Development and Validation of a Nomogram for Predicting the Mortality after Penetrating Traumatic Brain Injury

Prognostic factors and clinical nomogram for in-hospital mortality in traumatic brain injury

BACKGROUND

Traumatic brain injury (TBI) is a major cause of death and functional disability in the general population. The nomogram is a clinical prediction tool that has been researched for a wide range of medical conditions. The purpose of this study was to identify prognostic factors associated with in-hospital mortality. The secondary objective was to develop a clinical nomogram for TBI patients' in-hospital mortality based on prognostic factors.

METHODS

A retrospective cohort study was conducted to analyze 14,075 TBI patients who were admitted to a tertiary hospital in southern Thailand. The total dataset was divided into the training and validation datasets. Several clinical characteristics and imaging findings were analyzed for in-hospital mortality in both univariate and multivariable analyses using the training dataset. Based on binary logistic regression, the nomogram was developed and internally validated using the final predictive model. Therefore, the predictive performances of the nomogram were estimated by the validation dataset.

RESULTS

Prognostic factors associated with in-hospital mortality comprised age, hypotension, antiplatelet, Glasgow coma scale score, pupillary light reflex, basilar skull fracture, acute subdural hematoma, subarachnoid hemorrhage, midline shift, and basal cistern obliteration that were used for building nomogram. The predictive performance of the nomogram was estimated by the training dataset; the area under the receiver operating characteristic curve (AUC) was 0.981. In addition, the AUCs of bootstrapping and cross-validation methods were 0.980 and 0.981, respectively. For the temporal validation with an unseen dataset, the sensitivity, specificity, accuracy, and AUC of the nomogram were 0.90, 0.88, 0.88, and 0.89, respectively.

CONCLUSION

A nomogram developed from prognostic factors had excellent performance; thus, the tool had the potential to serve as a screening tool for prognostication in TBI patients. Furthermore, future research should involve geographic validation to examine the predictive performances of the clinical prediction tool.

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

Objective

Traumatic brain injury (TBI) leads to death and disability. This study developed an effective prognostic nomogram for assessing the risk factors for TBI mortality.

Method

Data 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).

Result

The 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.

Conclusion

The proposed nomogram (TBI-IHM model) has significant potential as a clinical utility in predicting mortality in TBI patients.

Prognostic Impact of the Combination of MGMT Methylation and TERT Promoter Mutation in Glioblastoma

Background  The concept of combinational analysis between the methylation of O ⁶ -methylguanine-DNA methyltransferase ( MGMT ) and telomerase reverse transcriptase promoter ( pTERT ) mutation in glioblastoma (GBM) has been reported. The main study objective was to determine the prognosis of patients with GBM based on MGMT/pTERT classification, while the secondary objective was to estimate the temozolomide effect on the survival time of GBM with MGMT/pTERT classification. Methods  A total of 50 GBM specimens were collected after tumor resection and were selected for investigating MGMT methylation and pTERT mutation. Clinical imaging and pathological characteristics were retrospectively analyzed. Patients with MGMT/pTERT classification were analyzed using survival analysis to develop the nomogram for forecasting and individual prognosis. Results  All patients underwent resection (total resection: 28%, partial resection: 64%, biopsy: 8%). Thirty-two percent of all cases received adjuvant temozolomide with radiotherapy. Sixty-four percent of the case was found methylated MGMT , and 56% of the present cohort found pTERT mutation. Following combinational analysis of biomarkers, results showed that the GBMs with methylated MGMT and wild-type pTERT had a superior prognosis compared with other subtypes. Using Cox regression analysis with multivariable analysis, the extent of resection, postoperative chemoradiotherapy, MGMT/pTERT classification were associated with a favorable prognosis. Hence, a web-based nomogram was developed for deploying individual prognostication. Conclusions  The interaction of MGMT methylation and pTERT mutation was confirmed for predicting prognosis. The results from the present study could help physicians create treatment strategies for GBM patients in real-world situations.

Application of machine learning to predict the outcome of pediatric traumatic brain injury

PURPOSE

Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI.

METHODS

A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets.

RESULTS

There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78.

CONCLUSION

The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.

The value of simplicity: externally validating the Baylor cranial gunshot wound prognosis score

OBJECTIVE

Gunshot wounds to the head (GSWH) are devastating injuries with a grim prognosis. Several prognostic scores have been created to estimate mortality and functional outcome, including the so-called Baylor score, an uncomplicated scoring method based on bullet trajectory, patient age, and neurological status on admission. This study aimed to validate the Baylor score within a temporally, institutionally, and geographically distinct patient population.

METHODS

Data were obtained from the trauma registry at a level I trauma center in the southeastern US. Patients with a GSWH in which dural penetration occurred were identified from data collected between January 1, 2009, and June 30, 2019. Patient demographics, medical history, bullet trajectory, intent of GSWH (e.g., suicide), admission vital signs, Glasgow Coma Scale score, pupillary response, laboratory studies, and imaging reports were collected. The Baylor score was calculated directly by using its clinical components. The ability of the Baylor score to predict mortality and good functional outcome (Glasgow Outcome Scale score 4 or 5) was assessed using the receiver operating characteristic curve and the area under the curve (AUC) as a measure of performance.

RESULTS

A total of 297 patients met inclusion criteria (mean age 38.0 [SD 15.7] years, 73.4% White, 85.2% male). A total of 205 (69.0%) patients died, whereas 69 (23.2%) patients had good functional outcome. Overall, the Baylor score showed excellent discrimination of mortality (AUC = 0.88) and good functional outcome (AUC = 0.90). Baylor scores of 3-5 underestimated mortality. Baylor scores of 0, 1, and 2 underestimated good functional outcome.

CONCLUSIONS

The Baylor score is an accurate and easy-to-use prognostic scoring tool that demonstrated relatively stable performance in a distinct cohort between 2009 and 2019. In the current era of trauma management, providers may continue to use the score at the point of admission to guide family counseling and to direct investment of healthcare resources.

Incorporating conditional survival into prognostication for gunshot wounds to the head

OBJECTIVE

Several scores estimate the prognosis for gunshot wounds to the head (GSWH) at the point of hospital admission. However, prognosis may change over the course of the hospital stay. This study measures the accuracy of the Baylor score among patients who have already survived the acute phase of hospitalization and generates conditional outcome curves for the duration of hospital stay for patients with GSWH.

METHODS

Patients in whom GSWH with dural penetration occurred between January 2009 and June 2019 were identified from a trauma registry at a level I trauma center in the southeastern US. The Baylor score was calculated using component variables. Conditional overall survival and good functional outcome (Glasgow Outcome Scale score of 4 or 5) curves were generated. The accuracy of the Baylor score in predicting mortality and functional outcome among acute-phase survivors (survival > 48 hours) was assessed using receiver operating characteristic curves and the area under the curve (AUC).

RESULTS

A total of 297 patients were included (mean age 38.0 [SD 15.7] years, 73.4% White, 85.2% male), and 129 patients survived the initial 48 hours of admission. These acute-phase survivors had a decreased mortality rate of 32.6% (n = 42) compared to 68.4% (n = 203) for all patients, and an increased rate of good functional outcome (48.1%; n = 62) compared to the rate for all patients (23.2%; n = 69). Among acute-phase survivors, the Baylor score accurately predicted mortality (AUC = 0.807) and functional outcome (AUC = 0.837). However, the Baylor score generally overestimated true mortality rates and underestimated good functional outcome. Additionally, hospital day 18 represented an inflection point of decreasing probability of good functional outcome.

CONCLUSIONS

During admission for GSWH, surviving beyond the acute phase of 48 hours doubles the rates of survival and good functional outcome. The Baylor score maintains reasonable accuracy in predicting these outcomes for acute-phase survivors, but generally overestimates mortality and underestimates good functional outcome. Future prognostic models should incorporate conditional survival to improve the accuracy of prognostication after the acute phase.

A nomogram to predict in-hospital mortality of neonates admitted to the intensive care unit

Background

To explore the influencing factors for in-hospital mortality in the neonatal intensive care unit (NICU) and to establish a predictive nomogram.

Methods

Neonatal data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Both univariate and multivariate logit binomial general linear models were used to analyse the factors influencing neonatal death. The area under the receiver operating characteristics (ROC) curve was used to assess the predictive model, which was visualized by a nomogram.

Results

A total of 1258 neonates from the NICU in the MIMIC-III database were eligible for the study, including 1194 surviving patients and 64 deaths. Multivariate analysis showed that red cell distribution width (RDW) (odds ratio [OR] 0.813, p=0.003) and total bilirubin (TBIL; OR 0.644, p<0.001) had protective effects on neonatal in-hospital death, while lymphocytes (OR 1.205, p=0.025), arterial partial pressure of carbon dioxide (PaCO₂; OR 1.294, p=0.016) and sequential organ failure assessment (SOFA) score (OR 1.483, p<0.001) were its independent risk factors. Based on this, the area under the curve of this predictive model was up to 0.865 (95% confidence interval 0.813 to 0.917), which was also confirmed by a nomogram.

Conclusions

The nomogram constructed suggests that RDW, TBIL, lymphocytes, PaCO₂ and SOFA score are all significant predictors for in-hospital mortality in the NICU.

Clinical Nomogram Predicting Intracranial Injury in Pediatric Traumatic Brain Injury

Background:

There are differences in injured mechanisms among pediatric traumatic brain injury (TBI) in developing countries. This study aimed to develop and validate clinical nomogram for predicting intracranial injury in pediatric TBI that will be implicated in balancing the unnecessary investigation in the general practice.

Materials and Methods:

The retrospective study was conducted in all patients who were younger than 15 years old and underwent computed tomography (CT) of the brain after TBI in southern Thailand. Injured mechanisms and clinical characteristics were identified and analyzed with binary logistic regression for predicting intracranial injury. Using random sampling without replacement, the total data was split into nomogram developing dataset (80%) and testing dataset (20%). Therefore, a nomogram was constructed and applied via the web-based application from the developing dataset. Using testing dataset, validation as binary classifiers was performed by various probabilities levels.

Results:

A total of 900 victims were enrolled. The mean age was 87.2 (standard deviation [SD] 57.4) months, and 65.3% of all patients injured were from road traffic accidents. The rate of positive findings in CT of the brain was 32.8%. A nomogram was developed from the significant variables, including age groups, road traffic accidents, loss of consciousness, scalp hematoma/laceration, motor weakness, signs of basilar skull fraction, low Glasgow Coma Scale score, and pupillary light reflex.

Therefore, a nomogram was developed from 80% of data and was validated from 20% of data. The accuracy, sensitivity, specificity, positive, and negative predictive values of the nomogram were 0.83, 0.42, 1.00, 1.00, and 0.81 at a cutoff value of 0.5 probability.

Conclusion:

This study provides a clinical nomogram that will be applied to making decisions in general practice as a diagnostic tool from high specificity.