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Background: Real world studies of silent brain infarction (SBI) and white matter disease (WMD) are impeded by challenges in cohort identification. Natural language processing (NLP) from imaging reports may facilitate future studies. However, electronic health records can be heterogeneous and the process of interpreting neuroimages and generating reports can vary. Understanding knowledge representation and relationships between neuroimages and imaging reports is crucial for using NLP to facilitate disease management and cohort identification. Methods: A balanced sample of head neuroimages (CT, MRI) of patients >50 years without clinical histories of symptomatic stroke, TIA, or dementia were obtained at Mayo Clinic and Tufts Medical Center. A team of 4 radiology residents performed report interpretation (RI) on 1000 reports according to a standardized protocol for the presence of SBIs, the presence of WMD, and WMD grade. A random subsample of 400 was doubly read for interrater reliability. For benchmarking, a team of 4 neuroradiologists directly reviewed and described findings on a subsample of 182 images, each doubly read. We assessed interrater reliability for direct review (DR) and RI, and agreement between these 2 information sources. An NLP algorithm was developed to review and extract findings from 1000 imaging reports. Results: For DR, interrater reliability was moderate for SBIs and WMD (k = 0.53, 95% CI 0.43-0.64 and k = 0.47, 95% CI 0.33-0.61) and good for WMD grade (Spearman 0.71, p<0.001). For RI, interrater reliability for SBIs, WMD and WMD grade was good (k = 0.88, 95% CI 0.80-0.97; k = 0.98, 95% CI 0.97-1.00; and Spearman = 0.985, p<0.001, respectively). Agreement between DR and RI was good for SBIs (k = 0.77, 95% CI 0.67-0.86) and WMD (k = 0.65, 95% CI 0.54-0.77). Spearman rank correlations comparing WMD grade interpretation DR to RI was 0.60 (p<0.001). In identifying the presence of SBIs and WMDs, the accuracy of the NLP algorithm was 0.991 and 0.994, respectively. Conclusion: For the presence of SBI and WMD, and WMD grade, agreement between RI and DR was similar to agreement between 2 neuroradiologists directly reviewing neuroimages. It is highly feasible to use NLP to identify patients with SBIs and WMDs for clinical studies.

Background: White matter disease (WMD) and silent brain infarction (SBI) are known to be risk markers for stroke. Nevertheless, the predictive value of these changes when seen incidentally on routinely-obtained neuroimages is unknown. Methods: In this retrospective cohort study, Kaiser Permanente-Southern California health plan enrollees aged ≥ 50 years old with a brain CT or MRI scan between 2009-2019 and without a prior history of ischemic stroke, transient ischemic attack, or dementia were identified. Natural language processing (NLP) was used to identify patients with SBI and WMD on the index neuroimaging report. We used Cox proportional hazards to estimate the risk of future ischemic stroke associated with the presence of SBI and of WMD, controlling for major stroke risk factors. Results: Among 262,875 individuals receiving brain neuroimaging, 13,154 (5.0%) and 78,330 (29.8%) had SBI and WMD, respectively. The Table below summarizes the crude stroke incidence rates. The crude hazard ratio (HR) was 3.40 (95% CI 3.25-3.56) for SBI and 2.63 (95% CI 2.54-2.71) for WMD. In the multivariable model controlling for all major stroke risk factors, the effect of SBI was found to be stronger in younger versus older patients and for MRI- versus CT-discovered lesions. With MRI, the average adjusted HR over time was 2.95 (95% CI 2.53-3.44) for those < age 65 and 2.15 (95% CI 1.91-2.41) for those ≥ age 65. With CT scan, the average adjusted HR over time was 2.48 (95%CI 2.19-2.81) for those < age 65 and 1.81 (95% CI 1.71-1.91) for those ≥ age 65. The adjusted HR associated with a finding of WMD was 1.76 (95% CI 1.69-1.82) and was not modified by age or imaging modality. The effect of SBI decreased gradually over time, while the effect of WMD remained constant. Conclusion: Incidentally-discovered SBI and WMD are common in patients ≥ age 50 and are associated with substantial increases in the risk of subsequent symptomatic stroke. The findings may represent an opportunity for stroke prevention.

Background: White matter disease (WMD) and silent brain infarction (SBI) are known to be risk markers for dementia. Nevertheless, the effects of incidentally-discovered WMD and SBI on routinely-obtained neuroimages is unknown. Methods: In the retrospective cohort study, Kaiser Permanente-Southern California (KPSC) health plan enrollees aged ≥ 50 years old with a brain cross-sectional image (CT or MRI) between 2009-2019 and without a prior history of ischemic stroke, transient ischemic attack, or dementia were identified. Natural language processing (NLP) was used to identify patients with SBI and WMD on the first qualifying neuroimaging report. Cox proportional hazards regression model was applied to estimate the effects of WMD and SBI on dementia, controlling for major cardiovascular risk factors. Results: Among 262,875 individuals receiving brain neuroimaging, 13,154 (5.0%) and 78,330 (29.8%) had SBI and WMD, respectively. The table below summarizes the crude dementia incidence rates. The crude hazard ratio (HR) was 3.69 (95% CI 3.58-3.80) for WMD and 2.80 (95% CI 2.68-2.93) for SBI. In the multivariable model controlling for all major cardiovascular risk factors, the effect of WMD on dementia risk was found to be stronger in younger compared to older patients and for CT- compared to MRI-discovered lesions. With CT, the adjusted HR was 3.08 (95% CI 2.84-3.34) for those < age 70 and 1.90 (95% CI 1.83-1.97) for those ≥ age 70. With MRI, the adjusted HR was 2.58 (95% CI 2.31-2.87) for those < age 70 and 1.59 (95% CI 1.45-1.75) for those ≥ age 70. The adjusted HR associated with a finding of SBI was 2.01 (95% CI 1.78-2.32) for patients age <70 and 1.26 (95% CI 1.20-1.32) for patients age ≥70, and was not modified by imaging modality. Conclusion: Incidentally-discovered WMD and SBI are common in patients ≥ age 50 and are associated with substantial increase in the risk of subsequent dementia. The findings may represent an opportunity for dementia prevention.

Introduction: Silent brain infarcts (SBIs) and white matter disease (WMD) are highly prevalent and associated with increased risk of ischemic stroke in patients with traditional stroke risk factors (RFs) in prospective cohort studies. Their frequency and associations with stroke RFs have not been well described in real world populations. Methods: This was a cross-sectional study of patients age ≥ 50 in the Kaiser Permanente-Southern California (KPSC) health system between 2009-2019 with a head CT or MRI for non-stroke indications and no history of ischemic stroke, transient ischemic attack, or dementia. A natural language processing (NLP) algorithm developed at Mayo Clinic and Tufts Medical Center was applied to the KPSC EHR to identify individuals with reported SBIs or WMD. Multivariable Poisson regression with robust error variance was used to estimate risk ratios of demographics, stroke RFs (from the Framingham Stroke Risk Score), and scan modality on the presence of SBIs or WMD. Results: Among 262,875 individuals, the NLP identified 13,154 (5.0%) with SBIs and 78,330 (29.8%) with WMD. Stroke RFs were highly prevalent in this cohort. The majority underwent CTs (74.8%) instead of MRIs as their initial neuroimaging. After adjustment for demographics and RFs, advanced age demonstrated a strong association with increased risk of SBIs and WMD (table). MRI was associated with a reduced risk of reported SBIs (ARR: 0.87, 95% CI 0.83-0.91) and an increased risk of reported WMD (ARR 2.86, 95% CI 2.83-2.90). Despite being prevalent, traditional stroke RFs had weak associations with increased risk of SBIs or increased risk of WMD. Conclusions: Advanced age is strongly associated with incidentally discovered SBIs and WMD on neuroimaging studies obtained in routine care. The development of SBIs and WMD may not be fully attributable to traditional stroke RFs.

Introduction Globally, the use of telestroke programmes for acute care is expanding. Currently, a standardised set of variables for enabling reliable international comparisons of telestroke programmes does not exist. The aim of the study was to establish a consensus-based, minimum dataset for acute telestroke to enable the reliable comparison of programmes, clinical management and patient outcomes. Methods An initial scoping review of variables was conducted, supplemented by reaching out to colleagues leading some of these programmes in different countries. An international expert panel of clinicians, researchers and managers ( n = 20) from the Australasia Pacific region, USA, UK and Europe was convened. A modified-Delphi technique was used to achieve consensus via online questionnaires, teleconferences and email. Results Overall, 533 variables were initially identified and harmonised into 159 variables for the expert panel to review. The final dataset included 110 variables covering three themes (service configuration, consultations, patient information) and 12 categories: (1) details about telestroke network/programme ( n = 12), (2) details about initiating hospital ( n = 10), (3) telestroke consultation ( n = 17), (4) patient characteristics ( n = 7), (5) presentation to hospital ( n = 5), (6) general clinical care within first 24 hours ( n = 10), (7) thrombolysis treatment ( n = 10), (8) endovascular treatment ( n = 13), (9) neurosurgery treatment ( n = 8), (10) processes of care beyond 24 hours ( n = 7), (11) discharge information ( n = 5), (12) post-discharge and follow-up data ( n = 6). Discussion The acute telestroke minimum dataset provides a recommended set of variables to systematically evaluate acute telestroke programmes in different countries. Adoption is recommended for new and existing services.

AbstractTo address the pending public health crisis due to Alzheimer's disease (AD) and related neurodegenerative disorders, the Marian S. Ware Alzheimer Program at the University of Pennsylvania held a meeting entitled “State of the Science Conference on the Advancement of Alzheimer's Diagnosis, Treatment and Care,” on June 21‐22, 2012. The meeting comprised four workgroups focusing on Biomarkers; Clinical Care and Health Services Research; Drug Development; and Health Economics, Policy, and Ethics. The workgroups shared, discussed, and compiled an integrated set of priorities, recommendations, and action plans, which are presented in this article.