Neuroimaging Findings and Their Prognostic Value in Acute Ischaemic Stroke Patients at University of Maiduguri Teaching Hospital (UMTH), Borno State, Nigeria
Main Article Content
Keywords
Acute ischemic stroke, Neuroimaging, Mortality, Risk stratification, Prognosis, Nigeria
Abstract
Background: Accurate prediction of stroke outcomes in resource-limited settings remains challenging. This study assessed the utility of neuroimaging findings in predicting mortality among acute ischaemic stroke patients at the University of Maiduguri Teaching Hospital, Nigeria.
Methodology: This prospective study enrolled 171 consecutive adults with acute ischaemic stroke between January and December 2023. All patients underwent non-contrast brain CT scanning, with infarct volume calculated using standardized measurements. The primary outcome was 30-day mortality. Multivariate logistic regression analysis identified independent predictors of mortality, which were used to develop a risk stratification system.
Results: Large infarct volume (>100,000 mm³) emerged as the strongest independent predictor of mortality (aOR 6.82, 95% CI 2.05-22.68, p=0.002), followed by multiple territory involvement (aOR 3.42, 95% CI 1.43-8.17, p=0.006). The developed risk score demonstrated good discriminative ability (AUC 0.775, 95% CI 0.689-0.860) and stratified patients into three risk categories with mortality rates of 8.2% (low), 11.8% (intermediate), and 42.0% (high) (p<0.001).
Conclusion: Specific neuroimaging parameters can effectively predict early mortality in acute ischaemic stroke. The developed risk stratification tool could improve patient care in resource-limited settings.
References
2. Adeloye D, Ezejimofor M, Auta A, Mpazanje RG, Ezeigwe N, Ngige EN, et al. Estimating morbidity due to stroke in Nigeria: a systematic review and meta-analysis. Journal of the Neurological Sciences. 2019;402:136-44.
3. Feigin VL, Forouzanfar MH, Krishnamurthi R, Mensah GA, Connor M, Bennett DA, et al. Global and regional burden of stroke during 1990-2010: findings from the Global Burden of Disease Study 2010. Lancet. 2014;383(9913):245-54.
4. Alkali NH, Bwala SA, Akano AO, Osi-Ogbu O, Alabi P, Ayeni OA. Stroke risk factors, subtypes, and 30-day case fatality in Abuja, Nigeria. Niger Med J. 2013;54(2):129-35.
5. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, et al. Guidelines for the Early Management of Patients with Acute Ischaemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischaemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke 2019;50(12):e344-e418.
6. Chalela JA, Kidwell CS, Nentwich LM, Luby M, Butman JA, Demchuk AM, et al. Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. Lancet 2007;369(9558):293-8.
7. Saver JL, Johnston KC, Homer D, Wityk R, Koroshetz W, Truskowski LL, et al. Infarct Volume as a Surrogate or Auxiliary Outcome Measure in Ischaemic Stroke Clinical Trials. Stroke. 1999;30(2):293-8.
8. Vogt G, Laage R, Shuaib A, Schneider A. Initial Lesion Volume Is an Independent Predictor of Clinical Stroke Outcome at Day 90. Stroke. 2012;43(5):1266-72.
9. Meng X, Ji J. Infarct volume and outcome of cerebral ischaemia, a systematic review and meta-analysis. International Journal of Clinical Practice. 2021;75(11):e14773.
10. Watila MM, Nyandaiti YW, Ahidjo A, Balarabe SA, Ibrahim A, Bakki B, et al. Effect of Admission Hyperglycaemia on Infarct Size and Clinical Outcome in Black Patients with Acute Ischaemic Stroke, Northeast Nigeria. Journal of Advances in Medicine and Medical Research. 2014;4(34):5324-34.
11. Saeed J, Samir GM, El-Said AF. Association of stroke severity, leukocytosis, and infarction size with early neurological deterioration in acute ischaemic stroke. The Egyptian Journal of Internal Medicine. 2019;31(4):774-8.
12. Bawiskar N, Kumar S, Acharya S, Kothari N, Gemnani RR. Association of Serum Calcium With Infarct Size and Severity in Acute Ischaemic Stroke: A Rural Hospital-Based Cross-Sectional Study. Cureus. 2023;15(8):e43015.
13. Boehme AK, Siegler JE, Mullen MT, Albright KC, Lyerly MJ, Monlezun DJ, et al. Racial and gender differences in stroke severity, outcomes, and treatment in patients with acute ischaemic stroke. J Stroke Cerebrovasc Dis. 2014;23(4):e255-61.
14. Riley RD, Ensor J, Snell KIE, Harrell FE, Martin GP, Reitsma JB, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441.
15. Fekadu G, Chelkeba L, Melaku T, Tegene E, Kebede A. 30-day and 60-day rates and predictors of mortality among adult stroke patients: Prospective cohort study. Ann Med Surg (Lond). 2020;53:1-11.
16. R Core Team. _R: A Language and Environment for Statistical Computing_. Vienna, Austria: R Foundation for Statistical Computing, 2023.
17. Abdalkader M, Siegler JE, Lee JS, Yaghi S, Qiu Z, Huo X, et al. Neuroimaging of Acute Ischaemic Stroke: Multimodal Imaging Approach for Acute Endovascular Therapy. J Stroke. 2023;25(1):55-71.
18. Ospel JM, Hill MD, Menon BK, Demchuk A, McTaggart R, Nogueira R, et al. Strength of Association between Infarct Volume and Clinical Outcome Depends on the Magnitude of Infarct Size: Results from the ESCAPE-NA1 Trial. AJNR Am J Neuroradiol. 2021;42(8):1375-9.
19. Feigin VL, Brainin M, Norrving B, Martins S, Sacco RL, Hacke W, et al. World Stroke Organization (WSO): Global Stroke Fact Sheet 2022. Int J Stroke. 2022;17(1):18-29.
20. Fan H, Wei L, Zhao X, Zhu Z, Lu W, Roshani R, et al. White matter hyperintensity burden and functional outcomes in acute ischaemic stroke patients after mechanical thrombectomy: A systematic review and meta-analysis. NeuroImage: Clinical. 2024;41:103549.
21. Ghoneem A, Osborne MT, Abohashem S, Naddaf N, Patrich T, Dar T, et al. Association of Socioeconomic Status and Infarct Volume with Functional Outcome in Patients with Ischaemic Stroke. JAMA Netw Open. 2022;5(4):e229178.
22. Mittal SH, Goel D. Mortality in ischaemic stroke score: A predictive score of mortality for acute ischaemic stroke. Brain Circ. 2017;3(1):29-34.
23. Someeh N, Mirfeizi M, Asghari-Jafarabadi M, Alinia S, Farzipoor F, Shamshirgaran SM. Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study. Scientific Reports. 2023;13(1):18530.
24. Khandare P, Saluja A, Solanki RS, Singh R, Vani K, Garg D, et al. Serum S100B and NSE Levels Correlate With Infarct Size and Bladder-Bowel Involvement Among Acute Ischaemic Stroke Patients. J Neurosci Rural Pract. 2022;13(2):218-25.