Nomogram for predicting traumatic subdural effusion after mild traumatic brain injury

Wei, Lichao; Chang, Bowen; Geng, Zhi; Chen, Ming; Cao, Yanghui; Yao, Liang; Ma, Chao

doi:10.3389/fneur.2022.947976

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Age plays a crucial role in determining the prognosis of various diseases (13)(14)(15). After adjusting for multiple factors in our analysis, older age was found to be an independent prognostic factor, .…”

Section: Discussionmentioning

confidence: 65%

Establishment and external validation of a nomogram for predicting 28-day mortality in patients with skull fracture

Tang,

Zhong,

Nijiati

et al. 2024

Front. Neurol.

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BackgroundSkull fracture can lead to significant morbidity and mortality, yet the development of effective predictive tools has remained a challenge. This study aimed to establish and validate a nomogram to evaluate the 28-day mortality risk among patients with skull fracture.Materials and methodsData extracted from the Medical Information Mart for Intensive Care (MIMIC) database were utilized as the training set, while data from the eICU Collaborative Research Database were employed as the external validation set. This nomogram was developed using univariate Cox regression, best subset regression (BSR), and the least absolute shrinkage and selection operator (LASSO) methods. Subsequently, backward stepwise multivariable Cox regression was employed to refine predictor selection. Variance inflation factor (VIF), akaike information criterion (AIC), area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) were used to assess the model's performance.ResultsA total of 1,527 adult patients with skull fracture were enrolled for this analysis. The predictive factors in the final nomogram included age, temperature, serum sodium, mechanical ventilation, vasoactive agent, mannitol, extradural hematoma, loss of consciousness and Glasgow Coma Scale score. The AUC of our nomogram was 0.857, and C-index value was 0.832. After external validation, the model maintained an AUC of 0.853 and a C-index of 0.829. Furthermore, it showed good calibration with a low Brier score of 0.091 in the training set and 0.093 in the external validation set. DCA in both sets revealed that our model was clinically useful.ConclusionA nomogram incorporating nine features was constructed, with a good ability in predicting 28-day mortality in patients with skull fracture.

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Section: Discussionmentioning

confidence: 65%

Establishment and external validation of a nomogram for predicting 28-day mortality in patients with skull fracture

Tang,

Zhong,

Nijiati

et al. 2024

Front. Neurol.

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“…A deterministic linkage dataset study in 2016 found that falling was Belgium's predominant cause of injury. Road safety and aging patients with TBI have increased recently, reducing transport-related TBI mortality and causing a shift in the health care system ( 1 , 6 , 7 ). Over the past two decades, significant advancements have been made in managing severe TBI both surgically and medically ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram

Chen,

Jin,

Zeng

et al. 2023

Front. Neurol.

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ObjectiveTraumatic brain injury (TBI) leads to death and disability. This study developed an effective prognostic nomogram for assessing the risk factors for TBI mortality.MethodData were extracted from an online database called “Multiparameter Intelligent Monitoring in Intensive Care IV” (MIMIC IV). The ICD code obtained data from 2,551 TBI persons (first ICU stay, >18 years old) from this database. R divided samples into 7:3 training and testing cohorts. The univariate analysis determined whether the two cohorts differed statistically in baseline data. This research used forward stepwise logistic regression after independent prognostic factors for these TBI patients. The optimal variables were selected for the model by the optimal subset method. The optimal feature subsets in pattern recognition improved the model prediction, and the minimum BIC forest of the high-dimensional mixed graph model achieved a better prediction effect. A nomogram-labeled TBI-IHM model containing these risk factors was made by nomology in State software. Least Squares OLS was used to build linear models, and then the Receiver Operating Characteristic (ROC) curve was plotted. The TBI-IHM nomogram model's validity was determined by receiver operating characteristic curves (AUCs), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA).ResultThe eight features with a minimal BIC model were mannitol use, mechanical ventilation, vasopressor use, international normalized ratio, urea nitrogen, respiratory rate, and cerebrovascular disease. The proposed nomogram (TBI-IHM model) was the best mortality prediction model, with better discrimination and superior model fitting for severely ill TBI patients staying in ICU. The model's receiver operating characteristic curve (ROC) was the best compared to the seven other models. It might be clinically helpful for doctors to make clinical decisions.ConclusionThe proposed nomogram (TBI-IHM model) has significant potential as a clinical utility in predicting mortality in TBI patients.

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The “Hand as Foot” teaching method in traumatic subdural hydroma

Guo¹,

Su²,

Zhang³

et al. 2023

Asian Journal of Surgery

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Nomogram for predicting traumatic subdural effusion after mild traumatic brain injury

Cited by 5 publications

References 22 publications

Establishment and external validation of a nomogram for predicting 28-day mortality in patients with skull fracture

Establishment and external validation of a nomogram for predicting 28-day mortality in patients with skull fracture

The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram

The “Hand as Foot” teaching method in traumatic subdural hydroma

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