Practical one-dimensional measurements of age-related brain atrophy are validated by 3-dimensional values and clinical outcomes: a retrospective study

Atrial fibrillation and preexisting cognitive impairment in ischemic stroke patients: Dijon Stroke Registry

BACKGROUND

Atrial Fibrillation (AF) is a common cause of ischemic stroke (IS), and is associated with cognitive impairment in the general population. We aimed to compare the prevalence of preexisting cognitive impairment between IS patients with and without AF, and to assess whether prior brain damage could contribute to the observed differences.

METHODS

Patients with acute IS were prospectively identified from the population-based Dijon Stroke Registry, France. Patients who had a CT-scan as brain imaging modality were included in this analysis to assess the presence of preexisting leukoaraiosis, old vascular brain lesions, and cerebral atrophy. Characteristics of patients including prior-to-stroke cognitive status (normal cognition, mild cognitive impairment (MCI), or dementia) were compared between those with and without AF.

RESULTS

Among 916 IS patients, 288 (31.4 %) had AF, of whom 88 had newly diagnosed AF. AF patients had more frequent prior MCI (17.8 % versus 10.2 %) or dementia (22.4 % versus 13.1 %) (p = 0.001), vascular risk factors, and preexisting brain damage. In unadjusted model, preexisting cognitive impairment was associated with AF (OR=2.24; 95 % CI: 1.49-3.37, p < 0.001 for MCI; OR=2.20; 95 % CI: 1.52-3.18, p < 0.001 for dementia). After adjustment for clinical and imaging variables, preexisting mild cognitive impairment (OR=1.87; 95 % CI: 1.06-3.32, p = 0.032) and dementia (OR=1.98; 95 % CI: 1.15-3.40, p = 0.013) were independently associated with AF.

CONCLUSION

AF is a common condition in IS patients and is associated with preexisting cognitive impairment. Brain lesions visible on imaging did not seem to fully account for this association that may involve other mechanisms yet to be elucidated.

Long-Term Survival of Ischemic Stroke Patients according to Prior Cognitive Status: Dijon Stroke Registry

INTRODUCTION

Understanding the influence of preexisting cognitive impairment on the poststroke outcome is a critical challenge in the context of current aging and growing population. This study aimed to assess long-term survival of patients with acute ischemic stroke (IS) according to their premorbid cognitive status and to identify contributing factors of death.

METHODS

Patients with IS were prospectively identified among residents of Dijon, France, using a population-based registry (2013-2017). The association between case fatality at 5 years and prestroke cognitive status was assessed by multivariable Cox models adjusted for other clinical characteristics and preexisting brain damage on the initial CT scan including leukoaraiosis, old vascular brain lesions, and cortical and central brain atrophy, as well as major arterial occlusion.

RESULTS

1,049 patients were included (mean age ± SD: 76.3 ± 15.2 years old, 54% women). Case fatality rates at 5 years were 38.1% in patients without cognitive impairment, 65.9% in patients with prior mild cognitive impairment (MCI, n = 132, 12.6%), and 86.6% in patients with dementia (n = 164, 15.6%) (p < 0.001). MCI (HR = 1.39; 95% CI: 1.06-1.81, p = 0.016) and dementia (HR = 1.89; 95% CI: 1.45-2.46, p < 0.001) were both independently associated with higher case fatality after adjustment for clinical variables. The association remained significant after further adjustment for preexisting brain damage and major arterial occlusion (HR = 1.47; 95% CI: 1.10-1.98, p = 0.009, for MCI and HR = 1.90; 95% CI: 1.43-2.53, p < 0.001, for dementia) among patients with available data on the CT scan (n = 916). Factors associated with death were roughly similar across groups.

CONCLUSION

This study highlighted a poor long-term survival of IS patients with preexisting cognitive impairment, independently of other contributing factors of death. It is critical to better understand the trajectory of IS patients with preexisting cognitive impairment and to identify prognostic markers to guide clinicians in their management strategies.

Pre-existing brain damage and association between severity and prior cognitive impairment in ischemic stroke patients

BACKGROUND

We evaluated whether pre-existing brain damage may explain greater severity in cognitively-impaired patients with ischemic stroke (IS).

METHODS

IS patients were retrieved from the population-based registry of Dijon, France. Pre-existing damage (leukoaraiosis, old vascular brain lesions, cortical and central brain atrophy) was assessed on initial CT-scan. Association between prestroke cognitive status defined as no impairment, mild cognitive impairment (MCI), or dementia, and clinical severity at IS onset assessed with the NIHSS score was evaluated using ordinal regression analysis. Mediation analysis was performed to assess pre-existing brain lesions as mediators of the relationship between cognitive status and severity.

RESULTS

Among the 916 included patients (mean age 76.8 ± 15.0 years, 54.3% women), those with pre-existing MCI (n = 115, median NIHSS [IQR]: 6 [2-15]) or dementia (n = 147, median NIHSS: 6 [3-15]) had a greater severity than patients without (n = 654, median NIHSS: 3 [1-9]) in univariate analysis (OR=1.69; 95% CI: 1.18-2.42, p = 0.004, and OR=2.06; 95% CI: 1.49-2.84, p < 0.001, respectively). Old cortical lesion (OR=1.53, p = 0.002), central atrophy (OR=1.41, p = 0.005), cortical atrophy (OR=1.90, p < 0.001) and moderate (OR=1.41, p = 0.005) or severe (OR=1.84, p = 0.002) leukoaraiosis were also associated with greater severity. After adjustments, pre-existing MCI (OR=1.52; 95% CI: 1.03-2.26, p = 0.037) or dementia (OR=1.94; 95% CI: 1.32-2.86, p = 0.001) remained associated with higher severity at IS onset, independently of confounding factors including imaging variables. Association between cognitive impairment and severity was not mediated by pre-existing visible brain damages.

CONCLUSION

Impaired brain ischemic tolerance in IS patients with prior cognitive impairment could involve other mechanisms than pre-existing visible brain damage.

Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using Deep Learning Models

Objective

Brain atrophy is an important imaging characteristic of cerebral small vascular disease (CSVD). Our study explores the linear measurement application on CT images of CSVD patients and develops a fully automatic brain atrophy classification model. The second aim was to compare it with the end-to-end Convolutional Neural Networks (CNNs) model.

Methods

A total of 385 subjects such as 107 no-atrophy brain, 185 mild atrophy, and 93 severe atrophy were collected and randomly separated into training set (n = 308) and test set (n = 77). Key slices for linear measurement were manually identified and used to annotate nine linear measurements and a binary classification of cerebral sulci widening. A linear-measurement-based pipeline (2D model) was constructed for two-types (existence/non-existence brain atrophy) or three-types classification (no/mild atrophy/severe atrophy). For comparison, an end-to-end CNN model (3D-deep learning model) for brain atrophy classification was also developed. Furthermore, age and gender were integrated to the 2D and 3D models. The sensitivity, specificity, accuracy, average F1 score, receiver operating characteristics (ROC) curves for two-type classification and weighed kappa for three-type classification of the two models were compared.

Results

Automated measurement of linear measurements and cerebral sulci widening achieved moderate to almost perfect agreement with manual annotation. In two-type atrophy classification, area under the curves (AUCs) of the 2D model and 3D model were 0.953 and 0.941 with no significant difference (p = 0.250). The Weighted kappa of the 2D model and 3D model were 0.727 and 0.607 according to standard classification they displayed, mild atrophy and severe atrophy, respectively. Applying patient age and gender information improved classification performances of both 2D and 3D models in two-type and three-type classification of brain atrophy.

Conclusion

We provide a model composed of different modules that can classify CSVD-related brain atrophy on CT images automatically, using linear measurement. It has similar performance and better interpretability than the end-to-end CNNs model and may prove advantageous in the clinical setting.

Brain Atrophy and Leukoaraiosis Correlate with Futile Stroke Thrombectomy

INTRODUCTION

Although mechanical thrombectomy (MT) is a proven therapy for acute large vessel occlusion strokes, futile recanalization in the elderly is common and costly. Strategies to minimize futile recanalization may reduce unnecessary thrombectomy transfers and procedures. We evaluated whether a simple and rapid visual assessment of brain atrophy and leukoaraiosis on a plain head CT correlates with futile stroke recanalization in the elderly.

METHODS

Consecutive stroke patients admitted for thrombectomy, older than 65 years of age, all with TICI 2b/3 recanalization rates were retrospectively studied from multiple comprehensive stroke centers. Brain atrophy and leukoaraiosis were visually analyzed from pre-intervention plain head CTs using a simplified scheme based on validated scales. Baseline demographics were collected and the primary outcome measure was 90-day modified Rankin score (mRS). Cochran-Armitage trend test was applied in analyzing the association of the severity of brain atrophy and leukoaraiosis with 90-day mRS.

RESULTS

Between 2017 and 2019, 175 patients > 65 years who underwent thrombectomy with TICI 2b/3 recanalization from two comprehensive stroke centers were evaluated. The median age was 77 years. IV-tPA was given in 59% of patients, average initial NIHSS was 19, average baseline mRS was 0.77 and median time to recanalization was 300 minutes. Age and severity of atrophy/leukoaraiosis was categorized into three groups of increasing severity and associated with 90 day mRS 0-3 rates of 62%, 49% and 41% (p=0.037) respectively.

CONCLUSIONS

A simplified, visual assessment of the degree of brain atrophy and leukoaraiosis measured on plain head CT correlates with futile recanalization in patients age >65 years. Although additional validation is needed, these findings suggest that brain atrophy and leukoaraiosis may have value as a surrogate marker of prestroke functional status. In doing so, simplified visual plain head CT grading scales may minimize elderly futile recanalization.

High Blood Lead Levels: An Increased Risk for Development of Brain Hyperintensities among Type 2 Diabetes Mellitus Patients

The current study was aimed to ascertain the effect of blood lead level on brain tissues in patients with type 2 diabetes. A total of 300 human participants ages 27 to 60 years with type 2 diabetes (n = 150) and healthy individuals (n = 150) were included in this study. The serum samples were used for measuring HbA1c and fasting blood glucose. Blood lead level was measured using flame atomic absorption spectrophotometer. Magnetic resonance imaging sub-analysis was used to assess the brain hyperintensities. Brain hyperintensities were found in 55% of patients with diabetes and 6% of non-diabetic control group subjects. The deep white matter hyperintensities were observed in 45% of diabetic patients, while the subcortical hyperintensities were noted in 10% of cases. Entorhinal cortex changes (31%) and hippocampus changes (42%) were noted in diabetic patients with brain hyperintensities. Diabetic patients with brain hyperintensities showed higher blood lead levels, HbA1c, and fasting blood sugar (p < 0.0001) as compared with healthy volunteers. A higher correlation (R² = 0.8922) was found between deep white matter hyperintensities' size and blood lead levels. In nutshell, persistence of high blood lead level in diabetic patients may progress to brain hyperintensities which may consequently lead to cognitive, behavioral changes and Alzheimer's disease.

Novel multi-linear quantitative brain volume formula for manual radiological evaluation of brain atrophy

Objectives

The brain atrophy commonly occurs in elderly and in some childhood conditions making the techniques for quantifying brain volume needful. Since the automated quantitative methods of brain volume assessment have limited availability in developing countries, it was the purpose of this study to design and test an alternative formula that is applicable to all healthcare levels.

Methods

The multi-linear diagonal brain fraction formula (DBF) was designed from dimensions of brain relative to skull and ventricles. To test a developed formula, a total of 347 subjects aged between 0 and 18 years who had brain CT scans performed recruited and subjected to a systematic measurement of their brains in a diagonal brain fashion.

Results

Out of 347 patients evaluated, 62 subjects (17.8 %) were found to be cases of brain atrophy. The three radiological measurements which included sulcal width (SW), ventricular width (VW) and Evans Index (EI) were concurrently performed. SW and VW showed good age correlation. Similar tests were extended to diagonal brain fraction (DBF) and skull vertical horizontal ratio (VHR) in which DBF showed significant age correlation.

Conclusions

The DBF formula shows significant ability of differentiating changes of brain volume suggesting that it can be utilized as an alternative brain fraction quantification method bearing technical simplicity in assessing gross brain volume.

Advances in knowledge

The designed formula is unique in that it captures even the possible asymmetrical volume loss of brain through diagonal lines. The proposed scores being in term of ratios give four grades of brain atrophy.