RSS-Feed abonnieren
DOI: 10.1055/s-0036-1586219
Rotterdam Computed Tomography Score to Predict Outcome in Traumatic Brain Injury Patients
Address for correspondence
Publikationsverlauf
04. Januar 2016
24. Mai 2016
Publikationsdatum:
26. Juli 2016 (online)
Abstract
Introduction In this article, we describe our experience of using Rotterdam computed tomography (CT) score at index admission to predict the outcome in traumatic brain injury (TBI) patients.
Materials and Methods A total of 370 TBI patients admitted to the Neurosurgery Intensive Care Unit, Narayana Medical College and Hospital, Andhra Pradesh, between January 2014 and December 2014 were evaluated. Based on availability of emergency CT scan, these patients' charts were reviewed prospectively. CT scan findings were quantified using Rotterdam CT classification (basal cistern, midline shift, and intraventricular blood/subarachnoid blood). Patient characteristic, Glasgow Coma Scale (GCS) score, Rotterdam CT classification, and outcome were analyzed. Correlation between Rotterdam CT classification at index admission and outcome at discharge from the hospital, alive or dead, was assessed.
Results The mean age of patients was 39.19 ± 15.18 years. Rotterdam CT score was significant (p < 0.001) with age, GCS score, and outcome but not significant with gender (p = 0.618). The outcome and individual components of Rotterdam CT classification were statistically significant.
Conclusion Increase in Rotterdam CT score was significantly associated with mortality at discharge. We suggest that it is possible to predict the outcome based on CT scan findings. However, the findings can have shortcomings, due to obvious reasons.
#
Introduction
With the advent of computed tomography (CT) scan, radiological evaluation of traumatic brain injury (TBI) has undergone major changes not only in terms of identifying and localizing intracranial lesions but also for predicting outcome of these patients based on the imaging findings.[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] In this article, we describe our experience of using Rotterdam score to predict the outcome in TBI patients.
#
Material and Methods
From January 2014 to December 2014, a total of 370 TBI patients admitted to the Neurosurgery Intensive Care Unit, Narayana Medical College and Hospital, Andhra Pradesh, were evaluated. Based on availability of emergency CT scan, 370 patients' charts were prospectively reviewed after consent was obtained from our institutional review board. The data collected include demographic information, Glasgow Coma Scale (GCS) score, and CT image details. All TBI patients with lower or worsening GCS score, acute onset of focal neurological deficits, progressively disturbed consciousness, or absence of neurological signs underwent brain CT scans soon after arrival at the emergency department. Rotterdam CT classification was used to categorize the CT scan findings.[12] [13] The individual CT image findings were interpreted and scored according to the Rotterdam CT classification 7 as follows: (a) status of basal cisterns subdivided into normal (0), compressed (1), or absent (2); (b) midline shift subdivided into 0 to 5 mm (0) or more than 5 mm (1); (c) epidural hematoma subdivided into present (0) or absent (1); and (d) traumatic subarachnoid hemorrhage or/and intraventricular hemorrhage subdivided into absent (0) or present (1). Adding plus 1 to the sum score made the grading numerically consistent with the grading of the motor score of the GCS. Outcome assessment was based on patient status at the time of discharge from the hospital, either alive or dead.
#
Statistical Analysis
A common analysis and reporting plan was prepared and analysis of data was done using StatsDirect version 3.0.150 (StatsDirect statistical software, StatsDirect Ltd.: http://www.statsdirect.com). The strength of the association between the Rotterdam CT score and outcome for TBI was examined by a univariate analysis using binary logistic regression models. Results are expressed as frequency for categorical and descriptive for continuous variables and for univariate analysis odds ratios with 95% confidence intervals.
#
Results
The details of 370 patient characteristic, GCS score, Rotterdam CT classification, and outcome are given in [Table 1]. The patient's ages were subclassified into decade wise. Most of the patients were managed conservatively, and in 145 cases neurosurgical intervention was performed. The details of Rotterdam CT classification in each decade is shown in [Table 2]. Distribution of Rotterdam CT classification in mild, moderate, and severe category is shown in [Table 3]. During the study period, 57 (15.4%) patients expired. Rotterdam CT score was significant (p < 0.001) with age, GCS score, and outcome but not significant with gender (p = 0.618). The outcome and individual components of Rotterdam CT classification (basal cistern, midline shift, and intraventricular blood/subarachnoid blood) were statistically significant ([Table 3]). Univariate analysis revealed that the Rotterdam CT score was significantly associated with mortality (odds ratio: 2.783, 95% confidence interval: 2.011–3.852; p < 0.001). The details of 370 patient characteristics, GCS score, and outcome are given in [Table 1]. During study period, 53 (14.3%) patients expired. The outcome and individual components on CT score (subdural blood, intracerebral blood, epidural blood, intraventricular blood/subarachnoid blood, and suprasellar blood) were tested for statistical significant ([Table 3]).
#
Discussion
Several studies have reported different grading systems and have correlated the imaging findings to predict outcome in cases with TBI.[2] [7] [8] [9] [10] [14] [15] [16] Few studies have raised the issue related to the differences between the prognostic models for low-middle and high-income countries and found that only few prognostic models for TBI were developed in low-middle–income countries.[17] [18]
Because of its widespread availability and ability to precisely detect and locate intracranial hematomas, contusions, edema, and other mass lesions, the CT scan has become the investigation of choice in TBI patients.[1] Apart from the clinical characteristics, several studies have demonstrated the role of abnormal and positive CT scan to predict the outcome in patients with TBI.[1] [2] [6] [12] [13] [19] [20] As far as the demographic details are concerned, this study is in agreement with many other studies that the TBI involves young adult males.[21] [22]
Many authors have studied the abnormal CT characteristics and suggested different classification and scoring systems to grade the severity of TBI and, based on these abnormal characteristics, to predict the outcome.[2] [10] [13] [20] [23] [24] [25] [26] [27] [28] It has been found that in moderate and severe TBI, the volume of the intracranial lesions and extent of midline shift are powerful outcome predictors and can be used to predict the outcome of these patients.[29] In the Rotterdam CT score, the authors included individual CT characteristics (i.e., the status of basal cisterns, midline shift, and types of mass lesions or intracranial hemorrhage) and combined them to develop a model to predict the outcome in patients with moderate to severe TBI who underwent decompressive craniectomy.[2] [28] [30] [31] A study involving pediatric patients (<17 years) reports that children with lower scores have better survival outcome as compared with adults with same scoring, but children with higher Rotterdam CT scores have worst survival as compared with adults.[28] The authors concluded that the Rotterdam CT scoring system can be a relatively objective, simple, and practical tool to prognosticate the outcome in both adults and pediatric patients with TBI.[2] [28] [30] [31] In our study, we found that easy-to-use model and result showed that the higher the Rotterdam CT score, the poorer the outcome (it is in agreement with published literature).[2] [28] [30] [31]
#
Conclusion
CT is widely used in emergency as the standard investigation tool for the evaluation of structural injuries and to plan the management of TBI patients. Although it is possible to predict the outcome just based on CT scan findings, because of obvious reasons predicting outcome that is solely based on CT scan findings can have significant shortcomings. To further verify these differences, there is a need for more research with more reliable data from low- and middle-income countries to help improve our understanding regarding the differences (if any) relating to prediction models.
#
#
-
References
- 1 Gupta PK, Atul K, Dwivedi AN , et al. CT scan findings and outcomes of head injury patients: a cross sectional study. J Pak Med Students 2011; 1: 78-82
- 2 Huang YH, Deng YH, Lee TC, Chen WF. Rotterdam computed tomography score as a prognosticator in head-injured patients undergoing decompressive craniectomy. Neurosurgery 2012; 71 (1) 80-85
- 3 Duhaime AC, Gean AD, Haacke EM , et al; Common Data Elements Neuroimaging Working Group Members, Pediatric Working Group Members. Common data elements in radiologic imaging of traumatic brain injury. Arch Phys Med Rehabil 2010; 91 (11) 1661-1666
- 4 Nagurney JT, Borczuk P, Thomas SH. Elderly patients with closed head trauma after a fall: mechanisms and outcomes. J Emerg Med 1998; 16 (5) 709-713
- 5 Zimmerman RA, Bilaniuk LT, Gennarelli T, Bruce D, Dolinskas C, Uzzell B. Cranial computed tomography in diagnosis and management of acute head trauma. AJR Am J Roentgenol 1978; 131 (1) 27-34
- 6 Panil Kumar B, Hegde KV, Agrawal A, Rooparani K. Indications and timing for CT scan in traumatic brain injury and analysis of CT scan findings. Narayana Medical Journal 2012; 1 (2) 35-46
- 7 Havill JH, Sleigh JW, Davis GM , et al. Observer error and prediction of outcome—grading of head injury based on computerised tomography. Crit Care Resusc 2001; 3 (1) 15-18
- 8 Lipper MH, Kishore PR, Enas GG, Domingues da Silva AA, Choi SC, Becker DP. Computed tomography in the prediction of outcome in head injury. AJR Am J Roentgenol 1985; 144 (3) 483-486
- 9 Liu HM, Tu YK, Su CT. Changes of brainstem and perimesencephalic cistern: dynamic predictor of outcome in severe head injury. J Trauma 1995; 38 (3) 330-333
- 10 Marshall LF, Marshall SB, Klauber MR , et al. A new classification of head injury based on computerized tomography. JNS 1991; 75 (1s) S14-S20
- 11 Thomas BW, Mejia VA, Maxwell RA , et al. Scheduled repeat CT scanning for traumatic brain injury remains important in assessing head injury progression. J Am Coll Surg 2010; 210 (5) 824-830 , 831–832
- 12 Raj R, Siironen J, Skrifvars MB, Hernesniemi J, Kivisaari R. Predicting outcome in traumatic brain injury: development of a novel computerized tomography classification system (Helsinki computerized tomography score). Neurosurgery 2014; 75 (6) 632-646 , discussion 646–647
- 13 Maas AI, Hukkelhoven CW, Marshall LF, Steyerberg EW. Prediction of outcome in traumatic brain injury with computed tomographic characteristics: a comparison between the computed tomographic classification and combinations of computed tomographic predictors. Neurosurgery 2005; 57 (6) 1173-1182 , discussion 1173–1182
- 14 Mebrahtu-Ghebrehiwet M, Quan L, Andebirhan T. The profile of CT scan findings in acute head trauma in Orotta Hospital, Asmara, Eritrea. J Eritrean Med Assoc 2009; 4 (1) 5-8
- 15 Yuh EL, Cooper SR, Ferguson AR, Manley GT. Quantitative CT improves outcome prediction in acute traumatic brain injury. J Neurotrauma 2012; 29 (5) 735-746
- 16 Zhu GW, Wang F, Liu WG. Classification and prediction of outcome in traumatic brain injury based on computed tomographic imaging. J Int Med Res 2009; 37 (4) 983-995
- 17 Perel P, Edwards P, Wentz R, Roberts I. Systematic review of prognostic models in traumatic brain injury. BMC Med Inform Decis Mak 2006; 6: 38
- 18 Hofman K, Primack A, Keusch G, Hrynkow S. Addressing the growing burden of trauma and injury in low- and middle-income countries. Am J Public Health 2005; 95 (1) 13-17
- 19 Jeret JS, Mandell M, Anziska B , et al. Clinical predictors of abnormality disclosed by computed tomography after mild head trauma. Neurosurgery 1993; 32 (1) 9-15 , discussion 15–16
- 20 Murray GD, Butcher I, McHugh GS , et al. Multivariable prognostic analysis in traumatic brain injury: results from the IMPACT study. J Neurotrauma 2007; 24 (2) 329-337
- 21 Bharti P, Nagar AM, Umesh T. Pattern of trauma in western Uttar Pradesh. Neurol India 1993; 41: 49-50
- 22 Hukkelhoven CW, Steyerberg EW, Rampen AJ , et al. Patient age and outcome following severe traumatic brain injury: an analysis of 5600 patients. J Neurosurg 2003; 99 (4) 666-673
- 23 Chesnut RM, Ghajar J, Maas AR J. Guidelines for the management and prognosis of severe traumatic brain injury part II: early indicators of prognosis in severe traumatic brain injury. J Neurotrauma 2000; 17: 556-627
- 24 Compagnone C, Murray GD, Teasdale GM , et al; European Brain Injury Consortium. The management of patients with intradural post-traumatic mass lesions: a multicenter survey of current approaches to surgical management in 729 patients coordinated by the European Brain Injury Consortium. Neurosurgery 2005; 57 (6) 1183-1192 , discussion 1183–1192
- 25 Lobato RD, Gomez PA, Alday R , et al. Sequential computerized tomography changes and related final outcome in severe head injury patients. Acta Neurochir (Wien) 1997; 139 (5) 385-391
- 26 Servadei F, Nasi MT, Giuliani G , et al. CT prognostic factors in acute subdural haematomas: the value of the ‘worst’ CT scan. Br J Neurosurg 2000; 14 (2) 110-116
- 27 Steyerberg EW, Mushkudiani N, Perel P , et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 2008; 5 (8) e165 , discussion e165
- 28 Liesemer K, Riva-Cambrin J, Bennett KS , et al. Use of Rotterdam CT scores for mortality risk stratification in children with traumatic brain injury. Pediatr Crit Care Med 2014; 15 (6) 554-562
- 29 Jacobs B, Beems T, van der Vliet TM, Diaz-Arrastia RR, Borm GF, Vos PE. Computed tomography and outcome in moderate and severe traumatic brain injury: hematoma volume and midline shift revisited. J Neurotrauma 2011; 28 (2) 203-215
- 30 Fujimoto K, Miura M, Otsuka T, Kuratsu J. Sequential changes in Rotterdam CT scores related to outcomes for patients with traumatic brain injury who undergo decompressive craniectomy. J Neurosurg 2016; 124 (6) 1640-1645
- 31 Waqas M, Shamim MS, Enam SF , et al. Predicting outcomes of decompressive craniectomy: use of Rotterdam Computed Tomography Classification and Marshall Classification. Br J Neurosurg 2016; 30 (2) 258-263
Address for correspondence
-
References
- 1 Gupta PK, Atul K, Dwivedi AN , et al. CT scan findings and outcomes of head injury patients: a cross sectional study. J Pak Med Students 2011; 1: 78-82
- 2 Huang YH, Deng YH, Lee TC, Chen WF. Rotterdam computed tomography score as a prognosticator in head-injured patients undergoing decompressive craniectomy. Neurosurgery 2012; 71 (1) 80-85
- 3 Duhaime AC, Gean AD, Haacke EM , et al; Common Data Elements Neuroimaging Working Group Members, Pediatric Working Group Members. Common data elements in radiologic imaging of traumatic brain injury. Arch Phys Med Rehabil 2010; 91 (11) 1661-1666
- 4 Nagurney JT, Borczuk P, Thomas SH. Elderly patients with closed head trauma after a fall: mechanisms and outcomes. J Emerg Med 1998; 16 (5) 709-713
- 5 Zimmerman RA, Bilaniuk LT, Gennarelli T, Bruce D, Dolinskas C, Uzzell B. Cranial computed tomography in diagnosis and management of acute head trauma. AJR Am J Roentgenol 1978; 131 (1) 27-34
- 6 Panil Kumar B, Hegde KV, Agrawal A, Rooparani K. Indications and timing for CT scan in traumatic brain injury and analysis of CT scan findings. Narayana Medical Journal 2012; 1 (2) 35-46
- 7 Havill JH, Sleigh JW, Davis GM , et al. Observer error and prediction of outcome—grading of head injury based on computerised tomography. Crit Care Resusc 2001; 3 (1) 15-18
- 8 Lipper MH, Kishore PR, Enas GG, Domingues da Silva AA, Choi SC, Becker DP. Computed tomography in the prediction of outcome in head injury. AJR Am J Roentgenol 1985; 144 (3) 483-486
- 9 Liu HM, Tu YK, Su CT. Changes of brainstem and perimesencephalic cistern: dynamic predictor of outcome in severe head injury. J Trauma 1995; 38 (3) 330-333
- 10 Marshall LF, Marshall SB, Klauber MR , et al. A new classification of head injury based on computerized tomography. JNS 1991; 75 (1s) S14-S20
- 11 Thomas BW, Mejia VA, Maxwell RA , et al. Scheduled repeat CT scanning for traumatic brain injury remains important in assessing head injury progression. J Am Coll Surg 2010; 210 (5) 824-830 , 831–832
- 12 Raj R, Siironen J, Skrifvars MB, Hernesniemi J, Kivisaari R. Predicting outcome in traumatic brain injury: development of a novel computerized tomography classification system (Helsinki computerized tomography score). Neurosurgery 2014; 75 (6) 632-646 , discussion 646–647
- 13 Maas AI, Hukkelhoven CW, Marshall LF, Steyerberg EW. Prediction of outcome in traumatic brain injury with computed tomographic characteristics: a comparison between the computed tomographic classification and combinations of computed tomographic predictors. Neurosurgery 2005; 57 (6) 1173-1182 , discussion 1173–1182
- 14 Mebrahtu-Ghebrehiwet M, Quan L, Andebirhan T. The profile of CT scan findings in acute head trauma in Orotta Hospital, Asmara, Eritrea. J Eritrean Med Assoc 2009; 4 (1) 5-8
- 15 Yuh EL, Cooper SR, Ferguson AR, Manley GT. Quantitative CT improves outcome prediction in acute traumatic brain injury. J Neurotrauma 2012; 29 (5) 735-746
- 16 Zhu GW, Wang F, Liu WG. Classification and prediction of outcome in traumatic brain injury based on computed tomographic imaging. J Int Med Res 2009; 37 (4) 983-995
- 17 Perel P, Edwards P, Wentz R, Roberts I. Systematic review of prognostic models in traumatic brain injury. BMC Med Inform Decis Mak 2006; 6: 38
- 18 Hofman K, Primack A, Keusch G, Hrynkow S. Addressing the growing burden of trauma and injury in low- and middle-income countries. Am J Public Health 2005; 95 (1) 13-17
- 19 Jeret JS, Mandell M, Anziska B , et al. Clinical predictors of abnormality disclosed by computed tomography after mild head trauma. Neurosurgery 1993; 32 (1) 9-15 , discussion 15–16
- 20 Murray GD, Butcher I, McHugh GS , et al. Multivariable prognostic analysis in traumatic brain injury: results from the IMPACT study. J Neurotrauma 2007; 24 (2) 329-337
- 21 Bharti P, Nagar AM, Umesh T. Pattern of trauma in western Uttar Pradesh. Neurol India 1993; 41: 49-50
- 22 Hukkelhoven CW, Steyerberg EW, Rampen AJ , et al. Patient age and outcome following severe traumatic brain injury: an analysis of 5600 patients. J Neurosurg 2003; 99 (4) 666-673
- 23 Chesnut RM, Ghajar J, Maas AR J. Guidelines for the management and prognosis of severe traumatic brain injury part II: early indicators of prognosis in severe traumatic brain injury. J Neurotrauma 2000; 17: 556-627
- 24 Compagnone C, Murray GD, Teasdale GM , et al; European Brain Injury Consortium. The management of patients with intradural post-traumatic mass lesions: a multicenter survey of current approaches to surgical management in 729 patients coordinated by the European Brain Injury Consortium. Neurosurgery 2005; 57 (6) 1183-1192 , discussion 1183–1192
- 25 Lobato RD, Gomez PA, Alday R , et al. Sequential computerized tomography changes and related final outcome in severe head injury patients. Acta Neurochir (Wien) 1997; 139 (5) 385-391
- 26 Servadei F, Nasi MT, Giuliani G , et al. CT prognostic factors in acute subdural haematomas: the value of the ‘worst’ CT scan. Br J Neurosurg 2000; 14 (2) 110-116
- 27 Steyerberg EW, Mushkudiani N, Perel P , et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 2008; 5 (8) e165 , discussion e165
- 28 Liesemer K, Riva-Cambrin J, Bennett KS , et al. Use of Rotterdam CT scores for mortality risk stratification in children with traumatic brain injury. Pediatr Crit Care Med 2014; 15 (6) 554-562
- 29 Jacobs B, Beems T, van der Vliet TM, Diaz-Arrastia RR, Borm GF, Vos PE. Computed tomography and outcome in moderate and severe traumatic brain injury: hematoma volume and midline shift revisited. J Neurotrauma 2011; 28 (2) 203-215
- 30 Fujimoto K, Miura M, Otsuka T, Kuratsu J. Sequential changes in Rotterdam CT scores related to outcomes for patients with traumatic brain injury who undergo decompressive craniectomy. J Neurosurg 2016; 124 (6) 1640-1645
- 31 Waqas M, Shamim MS, Enam SF , et al. Predicting outcomes of decompressive craniectomy: use of Rotterdam Computed Tomography Classification and Marshall Classification. Br J Neurosurg 2016; 30 (2) 258-263