analyses identified independent risk factors associated with IOBB. The logistic regression models consisted of independent risk factors, and receiver operating characteristic curves, calibration, and decision curve analyses were used to assess the performance of the models. Various machine learning models were used to compare with the logistic regression model and analyze the importance of the factors, which were eventually jointly developed into a dynamic nomogram for predicting IOBB and published online in the form of a simple calculator.

RESULTS

IOBB occurred in 93/644 (14.4%) patients in the developmental cohort and 47/293 (16.0%) in the validation cohort. Univariate and multifactorial regression analyses showed that age, subdural hematoma, contralateral fracture, brain contusion, and CT value of the diseased lateral transverse sinus were associated with IOBB. A logistic regression model (full model) consisting of the above risk factors had excellent predictive power in both the development cohort [area under the curve (AUC)=0.930] and the validation cohort (AUC=0.913). Among the four machine learning models, the AdaBoost model showed the best predictive value (AUC=0.998). Factors in the AdaBoost model were ranked by importance and combined with the full model to create a dynamic nomogram for clinical application, which was published online as a practical and easy-to-use calculator.

CONCLUSIONS

The CT value of the diseased lateral transverse is an independent risk factor and a reliable predictor of IOBB. The online dynamic nomogram formed by combining logistic regression analysis models and machine learning models can more accurately predict the possibility of IOBBs in patients undergoing traumatic decompressive craniectomy.