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• In focal and unknown epilepsy, patients are recommended some form of brain imaging. • MRI the gold standard of epilepsy imaging is expensive and unavailable to many. • A CT scan may be enough in many PWE obviating the need for an MRI. • When a high burden of epilepsy is due to calcified lesions, NCCT and CECT are comparable. Brain imaging is needed when investigating epilepsy. Imaging options available include MRI and CT scan which may be non-contrast (NCCT) or contrast-enhanced (CECT). The specific clinical question and probable epilepsy substrate in the epidemiological context and socioeconomic milieu are important in determining the choice of imaging. In patients with well-controlled focal or unknown epilepsy who are unlikely to be surgical candidates, is CECT essential or can NCCT be an acceptable choice? A prospective observational study was conducted at a tertiary care centre in India. Consecutive patients with focal or unknown epilepsy who were relatively well-controlled on medical treatment underwent NCCT followed by CECT brain. Three neuroradiologists independently reported the images. Proportion of abnormalities missed on NCCT and picked only on CECT were determined. How often abnormalities picked on CECT changed patient management was also analysed. Two hundred and nineteen patients with focal (87 %) or unknown (13 %) epilepsy underwent NCCT followed by CECT brain. Most had epilepsy for >3 months and an annual seizure frequency of 2–10 seizures. There was a nearly perfect inter-observer agreement between 3 neuroradiologists in reporting the NCCT and CECT as 'normal' or 'abnormal' with kappa (κ) values of 0.9 and 1.0 respectively. The sensitivity of NCCT compared to CECT in detecting an abnormality was 97 % (CI 92.6 - 99.5 %) and the specificity was 99 % (CI 94.9 - 99.9 %). There was no significant difference in the proportion of NCCTs and CECTs found abnormal (50.22 % vs 51.14 %, p = 0.91). A solitary calcified granuloma was the most common abnormality reported on NCCT as well as CECT, 21.0 % and 19.1 % respectively. New findings picked on CECT alone, did not change management in any patient. When imaging focal or unknown epilepsy, an NCCT performs as well as a CECT, especially in regions where calcified lesions contribute a significant etiological burden. The role of imaging in epilepsy varies between patients and a universal recommendation of an MRI or a CECT in all patients is neither cost-efficient nor evidence-based. In drug responsive focal or unknown epilepsy of longstanding duration, CT scans are either normal or have calcified lesions that are easily picked on NCCT. [ABSTRACT FROM AUTHOR]

Background: Diabetes is an important modifiable risk factor for dementia. The prevalence of mild cognitive impairment (MCI) in type 2 diabetes (T2D) is high (You et al, 2021). This data is largely derived using screening tools (MMSE or MoCA), with little information on MCI subtypes. Evidence from the South Asian region is particularly scarce, where prevalence of diabetes is high, and is seen a decade earlier compared to Caucasians. Method: We have established a cohort of elders (³ 60 years of age), with and without diabetes, visiting a tertiary care hospital in North India (from 2019, ongoing). The participants underwent an interview to collect information on their demographic profile, risk associations, vascular health, and other comorbidities. Culturally validated neuropsychological battery was used for cognitive evaluation. MoCA was done in a subset of patients. Result: We recruited 840 participants in the cohort, 683 with T2DM and 157 without T2DM (controls). The mean age of the cohort was 65.3±4.5 years, 65.2% were males. Risk profile is described in Table 1. The cognitive z scores of participants with T2DM ranged from ‐0.25 to +0.44. The worst affected domain was attention, working memory and executive functions (Table 2). Using an actuarial definition of MCI (Bondi MW et al, 2014), the prevalence in participants with T2D was 29.7%. 17.3% had amnestic impairment, 14.8% dysexecutive, 14.2% language, 2.4% visuo‐perceptual and 9.1% multidomain impairment. The cognitive raw scores (by dementia risk stratification) are provided in Table 3. On logistic regression, lower education 3.45(2.09,5.68), depression 2.04(1.26,3.29), poor sleep quality 1.77(1.15,2.71), family income 1.70(1.22,2.38), followed by age 1.05(1.00,1.10) had a significant association with global low cognition. Conclusion: Roughly one‐third of elders with T2D visiting tertiary care services in North India have mild cognitive impairment. Amnestic subtype is the most frequent. Attention, working memory and executive functions are worst affected. Enhancing the cognitive reserve, mood and sleep quality may be important targets in prevention of cognitive decline in the elderly population with T2D. [ABSTRACT FROM AUTHOR]

Background: The major burden of dementia will be falling on Low and middle‐income countries in the next few decades. One of the major strategies to counter the rise in dementia burden is primary prevention, especially in at‐risk elderly individuals. Method: Strategic Multimodal Intervention in at‐risk Elderly Indians for Prevention of Dementia (SMRUTHI INDIA) is designed as a Cohort Multiple Randomised Controlled Trial (cmRCT) under a prospective community‐based cohort in rural India with long‐term follow‐up over many years. The rural cohort will be established in four zones of India, under the Indian Council of Medical Research (ICMR), Model Rural Health Research Units (MRHRUs) at Himachal Pradesh (North), Tripura (East), Rajasthan (West) and Karnataka (South). We plan to recruit 10000 participants with age > = 55 years and CAIDE score > = 6. SMRUTHI INDIA will be established in 2 phases. Phase 1 involves establishing a cohort (phase 1a) and development, followed by validation of the multimodal care bundle (phase 1 b). The care bundle preparation will have a development phase, acceptability and adaptability phase followed by feasibility testing involving a pilot RCT in a single centre. Phase 2 involves testing multiple interventions in cmRCT, and the first intervention to be tested will be the multimodal care bundle. The primary outcome will be the incidence of all‐cause dementia. Result: SMRUTHI INDIA will establish in parallel a large prospective rural cohort of at‐risk elderly in India and develop a culturally adapted and context‐specific multimodal care bundle. At the end of Phase 1, we will have the results of the pilot RCT on the feasibility of multimodal care bundle. Conclusion: SMRUTHI INDIA is a landmark cohort multiple randomised controlled trial (cmRCT) which will assess multiple interventions for the primary prevention of dementia for the first time in India. Funding: Department of Health Research, Indian Council of Medical Research (ICMR) [ABSTRACT FROM AUTHOR]

Identifying ischemic or hemorrhagic strokes clinically may help in situations where neuroimaging is unavailable to provide primary-care prior to referring to stroke-ready facility. Stroke classification-based solely on clinical scores faces two unresolved issues. One pertains to overestimation of score performance, while other is biased performance due to class-imbalance inherent in stroke datasets. After correcting the issues using Machine Learning theory, we quantitatively compared existing scores to study the capabilities of clinical attributes for stroke classification. We systematically searched PubMed, ERIC, ScienceDirect, and IEEE-Xplore from 2001 to 2021 for studies that validated the Siriraj, Guys Hospital/Allen, Greek, and Besson scores for stroke classification. From included studies we extracted the reported cross-tabulation to identify and correct the above listed issues for an accurate comparative analysis of the performance of clinical scores. A total of 21 studies were included. Comparative analysis demonstrates Siriraj Score outperforms others. For Siriraj Score the reported sensitivity range (Ischemic Stroke-diagnosis) 43-97% (Median = 78% [IQR 65-88%]) is significantly higher than our calculated range 40–90% (Median = 70% [IQR 57–73%]), also the reported sensitivity range (Hemorrhagic Stroke-diagnosis) 50–95% (Median = 71% [IQR 64–82%]) is higher than our calculated range 34–86% (Median = 59% [IQR 50–79%]) which indicates overestimation of performance by the included studies. Guys Hospital/Allen and Greek Scores show similar trends. Recommended weighted-accuracy metric provides better estimate of the performance. We demonstrate that clinical attributes have a potential for stroke classification, however the performance of all scores varies across demographics, indicating the need to fine-tune scores for different demographics. To improve this variability, we suggest creating global data pool with statistically significant attributes. Machine Learning classifiers trained over such dataset may perform better and generalise at scale. [ABSTRACT FROM AUTHOR]