Evaluation of Brain Stroke by Using Computed Tomography: Brain Stroke by Using Computed Tomography

Almeera Anwar; Areeba Akram; Neha Tanveer

doi:10.54393/pbmj.v8i12.1297

Authors

Almeera Anwar Department of Medical Imaging Sciences, The University of Lahore, Lahore, Pakistan
Areeba Akram Department of Medical Imaging Sciences, The University of Lahore, Lahore, Pakistan
Neha Tanveer Department of Medical Imaging Sciences, The University of Lahore, Lahore, Pakistan

DOI:

https://doi.org/10.54393/pbmj.v8i12.1297

Keywords:

Brain Stroke, Neuroimaging Findings, Computed Tomography, Predominant Stroke Type

Abstract

A stroke is a serious emergency in the medical sector, occurring when the flow of blood to the brain is blocked or cut short. This may occur due to the bursting of a blood vessel within the brain, an ischemic stroke, or the blocking of a blood vessel in the brain (Hemorrhagic stroke). Objectives: To assess the demographic profiles, clinical presentations, and neuroimaging images of brain stroke patients with the help of computed tomography and name the most common type of stroke and risk factors in a Pakistani sample. Methods: This cross-sectional study was done in a setting of Gondal Medical Complex Hospital, Gujranwala, Pakistan, with a sample size of 60 patients who had brain CT scans after a brain stroke. 16 slices CT machine was applied in the study. Results: It was as per this research that there was a greater impact of brain strokes on men compared to women. The age between 71 and 90 years is the most affected age group, and the frontal lobe is the most affected site, with hypertension being the major contributor. Conclusions: As per the results of this study, a CT scan is an excellent diagnostic measure in the location and cause of a brain stroke. Hypertension is the primary risk factor for brain stroke.

References

Mahgoub M, Ayad CE, Ahmed HA. Evaluation of Magnetic Resonance Imaging in the Diagnosis of Brain Stroke in Khartoum State, Sudan. Scholars Journal of Applied Medical Sciences. 2023 Mar; 3: 650-61. doi: 10.36347/sjams.2023.v11i03.029. DOI: https://doi.org/10.36347/sjams.2023.v11i03.029

Hamid GS. Risk Factors of CVA at Omdurman–Sudan April 2015 to August 2015 (Doctoral Dissertation). 2015.

Gomes J and Wachsman AM. Types of strokes. InHandbook of Clinical Nutrition and Stroke. Totowa, NJ: Humana Press. 2013 Mar: 15-31. doi: 10.1007/978-1-62703-380-0_2. DOI: https://doi.org/10.1007/978-1-62703-380-0_2

Patel A, Fang J, Gillespie C, Odom E, King SC, Luncheon C, Ayala C. Awareness of Stroke Signs and Symptoms and Calling 9-1-1 among US Adults: National Health Interview Survey, 2009 and 2014. Preventing Chronic Disease. 2019 Jun; 16: E78. doi: 10.5888/pcd16.180564. DOI: https://doi.org/10.5888/pcd16.180564

Mahesh KA, Shashank H, Srikanth S, Thejas A. Prediction of Stroke Using Machine Learning. In Proceedings of the Conference Paper. 2020 Jun.

Lawrence MR and Wilson R. Awareness of Stroke Warning Signs, Risk Factors and Response to Stroke: A Hospital-Based Survey. 2023. doi: 10.18203/2320-6012.ijrms20232098. DOI: https://doi.org/10.18203/2320-6012.ijrms20232098

Writing Group Members, Lloyd-Jones D, Adams R, Carnethon M, De Simone G, Ferguson TB et al. Heart disease and stroke statistics—2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2009 Jan; 119(3): e21-181. doi: 10.1161/CIRCULATIONAHA.108.191261. DOI: https://doi.org/10.1161/CIRCULATIONAHA.108.191261

Sirsat MS, Fermé E, Câmara J. Machine Learning for Brain Stroke: A Review. Journal of Stroke and Cerebrovascular Diseases. 2020 Oct; 29(10): 105162. doi: 10.1016/j.jstrokecerebrovasdis.2020.105162. DOI: https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105162

Centers for Disease Control and Prevention, National Center for Health Statistics. Compressed mortality file 1968-1978. CDC WONDER Online Database, Compiled from Compressed Mortality File CMF 1968-1988, Series 20, No. 2A, 2000. 2015.

Ma Q, Li R, Wang L, Yin P, Wang Y, Yan C et al. Temporal Trend and Attributable Risk Factors of Stroke Burden in China, 1990–2019: An Analysis for the Global Burden of Disease Study 2019. The Lancet Public Health. 2021 Dec; 6(12): e897-906. doi: 10.1016/S2468-2667(21)00228-0. DOI: https://doi.org/10.1016/S2468-2667(21)00228-0

Wang N, Yang Y, Qiu B, Gao Y, Wang A, Xu Q et al. Correlation of the systemic immune-inflammation index with short-and long-term prognosis after acute ischemic stroke. Aging (Albany, NY). 2022 Aug; 14(16): 6567. doi: 10.18632/aging.204228. DOI: https://doi.org/10.18632/aging.204228

Murphy SJ and Werring DJ. Stroke: Causes and Clinical Features. Medicine. 2020 Sep; 48(9): 561-6. doi: 10.1016/j.mpmed.2020.06.002. DOI: https://doi.org/10.1016/j.mpmed.2020.06.002

Ramamoorthy V, Chan K, Roy M, Saxena A, Ahmed MA, Zhang Z et al. Healthcare Expenditure Trends among Adult Stroke Patients in the United States, 2011-2020. Journal of Stroke and Cerebrovascular Diseases. 2023 Oct; 32(10): 107333. doi: 10.1016/j.jstrokecerebrovasdis.2023.107333. DOI: https://doi.org/10.1016/j.jstrokecerebrovasdis.2023.107333

Lavallée PC, Charles H, Albers GW, Caplan LR, Donnan GA, Ferro JM et al. Underlying Causes of TIA and Minor Ischemic Stroke and Risk of Major Vascular Events. JAMA Neurology. 2023 Nov; 80(11): 1199-208.

Dantas LF, Marchesi JF, Peres IT, Hamacher S, Bozza FA, Quintano Neira RA. Public Hospitalizations For Stroke in Brazil from 2009 to 2016. PLoS One. 2019 Mar; 14(3): e0213837. doi: 10.1371/journal.pone.0213837. DOI: https://doi.org/10.1371/journal.pone.0213837

Feigin VL, Stark BA, Johnson CO, Roth GA, Bisignano C, Abady GG et al. Global, Regional, and National Burden of Stroke and Its Risk Factors, 1990–2019: A Systematic Analysis for the Global Burden of Disease Study 2019. The Lancet Neurology. 2021 Oct; 20(10): 795-820.

Owolabi MO, Thrift AG, Mahal A, Ishida M, Martins S, Johnson WD et al. Primary Stroke Prevention Worldwide: Translating Evidence into Action. The Lancet Public Health. 2022 Jan; 7(1): e74-85. doi: 10.1016/S2468-2667(21)00281-4. DOI: https://doi.org/10.1016/S2468-2667(21)00281-4

Grysiewicz RA, Thomas K, Pandey DK. Epidemiology of Ischemic and Hemorrhagic Stroke: Incidence, Prevalence, Mortality, and Risk Factors. Neurologic Clinics. 2008 Nov; 26(4): 871-95. doi: 10.1016/j.ncl.2008.07.003. DOI: https://doi.org/10.1016/j.ncl.2008.07.003

Boehme AK, Esenwa C, Elkind MS. Stroke Risk Factors, Genetics, and Prevention. Circulation Research. 2017 Feb; 120(3): 472-95. doi: 10.1161/CIRCRESAHA.116.308398. DOI: https://doi.org/10.1161/CIRCRESAHA.116.308398

Bushnell C, Kernan WN, Sharrief AZ, Chaturvedi S, Cole JW, Cornwell III WK et al. 2024 Guideline for the Primary Prevention of Stroke: A Guideline from the American Heart Association/American Stroke Association. Stroke. 2024 Dec; 55(12): e344-424. doi: 10.1161/STR.0000000000000475. DOI: https://doi.org/10.1161/STR.0000000000000475

Espanol A, Lerebours F, Calviere L, Bonneville F, Ducros A, Larrue V et al. Silent Brain Infarct in Migraine: Systematic Review and Meta-Analysis. Revue Neurologique. 2024 Jun; 180(6): 486-93. doi: 10.1016/j.neurol.2023.05.003. DOI: https://doi.org/10.1016/j.neurol.2023.05.003

Ali M, van Os HJ, van der Weerd N, Schoones JW, Heymans MW, Kruyt ND et al. Sex Differences in Presentation of Stroke: A Systematic Review and Meta-Analysis. Stroke. 2022 Feb; 53(2): 345-54. doi: 10.1161/STROKEAHA.120.034040. DOI: https://doi.org/10.1161/STROKEAHA.120.034040

Liu Q, Fang J, Cui C, Dong S, Gao L, Bao J et al. Association of Aortic Stiffness and Cognitive Decline: A Systematic Review and Meta-Analysis. Frontiers in Aging Neuroscience. 2021 Jun; 13: 680205. doi: 10.3389/fnagi.2021.680205. DOI: https://doi.org/10.3389/fnagi.2021.680205

Shen W, Fan X, Wang L, Zhang Y. Traditional Chinese Medicine for Post-Stroke Cognitive Impairment: A Systematic Review and Meta-Analysis. Frontiers in Pharmacology. 2022 Feb; 13: 816333. doi: 10.3389/fphar.2022.816333. DOI: https://doi.org/10.3389/fphar.2022.816333