Prognostic model for patients with traumatic brain injuries and abnormal computed tomography scans

Rosí, Jefferson; Welling, Leonardo C.; Schafranski, Marcelo Derbli; Yeng, Lin Tchia; Prado, Reginaldo do; Koterba, Edwin; Andrade, Almir Ferreira de; Teixeira, Manoel Jacobsen; Figueiredo, Eberval Gadelha

doi:10.1016/j.jocn.2017.03.012

Cited by 15 publications

(16 citation statements)

References 36 publications

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“…However, this advantage could not be identified in the current study, as there was no significant difference in AUC values between the LR and SVM. The exceptional performance of the LR model implies that most patients that died following TBI could be explained by a relatively small set of independent predictors that fit the logistic model assumptions well, such as old age [ 32 ], hyperglycemia [ 33 ], GCS scores [ 32 , 34 ], and the presence of SDH [ 35 ]. Therefore, to improve the predictive performance of models than the LR, additional data or different approaches may be considered for the prediction task.…”

Section: Discussionmentioning

confidence: 99%

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models

et al. 2018

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BackgroundThe purpose of this study was to build a model of machine learning (ML) for the prediction of mortality in patients with isolated moderate and severe traumatic brain injury (TBI).MethodsHospitalized adult patients registered in the Trauma Registry System between January 2009 and December 2015 were enrolled in this study. Only patients with an Abbreviated Injury Scale (AIS) score ≥ 3 points related to head injuries were included in this study. A total of 1734 (1564 survival and 170 non-survival) and 325 (293 survival and 32 non-survival) patients were included in the training and test sets, respectively.ResultsUsing demographics and injury characteristics, as well as patient laboratory data, predictive tools (e.g., logistic regression [LR], support vector machine [SVM], decision tree [DT], naive Bayes [NB], and artificial neural networks [ANN]) were used to determine the mortality of individual patients. The predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operator characteristic curves. In the training set, all five ML models had a specificity of more than 90% and all ML models (except the NB) achieved an accuracy of more than 90%. Among them, the ANN had the highest sensitivity (80.59%) in mortality prediction. Regarding performance, the ANN had the highest AUC (0.968), followed by the LR (0.942), SVM (0.935), NB (0.908), and DT (0.872). In the test set, the ANN had the highest sensitivity (84.38%) in mortality prediction, followed by the SVM (65.63%), LR (59.38%), NB (59.38%), and DT (43.75%).ConclusionsThe ANN model provided the best prediction of mortality for patients with isolated moderate and severe TBI.

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Section: Discussionmentioning

confidence: 99%

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models

et al. 2018

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“…In Brazil, like in several other LMIC, the TBI related mortality and morbidity are not systematically recorded and only two studies evaluated prospectively the hospital mortality of patients with TBI and determined its independent predictors (15, 16) In our previous single center, 748 patients from Florianópolis, the Santa Catarina State capital (Southern Brazil), admitted with coma Glasgow scale (GCS) ≤ 8 were included between January 1994 and December 2003 (16). Another Brazilian study included patients admitted at the emergency unit of the University of São Paulo Hospital with an admission GCS score who had an abnormal brain computerized tomography (CT) scan ( n = 1,275) between September 2003 and December 2009.…”

Section: Introductionmentioning

confidence: 99%

Predictors of Hospital Mortality and the Related Burden of Disease in Severe Traumatic Brain Injury: A Prospective Multicentric Study in Brazil

et al. 2019

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Traumatic brain injury (TBI) is a worldwide social, economic, and health problem related to premature death and long-term disabilities. There were no prospective and multicentric studies analyzing the predictors of TBI related mortality and estimating the burden of TBI in Brazil. To address this gap, we investigated prospectively: (1) the hospital mortality and its determinants in patients admitted with severe TBI we analyzed in three reference centers; (2) the burden of TBI estimated by the years of life lost (YLLs) due to premature death based on the hospital mortality considering the hospital mortality. Between April 2014 and January 2016 (22 months), all the 266 patients admitted with Glasgow coma scale (GCS), ≤ 8 admitted in three TBI reference centers were included in the study. These centers cover a population of 1,527,378 population of the Santa Catarina state, Southern Brazil. Most patients were male ( n = 230, 86.5%), with a mean (SD) age of 38 (17) years. Hospital mortality was 31.1% ( n = 83) and independently associated with older age, worse cranial CT injury by the Marshall classification, the presence of subarachnoid hemorrhage in the CT, lower GCS scores and abnormal pupils at admission. The final multiple logistic regression model including these variables showed an overall accuracy for hospital mortality of 77.9% (specificity 88.6%, sensitivity 53.8%, PPV 67.7%, and NPV 81.1%). The estimated annual incidence of hospitalizations and mortality due to severe TBI were 9.5 cases and 5.43 per 100,000 inhabitants, respectively. The estimated YLLs in 22 months, in the 2 metropolitan areas were 2,841, corresponding to 1,550 YLLs per year and 101.5 YLLs per 100,000 people every year. The hospital mortality did not change significantly since the end of the 1990s and was similar to other centers in Brazil and Latin America. Significant predictors of hospital mortality were the same as those of studies worldwide, but their strength of association seemed to differ according to countries income. Present study results question the extrapolation of TBI hospital mortality models for high income to lower- and middle-income countries and therefore have implications for TBI multicentric trials including countries with different income levels.

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“…Severe traumatic brain injuries (TBI) are usually observed secondary to high velocity head trauma [ 1 ]. Several demographic and clinical data, like age, sex (male), Glasgow coma scale (GCS) at admission or pupillary response [ 2 , 3 ] may influence the neurological outcome in severe TBI. Most of the intracranial intraparenchymal traumatic lesions are located in the frontobasal area and at anterior aspect of the temporal lobes [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Lesions in deep gray nuclei after severe traumatic brain injury predict neurologic outcome

et al. 2017

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PurposeThis study evaluates the correlation between injuries to deep gray matter nuclei, as quantitated by lesions in these nuclei on MR T2 Fast Spin Echo (T2 FSE) images, with 6-month neurological outcome after severe traumatic brain injury (TBI).Materials and methodsNinety-five patients (80 males, mean age = 36.7y) with severe TBI were prospectively enrolled. All patients underwent a MR scan within the 45 days after the trauma that included a T2 FSE acquisition. A 3D deformable atlas of the deep gray matter was registered to this sequence; deep gray matter lesions (DGML) were evaluated using a semi-quantitative classification scheme. The 6-month outcome was dichotomized into unfavorable (death, vegetative or minimally conscious state) or favorable (minimal or no neurologic deficit) outcome.ResultsSixty-six percent of the patients (63/95) had both satisfactory registration of the 3D atlas on T2 FSE and available clinical follow-up. Patients without DGML had an 89% chance (P = 0.0016) of favorable outcome while those with bilateral DGML had an 80% risk of unfavorable outcome (P = 0.00008). Multivariate analysis based on DGML accurately classified patients with unfavorable neurological outcome in 90.5% of the cases.ConclusionLesions in deep gray matter nuclei may predict long-term outcome after severe TBI with high sensitivity and specificity.

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Prognostic model for patients with traumatic brain injuries and abnormal computed tomography scans

Cited by 15 publications

References 36 publications

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models

Predictors of Hospital Mortality and the Related Burden of Disease in Severe Traumatic Brain Injury: A Prospective Multicentric Study in Brazil

Lesions in deep gray nuclei after severe traumatic brain injury predict neurologic outcome

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