Saeed Kayhanian1,2, Adam M H Young3, Chaitanya Mangla4,5, Ibrahim Jalloh3, Helen M Fernandes3, Matthew R Garnett3, Peter J Hutchinson3, Shruti Agrawal6. 1. Department of Clinical Neurosciences, Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK. sk776@cam.ac.uk. 2. Fitzwilliam College, University of Cambridge, Cambridge, UK. sk776@cam.ac.uk. 3. Department of Clinical Neurosciences, Division of Neurosurgery, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK. 4. Fitzwilliam College, University of Cambridge, Cambridge, UK. 5. Department of Computer Science and Technology, University of Cambridge, Cambridge, UK. 6. Department of Paediatric Intensive Care, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK.
Abstract
BACKGROUND: Severe traumatic brain injury (TBI) is a leading cause of mortality in children, but the accurate prediction of outcomes at the point of admission remains very challenging. Admission laboratory results are a promising potential source of prognostic data, but have not been widely explored in paediatric cohorts. Herein, we use machine-learning methods to analyse 14 different serum parameters together and develop a prognostic model to predict 6-month outcomes in children with severe TBI. METHODS: A retrospective review of patients admitted to Cambridge University Hospital's Paediatric Intensive Care Unit between 2009 and 2013 with a TBI. The data for 14 admission serum parameters were recorded. Logistic regression and a support vector machine (SVM) were trained with these data against dichotimised outcomes from the recorded 6-month Glasgow Outcome Scale. RESULTS: Ninety-four patients were identified. Admission levels of lactate, H+, and glucose were identified as being the most informative of 6-month outcomes. Four different models were produced. The SVM using just the three most informative parameters was the best able to predict favourable outcomes at 6 months (sensitivity = 80%, specificity = 99%). CONCLUSIONS: Our results demonstrate the potential for highly accurate outcome prediction after severe paediatric TBI using admission laboratory data.
BACKGROUND: Severe traumatic brain injury (TBI) is a leading cause of mortality in children, but the accurate prediction of outcomes at the point of admission remains very challenging. Admission laboratory results are a promising potential source of prognostic data, but have not been widely explored in paediatric cohorts. Herein, we use machine-learning methods to analyse 14 different serum parameters together and develop a prognostic model to predict 6-month outcomes in children with severe TBI. METHODS: A retrospective review of patients admitted to Cambridge University Hospital's Paediatric Intensive Care Unit between 2009 and 2013 with a TBI. The data for 14 admission serum parameters were recorded. Logistic regression and a support vector machine (SVM) were trained with these data against dichotimised outcomes from the recorded 6-month Glasgow Outcome Scale. RESULTS: Ninety-four patients were identified. Admission levels of lactate, H+, and glucose were identified as being the most informative of 6-month outcomes. Four different models were produced. The SVM using just the three most informative parameters was the best able to predict favourable outcomes at 6 months (sensitivity = 80%, specificity = 99%). CONCLUSIONS: Our results demonstrate the potential for highly accurate outcome prediction after severe paediatric TBI using admission laboratory data.