Stroke, or cerebrovascular accident (CVA), is defined by the World Health Organization (WHO) as rapidly developing clinical signs of focal or global disturbance of cerebral function lasting more than 24 hours or leading to death, with no apparent cause other than vascular origin [1]. It is the second leading cause of death and a major cause of long-term disability worldwide [2]. Ischemic strokes account for 50–85% of all strokes globally [3]. In India, stroke is the fourth most common cause of death and the fifth most common cause of disability; the incidence ranges from 108–172 per 100,000 persons per year, with an estimated 619,000 stroke-related deaths annually and 28.5 million DALYs lost [4]. The five leading risk factors globally are hypertension (55.5%), high BMI (24.3%), hyperglycaemia (20.2%), air pollution (20.1%), and smoking (17.6%) [5]. NCCT brain is the initial investigation of choice in suspected stroke, enabling rapid differentiation of ischaemic from haemorrhagic stroke and exclusion of mimics such as tumours and abscess [3]. MRI with diffusion-weighted imaging (DWI) is more sensitive for hyperacute ischaemia and identifies TIA-related infarction more reliably. CT and MR angiography are performed when endovascular intervention is planned. The clinical picture, combined with neuroimaging, forms the cornerstone of stroke management. This study was undertaken to define the clinico-radiological profile of acute ischemic stroke patients presenting to a tertiary care hospital, with attention to risk factors, symptomatology, and neuroimaging findings, to inform local stroke management and prevention strategies. Objectives • To study the clinical profile of patients presenting with acute ischemic stroke. • To characterise the radiological (CT/MRI) findings in patients with ischemic stroke. • To assess modifiable and non-modifiable risk factors associated with ischemic stroke.

Study Design and Setting A hospital-based cross-sectional study was conducted in the Department of General Medicine, Basaveshwara Medical College and Hospital (BMCH), Chitradurga, Karnataka, India, from March 2021 to August 2022 (18 months). The study was approved by the Institutional Ethics Review Committee (Ref: BMC&H/IEC/2020–2021/113). Sample Size Sample size was calculated using N = 4pq/d², where p = 0.152, q = 0.848, d = 0.1. The minimum required sample was 50; with a 10% margin for attrition, a final sample of 60 patients was enrolled. Inclusion and Exclusion Criteria Inclusion: All patients ≥20 years with clinical signs and symptoms of stroke and neuroimaging-confirmed ischemic stroke. Exclusion: Haemorrhagic stroke, Todd’s palsy, head injury, infective or metastatic aetiology, cortical venous thrombosis, and recurrent stroke. Data Collection and Investigations Informed written consent was obtained from all patients or their attendants. A standardised proforma recorded demographics, risk factor history (hypertension, diabetes mellitus, dyslipidaemia, coronary artery disease, atrial fibrillation, smoking, alcohol, prior CVA, COVID-19), mode of onset, and presenting symptoms. Detailed general and neurological examination was performed in all patients. Investigations included CBC, random blood glucose, fasting lipid profile, renal and liver function tests, urine routine, ECG, 2D echocardiography, carotid Doppler ultrasonography, and brain neuroimaging (NCCT brain and/or MRI with DWI, FLAIR, and MR angiography as clinically indicated). Statistical Analysis Categorical variables are presented as frequencies and percentages. Continuous variables are expressed as mean ± SD. Associations were tested using Chi-square or Fisher’s exact test. p < 0.05 was considered statistically significant. Analysis was performed using IBM SPSS Statistics v25.

Age and Sex Distribution The mean age was 59.75 ± 13.69 years; the predominant age group was 51–60 years (25%), followed by 61–70 years (23.3%). Stroke in the young (≤40 years) accounted for 11.7% (Table 1). Males constituted 70% (M:F = 2.3:1). Table 1. Age-wise distribution of study subjects. Age Group (years) Cases (n) Percentage (%) ≤40 7 11.7 41–50 11 18.3 51–60 15 25.0 61–70 14 23.3 71–80 7 11.7 >80 6 10.0 Total 60 100 Risk Factors Hypertension was the most prevalent modifiable risk factor (75%), followed by diabetes mellitus (68.3%), coronary artery disease (21.7%), smoking (13.3%), alcohol use (11.7%), prior CVA (8.3%), atrial fibrillation (5%), and dyslipidaemia (5%) (Table 2). Table 2. Distribution of modifiable risk factors. Risk Factor Cases (n) Percentage (%) Hypertension 45 75.0 Diabetes mellitus 41 68.3 Coronary artery disease 13 21.7 Smoking 8 13.3 Alcohol 7 11.7 Prior CVA 5 8.3 Atrial fibrillation 3 5.0 Dyslipidaemia 3 5.0 Clinical Presentation Onset was insidious in 55% and sudden in 45% of patients. Motor weakness was the most common feature (76.7%), with hemiparesis in 58.3% and hemiplegia in 15%. Right-sided weakness was more frequent (46.7% vs 26.7% left-sided). Speech disturbance occurred in 60%, altered sensorium in 56.7%, vomiting in 33.3%, headache in 26.7%, giddiness in 18.3%, seizures in 11.7%, visual symptoms in 6.7%, and isolated sensory symptoms in 3.3%. Cranial nerve involvement was present in 23.3% of patients; the facial nerve (CN VII) was most frequently affected (42.85% of cranial nerve cases), followed by CN IX and CN X (21.42% each) and CN III (14.28%). Neuroimaging: Vessel Territory and Regional Distribution On neuroimaging, MCA territory was the most commonly involved (81.7%), followed by ACA (28.3%) and PCA (21.7%) territories (Table 3). Cortical strokes constituted 78.3% of all infarcts, with parietal lobe involvement in 50%, frontal lobe in 33.3%, and internal capsule in 20%. Cerebellar involvement was seen in 16.7%, and posterior fossa structures (medulla, pons, midbrain) in 8.4%. Table 3. Vessel territory involvement on neuroimaging. Vessel Territory Cases (n) Percentage (%) MCA (Middle Cerebral Artery) 49 81.7 ACA (Anterior Cerebral Artery) 17 28.3 PCA (Posterior Cerebral Artery) 13 21.7 Ancillary Investigations ECG was abnormal in 35%: ischaemic changes (23.3%), LVH (11.7%), and AF (5%). 2D echocardiography revealed abnormalities in 46.7%: ischaemic changes (23.3%), LVH (15%), and valvular heart disease (8.3%). Carotid intimal thickness was increased in 80% of patients. Dyslipidaemia on fasting lipid profile was found in 71.7%. A history of prior COVID-19 infection was noted in 11.7%. Representative Neuroimaging The following figures illustrate representative CT and MRI neuroimaging findings from the study cohort, corresponding to the major vessel territories and imaging modalities employed.

Age and Sex The mean age of stroke onset (59.75 ± 13.69 years) is consistent with Naik et al. (58.27 ± 16.02 years) [6] and Kaur et al. (60.46 ± 14.84 years) [4], confirming the sixth decade as the peak risk period. Stroke in the young (≤40 years) was noted in 11.7%, which carries significant public health implications due to prolonged disability and loss of productive years. Male predominance (M:F = 2.3:1) is consistent with higher rates of smoking, alcohol use, and occupational stress in men [7]. Risk Factors Hypertension was the leading modifiable risk factor (75%), consistent with global data [5] and higher than figures reported by Kaur et al. (52.5%) [4]. The co-prevalence of diabetes (68.3%) is particularly notable, given that diabetes independently raises ischemic stroke risk 1.8–6-fold through promotion of atherosclerosis and metabolic syndrome [8]. Carotid intimal thickness was increased in 80% of patients, underscoring the centrality of large-artery atherosclerosis in stroke pathogenesis in this cohort. Dyslipidaemia on fasting lipid profile (71.7%) was higher than the clinically diagnosed rate (5%), suggesting under-diagnosis at admission and the importance of routine lipid screening in all stroke patients. Clinical Presentation Motor weakness (76.7%) was the dominant presentation, comparable to 69.2% in Kaur et al. [4] and 72.2% in Swain et al. [3]. The high prevalence of speech disturbance (60%) reflects the predominance of dominant hemisphere (left MCA territory) infarcts in this cohort. Altered sensorium (56.7%) likely reflects referral bias toward severe strokes in a tertiary care setting. Cranial nerve involvement in 23.3%, predominantly facial nerve (CN VII), is consistent with the high frequency of MCA territory cortical infarcts. Neuroimaging MCA territory predominance (81.7%) is higher than in Naik et al. (77%) [6] and Kaur et al. (60.2%) [4], consistent with the MCA supplying the largest cerebral territory and being the most common destination for cardioembolic and carotid emboli. Representative neuroimaging (Figures 1–7) illustrates the spectrum of ischaemic findings encountered: subtle early NCCT changes with sulcal effacement (Fig. 1), large MCA territory infarcts with mass effect (Fig. 2), acute DWI-positive MCA territory infarcts (Fig. 3), PCA territory cerebellar and posterior fossa ischaemia (Figs. 4–6), and MR angiographic evidence of posterior communicating artery absence (Fig. 7). The MR angiography findings underscore the importance of vascular imaging in identifying targets for endovascular intervention. Echocardiographic abnormalities in 46.7% of patients and AF in 5% highlight the cardioembolic contribution to stroke burden, supporting routine cardiac evaluation in all ischaemic stroke patients

Ischemic stroke in this South Indian tertiary care population predominantly affects men in the sixth decade. Hypertension and diabetes mellitus are the dominant modifiable risk factors, present in three-quarters and two-thirds of patients respectively. MCA territory infarction is most prevalent, and cortical strokes outnumber deep or posterior fossa strokes. NCCT brain remains the cornerstone of initial evaluation, with MRI DWI providing superior sensitivity for hyperacute and posterior fossa ischaemia. Systematic carotid Doppler, echocardiography, and lipid profiling are essential to characterise stroke aetiology and guide secondary prevention. Early clinical diagnosis with timely neuroimaging-guided management is critical to reducing stroke-related mortality and disability. Conflicts of Interest The authors declare no conflicts of interest. Acknowledgements The authors thank the patients and their attendants who participated in this study, the nursing and radiology staff of BMCH Chitradurga, Dr. Shanshad Begum for statistical guidance, and the entire Department of General Medicine faculty for their support.

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