International Journal of Biomedicine. 2021;11(1):18-23.
Originally published March 5, 2021
Background: The present study aimed to detect the degree of midline shift from CT scans and the clinical status of the patient, to evaluate the relationship between the degree of midline shift found by the CT scan and GCS score as a predictor of clinical outcome in head injury patients. Furthermore, we aimed to assess the relationship between midline shift and age, sex, and causes.
Methods and Results: The study included 50 subjects (36 males and 14 females). The age range of the patients in this study was 18–95 years old (mean age of 48.34±17.02 years). The inclusion criteria were patients with traumatic brain injury (TBI) or patients evaluated for level of consciousness by a neurosurgeon. Toshiba 16 Slice CT scanner (Toshiba Medical Systems, Nasu, Japan 2003) was used to scan all patients in the supine, head first position. Contiguous 2 mm slices were obtained using the Toshiba 16-slice machine spiral technique (pitch 1.25–1.5, 0.75 s rotation time, 120 KvP, 2 mm reconstruction interval).
The results indicated that the degree of midline shift in patients with brain injuries was statistically significant as a determinant of clinical outcome. It appeared that the probability of poor clinical outcome was higher when there was a combination of midline shift and other types of intracranial hemorrhage, clinical factors, such as sex, age, and GCS score, and associated injuries. The worst outcome was seen in patients with midline shift and subdural hematoma, when compared with other lesions in patients with brain injuries.
Conclusion: This study suggests that the degree of midline shift may be predictive of clinical outcome in patients with head injuries.
1. Liao CC, Chen YF, Xiao F. Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms. Int J Biomed Imaging. 2018 Apr 12;2018:4303161. doi: 10.1155/2018/4303161.
2. Liao CC, Xiao F, Wong JM, Chiang IJ. Automatic recognition of midline shift on brain CT images. Comput Biol Med. 2010 Mar;40(3):331-9. doi: 10.1016/j.compbiomed.2010.01.004.
3. Liu R, Li S, Chew L, Boon C, Tchoyosom C, Cheng K, Tian Q, Zhang Z. From hemorrhage to midline shift: a new method of tracing the deformed midline in traumatic brain injury CT images. In: 16th IEEE International Conference on Image Processing; 2009: 2637–2640
4. Hiler M, Czosnyka M, Hutchinson P, Balestreri M, Smielewski P, Matta B, Pickard JD. Predictive value of initial computerized tomography scan, intracranial pressure, and state of autoregulation in patients with traumatic brain injury. J Neurosurg. 2006 May;104(5):731-7. doi: 10.3171/jns.2006.104.5.731.
5. Poca MA, Benejam B, Sahuquillo J, Riveiro M, Frascheri L, Merino MA, Delgado P, Alvarez-Sabin J. Monitoring intracranial pressure in patients with malignant middle cerebral artery infarction: is it useful? J Neurosurg. 2010 Mar;112(3):648-57. doi: 10.3171/2009.7.JNS081677.
6. Valadka AB, Gopinath SP, Robertson CS. Midline shift after severe head injury: pathophysiologic implications. J Trauma. 2000 Jul;49(1):1-8; discussion 8-10. doi: 10.1097/00005373-200007000-00001.
7. 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 Feb;28(2):203-15. doi: 10.1089/neu.2010.1558.
8. Jain S, Vyvere TV, Terzopoulos V, Sima DM, Roura E, Maas A, Wilms G, Verheyden J. Automatic Quantification of Computed Tomography Features in Acute Traumatic Brain Injury. J Neurotrauma. 2019 Jun;36(11):1794-1803. doi: 10.1089/neu.2018.6183.
9. Pillai SV, Kolluri VR, Praharaj SS. Outcome prediction model for severe diffuse brain injuries: development and evaluation. Neurol India. 2003 Sep;51(3):345-9.
10. Englander J, Cifu DX, Wright JM, Black K. The association of early computed tomography scan findings and ambulation, self-care, and supervision needs at rehabilitation discharge and at 1 year after traumatic brain injury. Arch Phys Med Rehabil. 2003 Feb;84(2):214-20. doi: 10.1053/apmr.2003.50094.
11. Liu R, Li S, Su B, Tan CL, Leong TY, Pang BC, Lim CC, Lee CK. Automatic detection and quantification of brain midline shift using anatomical marker model. Comput Med Imaging Graph. 2014 Jan;38(1):1-14. doi: 10.1016/j.compmedimag.2013.11.001.
12. Xiao F, Liao CC, Huang KC, Chiang IJ, Wong JM. Automated assessment of midline shift in head injury patients. Clin Neurol Neurosurg. 2010 Nov;112(9):785-90. doi: 10.1016/j.clineuro.2010.06.020.
13. Liao CC, Chiang IJ, Xiao F, Wong JM. Tracing the deformed midline on brain CT. Biomed Eng Appl Basis Comm 2006; 18(06): 305–311. doi: 10.4015/S1016237206000452.
14. Chiewvit P, Tritakarn SO, Nanta-aree S, Suthipongchai S. Degree of midline shift from CT scan predicted outcome in patients with head injuries. J Med Assoc Thai. 2010 Jan;93(1):99-107.
15. Gennarelli TA, Spielman GM, Langfitt TW, Gildenberg PL, Harrington T, Jane JA, Marshall LF, Miller JD, Pitts LH. Influence of the type of intracranial lesion on outcome from severe head injury. J Neurosurg. 1982 Jan;56(1):26-32. doi: 10.3171/jns.1982.56.1.0026.
16. Lobato RD, Cordobes F, Rivas JJ, de la Fuente M, Montero A, Barcena A, Perez C, Cabrera A, Lamas E. Outcome from severe head injury related to the type of intracranial lesion. A computerized tomography study. J Neurosurg. 1983 Nov;59(5):762-74. doi: 10.3171/jns.1983.59.5.0762.
17. Selladurai BM, Jayakumar R, Tan YY, Low HC. Outcome prediction in early management of severe head injury: an experience in Malaysia. Br J Neurosurg. 1992;6(6):549-57. doi: 10.3109/02688699209002372.
Received December 20, 2020.
Accepted January 24, 2020.
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