PEDIATRIC SEVERE TRAUMATIC BRAIN INJURY MORTALITY PREDICTION DETERMINED WITH MACHINE LEARNING-BASED MODELING

Abstract INTRODUCTION Acute subdural hematoma (aSDH) in the context of severe traumatic brain injury (TBI) is a neurosurgical emergency. Predictive models have been used in an attempt to modulate the morbidity and mortality of patient outcomes. We used machine learning (ML) to identify admission risk factors predictive of long-term morbidity in the severe TBI patient population with aSDH. METHODS Between 2013 and 2016, 85 patients with severe TBI and aSDH were included in the analysis. Random forest, ML architecture, was used to create a predictive model of long-term morbidity stratification. About 46 patients were included in the high morbidity group [Glasgow Outcome Scale (GOS) 1-2] and 39 patients were in the low morbidity group (GOS 3-5). We included 30 admission input variables including medical and surgical co-morbidities, neurological examination, laboratory values, and radiographic findings. RESULTS The predictive model showed a 78% precision. The highest scoring input variable was the pupillary examination in predicting high vs low morbidity (bilaterally unreactive vs symmetrically reactive; P < .0001). GCS on admission was higher in the low morbidity group (4 [3-7] vs 7 [3-7]; P < .0101). Rotterdam scores were higher in the high-morbidity group (3 [3-5] vs 4 [4-5]; P < .0032). GCS motor examination on admission was higher in the low-morbidity group (5 [1-5] vs. 2 [1-5]; P < .0106). The basal cisterns were found to be more patent in patients with the low-morbidity group (P = .0012). CONCLUSION ML is an efficient tool that can provide a reasonable level of accuracy in predicting long-term morbidity in patients with severe TBI and aSDH. Monitoring these admission criteria can help with risk-stratification of patients into higher and low risk tracks. Integration of ML into the treatment algorithm may allow the development of more refined guidelines to guide goal-directed therapy.

Download Full-text

Pentobarbital Coma For Refractory Intra-Cranial Hypertension After Severe Traumatic Brain Injury: Mortality Predictions and One-Year Outcomes in 55 Patients

Journal of Trauma and Acute Care Surgery ◽

10.1097/ta.0b013e3181de74c7 ◽

2010 ◽

Vol 69 (2) ◽

pp. 275-283 ◽

Cited By ~ 37

Author(s):

Gary T. Marshall ◽

Robert F. James ◽

Matthew P. Landman ◽

Patrick J. OʼNeill ◽

Bryan A. Cotton ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Severe Traumatic Brain Injury ◽

Injury Mortality ◽

One Year

Download Full-text

A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months

Acute and Critical Care ◽

10.4266/acc.2021.00486 ◽

2021 ◽

Author(s):

Mehdi Nourelahi ◽

Fardad Dadboud ◽

Hosseinali Khalili ◽

Amin Niakan ◽

Hossein Parsaei

Keyword(s):

Machine Learning ◽

Traumatic Brain Injury ◽

Brain Injury ◽

Severe Traumatic Brain Injury ◽

Learning Model ◽

Favorable Outcome ◽

Machine Learning Model

Download Full-text

Development of a Mortality Prediction Tool in Pediatric Severe Traumatic Brain Injury

Neurotrauma Reports ◽

10.1089/neur.2020.0039 ◽

2021 ◽

Vol 2 (1) ◽

pp. 115-122

Author(s):

Kawmadi Abeytunge ◽

Michael R. Miller ◽

Saoirse Cameron ◽

Tanya Charyk Stewart ◽

Ibrahim Alharfi ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Severe Traumatic Brain Injury ◽

Mortality Prediction ◽

Prediction Tool

Download Full-text

Machine learning-based dynamic mortality prediction after traumatic brain injury

Scientific Reports ◽

10.1038/s41598-019-53889-6 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 5

Author(s):

Rahul Raj ◽

Teemu Luostarinen ◽

Eetu Pursiainen ◽

Jussi P. Posti ◽

Riikka S. K. Takala ◽

...

Keyword(s):

Machine Learning ◽

Traumatic Brain Injury ◽

Brain Injury ◽

Cross Validation ◽

Characteristic Curve ◽

Mortality Prediction ◽

Internal Validation ◽

Middle Income ◽

Middle Income Countries ◽

Validation Technique

AbstractOur aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified cross-validation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm’s area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60–0.74) on day 1 to 0.81 (95% CI 0.75–0.87) on day 5. The ICP-MAP-CPP-GCS algorithm’s AUC increased from 0.72 (95% CI 0.64–0.78) on day 1 to 0.84 (95% CI 0.78–0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middle-income countries.

Download Full-text

Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury

Frontiers in Medicine ◽

10.3389/fmed.2021.793230 ◽

2021 ◽

Vol 8 ◽

Author(s):

Rui Na Ma ◽

Yi Xuan He ◽

Fu Ping Bai ◽

Zhi Peng Song ◽

Ming Sheng Chen ◽

...

Keyword(s):

Machine Learning ◽

Traumatic Brain Injury ◽

Logistic Regression ◽

Respiratory Failure ◽

Brain Injury ◽

Acute Respiratory Failure ◽

Severe Traumatic Brain Injury ◽

Gradient Boosting ◽

Extreme Gradient Boosting ◽

Out Of Sample

Background: There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes. This study aimed to design a predictive model for ARF.Methods: Adult patients with M-STBI [3 ≤ Glasgow Coma Scale (GCS) ≤ 12] with a definite history of brain trauma and abnormal head on CT images, obtained from September 2015 to May 2017, were included. Patients with age >80 years or <18 years, multiple injuries with TBI upon admission, or pregnancy (in women) were excluded. Two models based on machine learning extreme gradient boosting (XGBoost) or logistic regression, respectively, were developed for predicting ARF within 48 h upon admission. These models were evaluated by out-of-sample validation. The samples were assigned to the training and test sets at a ratio of 3:1.Results: In total, 312 patients were analyzed including 132 (42.3%) patients who had ARF. The GCS and the Marshall CT score, procalcitonin (PCT), and C-reactive protein (CRP) on admission significantly predicted ARF. The novel machine learning XGBoost model was superior to logistic regression model in predicting ARF [area under the receiver operating characteristic (AUROC) = 0.903, 95% CI, 0.834–0.966 vs. AUROC = 0.798, 95% CI, 0.697–0.899; p < 0.05].Conclusion: The XGBoost model could better predict ARF in comparison with logistic regression-based model. Therefore, machine learning methods could help to develop and validate novel predictive models.

Download Full-text