Case Report: Taxifolin for neurosurgery-associated early-onset cerebral amyloid angiopathy

Cases of iatrogenic cerebral amyloid angiopathy (CAA) have been increasingly reported recently, particularly those associated with neurosurgery. Preclinical studies have shown taxifolin to be promising for treating CAA. We describe a young 42-year-old man with a history of childhood traumatic brain injury that required a craniotomy for hematoma evacuation. He later presented with recurrent lobar intracerebral hemorrhage (ICH) decades later, which was histologically confirmed to be CAA. Serial 11C-Pittsburgh compound B positron emission tomography (11C-PiB-PET) imaging showed a 24% decrease in global standardized uptake value ratio (SUVR) at 10 months after taxifolin use. During this period, the patient experienced clinical improvement with improved consciousness and reduced recurrent ICH frequency, which may be partly attributable to the potential amyloid-β (Aβ) clearing the effect of taxifolin. However, this effect seemed to have diminished at 15 months, CAA should be considered in young patients presenting with recurrent lobar ICH with a history of childhood neurosurgery, and serial 11C-PiB-PET scans warrant further validation as a strategy for monitoring treatment response in CAA for candidate Aβ-clearing therapeutic agents such as taxifolin.

Efficacy and safety of cilostazol in decreasing progression of cerebral white matter hyperintensities-A randomized controlled trial

Introduction

Cerebral small vessel disease (SVD) is an important cause of dementia that lacks effective treatment. We evaluated the efficacy and safety of cilostazol, an antiplatelet agent with potential neurovascular protective effects, in slowing the progression of white matter hyperintensities (WMHs) in stroke- and dementia-free subjects harboring confluent WMH on magnetic resonance imaging (MRI).

Methods

In this single-center, randomized, double-blind, placebo-controlled study, we randomized stroke- and dementia-free subjects with confluent WMHs to receive cilostazol or placebo for 2 years in a 1:1 ratio. The primary outcome was change in WMH volume over 2 years. Secondary outcomes were changes in brain volumes, lacunes, cerebral microbleeds, perivascular space, and alterations in white matter microstructural integrity, cognition, motor function, and mood.

Results

We recruited 120 subjects from October 27, 2014, to January 21, 2019. A total of 55 subjects in the cilostazol group and 54 subjects in the control group were included for intention-to-treat analysis. At 2-year follow-up, the changes in WMH volume were not statistically different between cilostazol treatment and placebo (0.3±1.0 mL vs -0.1±0.8 mL, p = 0.167). Secondary outcomes, bleeding and vascular events, were also not statistically different between the two groups.

Discussion

In this trial with stroke- and dementia-free subjects with confluent WMHs, cilostazol did not impact WMH progression but demonstrated an acceptable safety profile. Future studies should address the treatment effects of cilostazol on subjects at different clinical stages of SVD.

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study

BACKGROUND

There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs alone, which is the most common method of non-invasive imaging the retina to detect Alzheimer's disease-dementia.

METHODS

In this retrospective, multicentre case-control study, we trained, validated, and tested a deep learning algorithm to detect Alzheimer's disease-dementia from retinal photographs using retrospectively collected data from 11 studies that recruited patients with Alzheimer's disease-dementia and people without disease from different countries. Our main aim was to develop a bilateral model to detect Alzheimer's disease-dementia from retinal photographs alone. We designed and internally validated the bilateral deep learning model using retinal photographs from six studies. We used the EfficientNet-b2 network as the backbone of the model to extract features from the images. Integrated features from four retinal photographs (optic nerve head-centred and macula-centred fields from both eyes) for each individual were used to develop supervised deep learning models and equip the network with unsupervised domain adaptation technique, to address dataset discrepancy between the different studies. We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid β burden (testing the deep learning model between amyloid β positive vs amyloid β negative).

FINDINGS

12 949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia. In the testing datasets, the bilateral deep learning model had accuracies ranging from 79·6% (SD 15·5) to 92·1% (11·4) and AUROCs ranging from 0·73 (SD 0·24) to 0·91 (0·10). In the datasets with data on PET, the model was able to differentiate between participants who were amyloid β positive and those who were amyloid β negative: accuracies ranged from 80·6 (SD 13·4%) to 89·3 (13·7%) and AUROC ranged from 0·68 (SD 0·24) to 0·86 (0·16). In subgroup analyses, the discriminative performance of the model was improved in patients with eye disease (accuracy 89·6% [SD 12·5%]) versus those without eye disease (71·7% [11·6%]) and patients with diabetes (81·9% [SD 20·3%]) versus those without the disease (72·4% [11·7%]).

INTERPRETATION

A retinal photograph-based deep learning algorithm can detect Alzheimer's disease with good accuracy, showing its potential for screening Alzheimer's disease in a community setting.

FUNDING

BrightFocus Foundation.

Can transcranial Doppler ultrasound be used for screening cerebral small vessel diseases in the community?

OBJECTIVES

Elderly persons harbouring severe white matter hyperintensity (WMH), a radiological manifestation of cerebral small vessel disease (SVD), have an increased risk of dementia, stroke and poor functional outcomes. A simple screening tool will enhance their recruitment into preventive trials for SVD. We explored the clinical utility of the pulsatility index (PI) of the middle cerebral artery (MCA), obtained from transcranial Doppler ultrasound (TCD), in identifying severe WMH among community elderly persons with vascular risk factors.

METHODS

Three hundred and thirty-one dementia- and stroke-free community elderly subjects with hypertension and/or diabetes mellitus underwent TCD to obtain the MCA PI. The WMH volume on 3.0 Tesla MRI was quantified and normalized to each subject's brain volume. The normalized WMH volumes were classified as low (<14.5 ml, 1 standard deviation [SD] above the mean, 84th percentile) or high (≥14.5 ml). The severity of WMH was also rated visually with the Fazekas score. Logistic regression and receiver-operator characteristics (ROC) analysis were performed to evaluate the association between the MCA PI and the severity of WMH.

RESULTS

The MCA PI was not an independent predictor of severe WMH. An MCA PI ≥1.095 detected high normalized WMH volumes with an area under the curve (AUC) of 0.553 (95% CI 0.473-0.633), sensitivity of 0.556, and specificity of 0.523. ROC analysis of the MCA PI in predicting high Fazekas scores yielded similar findings.

CONCLUSION

In stroke- and dementia-free elderly persons with vascular risk factors, the MCA PI was unable to identify severe WMH. (Word count: 260).

Converting MMSE to MoCA and MoCA 5-minute protocol in an educationally heterogeneous sample with stroke or transient ischemic attack

BACKGROUND

The Montreal Cognitive Assessment (MoCA) is psychometrically superior over the Mini-mental State Examination (MMSE) for cognitive screening in stroke or transient ischemic attack (TIA). It is free for clinical and research use. The objective of this study is to convert scores from the MMSE to MoCA and MoCA-5-minute protocol (MoCA-5 min) and to examine the ability of the converted scores in detecting cognitive impairment after stroke or TIA.

METHODS

A total of 904 patients were randomly divided into training (n = 623) and validation (n = 281) samples matched for demography and cognition. MMSE scores were converted to MoCA and MoCA-5 min using (1) equipercentile method with log-linear smoothing and (2) Poisson regression adjusting for age and education. Receiver operating characteristics curve analysis was used to examine the ability of the converted scores in differentiating patients with cognitive impairment.

RESULTS

The mean education was 5.8 (SD = 4.6; ranged 0-20) years. The entire spectrum of MMSE scores was converted to MoCA and MoCA-5 min using equipercentile method. Relationship between MMSE and MoCA scores was confounded by age and education, and a conversion equation with adjustment for age and education was derived. In the validation sample, the converted scores differentiated cognitively impaired patients with area under receiver operating characteristics curve 0.826 to 0.859.

CONCLUSION

We provided 2 methods to convert scores from the MMSE to MoCA and MoCA-5 min based on a large sample of patients with stroke or TIA having a wide range of education and cognitive levels. The converted scores differentiated patients with cognitive impairment after stroke or TIA with high accuracy.

An expedited stroke triage pathway: the key to shortening the door-to-needle time in delivery of thrombolysis

OBJECTIVES

To assess time management of stroke thrombolysis triage and functional outcomes in patients receiving recombinant tissue plasminogen activator for hyperacute stroke, and identify bottlenecks in delivery of the treatment.

DESIGN

Prospective study.

SETTING

A university teaching hospital in Hong Kong.

PATIENTS

Patients with suspected hyperacute stroke referred to the stroke thrombolysis team during October 2008 to September 2009.

MAIN OUTCOME MEASURES

Time performance records including door-to-stroke team, door-to-needle, and onset-to-thrombolysis times. Functional outcomes by modified Rankin Scale score at 3 months, and thrombolysis-related complications including haemorrhagic transformations and mortality.

RESULTS

During the 12-month period, 95 thrombolysis calls were received; recombinant tissue plasminogen activator was given intravenously to 17 (18%) of the patients and intra-arterially to 11 (12%). The mean (standard deviation) door-to-stroke team and the door-to-needle times for intravenous recombinant tissue plasminogen activator patients were 33 (25) and 80 (25) minutes, respectively; both were about 20 minutes longer than that recommended by the National Institute of Neurological Disorders and Stroke. The mean National Institute of Health Stroke Scale score for patients received intravenous recombinant tissue plasminogen activator was 16 (standard deviation, 7). The mean (standard deviation) onset-to-treatment time was 144 (42) minutes. Nine (53%) patients who received intravenous recombinant tissue plasminogen activator achieved favourable outcomes at 3 months, with a modified Rankin Scale score of 0 to 1. Symptomatic haemorrhage and mortality occurred in one (6%) patient.

CONCLUSION

A dedicated stroke triage pathway is essential to ensure efficient and safe delivery of thrombolysis therapy. Improvements in door-to-stroke team time through integration with emergency medicine staff and neuroradiologists may improve thrombolysis eligibility.