scholarly journals Outcome prediction model for severe traumatic brain injury

2013 ◽  
Vol 1 (1) ◽  
pp. 31-36 ◽  
Author(s):  
Jiro Iba ◽  
Osamu Tasaki ◽  
Tomohito Hirao ◽  
Tomoyoshi Mohri ◽  
Kazuhisa Yoshiya ◽  
...  
2007 ◽  
Vol 24 (1) ◽  
pp. 136-146 ◽  
Author(s):  
Boon Chuan Pang ◽  
Vellaisamy Kuralmani ◽  
Rohit Joshi ◽  
Yin Hongli ◽  
Kah Keow Lee ◽  
...  

2009 ◽  
Vol 66 (2) ◽  
pp. 304-308 ◽  
Author(s):  
Osamu Tasaki ◽  
Tadahiko Shiozaki ◽  
Toshimitsu Hamasaki ◽  
Kentaro Kajino ◽  
Haruhiko Nakae ◽  
...  

2019 ◽  
Vol 23 (6) ◽  
pp. 670-679
Author(s):  
Krista Greenan ◽  
Sandra L. Taylor ◽  
Daniel Fulkerson ◽  
Kiarash Shahlaie ◽  
Clayton Gerndt ◽  
...  

OBJECTIVEA recent retrospective study of severe traumatic brain injury (TBI) in pediatric patients showed similar outcomes in those with a Glasgow Coma Scale (GCS) score of 3 and those with a score of 4 and reported a favorable long-term outcome in 11.9% of patients. Using decision tree analysis, authors of that study provided criteria to identify patients with a potentially favorable outcome. The authors of the present study sought to validate the previously described decision tree and further inform understanding of the outcomes of children with a GCS score 3 or 4 by using data from multiple institutions and machine learning methods to identify important predictors of outcome.METHODSClinical, radiographic, and outcome data on pediatric TBI patients (age < 18 years) were prospectively collected as part of an institutional TBI registry. Patients with a GCS score of 3 or 4 were selected, and the previously published prediction model was evaluated using this data set. Next, a combined data set that included data from two institutions was used to create a new, more statistically robust model using binomial recursive partitioning to create a decision tree.RESULTSForty-five patients from the institutional TBI registry were included in the present study, as were 67 patients from the previously published data set, for a total of 112 patients in the combined analysis. The previously published prediction model for survival was externally validated and performed only modestly (AUC 0.68, 95% CI 0.47, 0.89). In the combined data set, pupillary response and age were the only predictors retained in the decision tree. Ninety-six percent of patients with bilaterally nonreactive pupils had a poor outcome. If the pupillary response was normal in at least one eye, the outcome subsequently depended on age: 72% of children between 5 months and 6 years old had a favorable outcome, whereas 100% of children younger than 5 months old and 77% of those older than 6 years had poor outcomes. The overall accuracy of the combined prediction model was 90.2% with a sensitivity of 68.4% and specificity of 93.6%.CONCLUSIONSA previously published survival model for severe TBI in children with a low GCS score was externally validated. With a larger data set, however, a simplified and more robust model was developed, and the variables most predictive of outcome were age and pupillary response.


2021 ◽  
Vol 2 (3) ◽  
pp. 01-06
Author(s):  
Yansong Xu ◽  
Zheng Liang

BACKGROUND: Patients with severe traumatic brain injury (sTBI) often presents with extracranial injuries, which may contribute to fatal outcome. The aim of this study was to construct the best death prediction model for sTBI and provide a feasible basis for early prognosis. METHODS: A retrospective study from the First Affiliated Hospital of Guangxi Medical University from January 2012 to September 2020 was performed. Relevant risk factors at admission and record survival were collected at discharge. Logistic regression was used to establish a death prediction model. The performance of the model was predicted by fitting goodness test and calculating the area under the ROC curve (AUC). The DCA curve was used to show the net benefit rate of patients. RESULTS: Of the 190 patients with sTBI, 91 died during hospitalization, with a mortality rate of 47.8 percent. Pupillary dilation, occipital lobe injury, SAH, cerebral hernia, and APACHE II score could predict the probability of death alone, with AUC of 0.636, 0.595, 0.611, 0.599 and 0.621 respectively. The AUC of death prediction for patients with sTBI was 0.860, and its sensitivity and specificity were 88.60% and 81.60%. The calibration and decision curve analysis (DCA) were conducted to validate the performance and clinical value of the novel model. CONCLUSIONS: The clinic-radiomic model incorporating both clinical factors and radiomic signature showed good performance for mortality risk prediction of sTBI. The predictive model can identify sTBI with high sensitivity and can be applied in patients with sTBI.


2012 ◽  
Vol 19 (1) ◽  
pp. 79-89 ◽  
Author(s):  
Bram Jacobs ◽  
Tjemme Beems ◽  
Ton M. van der Vliet ◽  
Arie B. van Vugt ◽  
Cornelia Hoedemaekers ◽  
...  

2015 ◽  
Vol 56 ◽  
pp. 167-174 ◽  
Author(s):  
Konstantinos Kalpakis ◽  
Shiming Yang ◽  
Peter F. Hu ◽  
Colin F. Mackenzie ◽  
Lynn G. Stansbury ◽  
...  

2013 ◽  
Vol 30 (11) ◽  
pp. 946-957 ◽  
Author(s):  
Akash Goyal ◽  
Michelle D. Failla ◽  
Christian Niyonkuru ◽  
Krutika Amin ◽  
Anthony Fabio ◽  
...  

2013 ◽  
Vol 19 (3) ◽  
pp. 364-375 ◽  
Author(s):  
Brian L. Edlow ◽  
Joseph T. Giacino ◽  
Ronald E. Hirschberg ◽  
Jason Gerrard ◽  
Ona Wu ◽  
...  

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