Current status of stroke in Qatar: Including data from the BRAINS study

A pilot study comparing the rehabilitation functional outcomes of post-COVID-19 stroke and non-COVID stroke patients: An occupational therapy perspective

Background and purpose: Recent studies have highlighted the clinical characteristics and incidence of post-COVID-19 stroke conditions. Comparing the function and overall prognosis of stroke patients and post-COVID-19 stroke patients is an intriguing idea. Therefore, the aim of this study was to examine and compare the functional outcomes between the two groups from an occupational therapy perspective. Methods: Forty patients admitted to a rehabilitation facility were included, 20 of whom were diagnosed with post-COVID-19 stroke and 20 with non-COVID-19 stroke (ischemic and hemorrhagic). The study was a mixed design consisting of both prospective and retrospective data collection. Existing data from electronic medical records were used for the retrospective dataset. The retrospective dataset only consisted of data from post-COVID-19 stroke patients. The prospective dataset consisted of data from non-COVID-19 stroke patients. Data were collected at the time of admission and at discharge. Outcome measures included the functional independence measure (FIM), the Action Research Arm Test (ARAT), the post-COVID-19 functional status (PCFS) scale, the Borg rating of perceived exertion, and the mini-mental state examination (MMSE). Results: Both the post-COVID-19 stroke and non-COVID stroke groups showed significant differences before and after rehabilitation (NIHSS (National Institutes of Health Stroke Scale): p = 0.014, 0.000, FIM: p = 0.000, 0.000, MMSE: p = 0.015, 0.000, ARAT: p = 0.000, 0.000, respectively). However, the mean difference in the non-COVID-19 stroke group was higher than that in the post-COVID-19 stroke group, particularly in MMSE, FIM, and NIHSS scores (NIHSS: 2.8 ± 0.4, 0.9 ± 0.04, FIM: 34.8 ± 5.03, 32.95 ± 0.81, MMSE: 5.05 ± 3.5, 0.7 ± 1.17, ARAT: 1 ± 0.062, 1.2 ± 0.47, respectively). It was also found that in the post-COVID-19 stroke group, age had a positive influence on NIHSS (p = 0.022) and FIM (p = 0.047), and impaired side affected the NIHSS scores (p = 0.007). In the non-COVID-19 stroke group, significant correlations were found between the NIHSS and FIM scores (r = -0.445, p = 0.050) and the NIHSS and ARAT scores (r = -0.529, p = 0.017). Conclusion: Higher mean differences in the non-COVID-19 stroke group than in the post-COVID-19 group could be due to additional COVID-19 complications in the stroke condition itself. Overall functional gain was observed in both groups due to the effective rehabilitation. Therefore, rehabilitation is critical for functional optimization in such vulnerable populations. There is an urgent need to consider post-pandemic rehabilitation aspects.

Machine learning-based prediction of one-year mortality in ischemic stroke patients

BACKGROUND

Accurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness of machine learning models in predicting one-year mortality after an ischemic stroke.

METHODS

Five machine learning models were trained using data from a national stroke registry, with logistic regression demonstrating the highest performance. The SHapley Additive exPlanations (SHAP) analysis explained the model's outcomes and defined the influential predictive factors.

RESULTS

Analyzing 8183 ischemic stroke patients, logistic regression achieved 83% accuracy, 0.89 AUC, and an F1 score of 0.83. Significant predictors included stroke severity, pre-stroke functional status, age, hospital-acquired pneumonia, ischemic stroke subtype, tobacco use, and co-existing diabetes mellitus (DM).

DISCUSSION

The model highlights the importance of predicting mortality in enhancing personalized stroke care. Apart from pneumonia, all predictors can serve the early prediction of mortality risk which supports the initiation of early preventive measures and in setting realistic expectations of disease outcomes for all stakeholders. The identified tobacco paradox warrants further investigation.

CONCLUSION

This study offers a promising tool for early prediction of stroke mortality and for advancing personalized stroke care. It emphasizes the need for prospective studies to validate these findings in diverse clinical settings.

Stroke in Qatar: a decade of insights from a national registry

BACKGROUND

Stroke is a major cause of death and disability worldwide and presents a significant burden on healthcare systems. This retrospective study aims to analyze the characteristics and outcomes of stroke patients admitted to Hamad General Hospital (HGH) stroke service in Qatar from January 2014 to July 2022.

METHODS

The medical records of 15,859 patients admitted during the study period were analyzed. The data collected included patient demographics, stroke types, admission location, procedures performed, mortality rates, and other clinical characteristics.

RESULTS

Of the total cohort, 70.9% were diagnosed with a stroke, and 29.1% were diagnosed with stroke mimics. Of the stroke patients, 85.3% had an ischemic stroke, and 14.7% had a hemorrhagic stroke. Male patients below 65 years old (80.2%) and of South Asian ethnicity (44.6%) were the most affected. The mortality rate was 4.6%, significantly higher for hemorrhagic stroke than ischemic stroke (12.6% vs. 3.2%). Female patients had a higher stroke-related mortality rate than male patients (6.8% vs. 4%). The thrombolysis rate was 9.5%, and the thrombectomy rate was 3.4% of the ischemic stroke cohort. The mean door-to-needle time for thrombolysis was 61.2 minutes, and the mean door-to-groin time for thrombectomy was 170 minutes. Stroke outcomes were good, with 59.3% of patients having favorable outcomes upon discharge (mRS ≤2), which improved to 68.2% 90 days after discharge.

CONCLUSION

This study provides valuable insights into stroke characteristics and outcomes in Qatar. The findings suggest that stroke mortality rates are low, and favorable long-term disability outcomes are achievable. However, the study identified a higher stroke-related mortality rate among female patients and areas for improvement in thrombolysis and thrombectomy time.

Mass spectrometry-based proteomic profiling of extracellular vesicle proteins in diabetic and non-diabetic ischemic stroke patients: a case-control study

Acute ischemic stroke is the most common cause of neurologic dysfunction caused by focal brain ischemia and tissue injury. Diabetes is a major risk factor of stroke, exacerbating disease management and prognosis. Therefore, discovering new diagnostic markers and therapeutic targets is critical for stroke prevention and treatment. Extracellular vesicles (EVs), with their distinctive properties, have emerged as promising candidates for biomarker discovery and therapeutic application. This case-control study utilized mass spectrometry-based proteomics to compare EVs from non-diabetic stroke (nDS = 14), diabetic stroke (DS = 13), and healthy control (HC = 12) subjects. Among 1288 identified proteins, 387 were statistically compared. Statistical comparisons using a general linear model (log2 foldchange ≥0.58 and FDR-p≤0.05) were performed for nDS vs HC, DS vs HC, and DS vs nDS. DS vs HC and DS vs nDS comparisons produced 123 and 149 differentially expressed proteins, respectively. Fibrinogen gamma chain (FIBG), Fibrinogen beta chain (FIBB), Tetratricopeptide repeat protein 16 (TTC16), Proline rich 14-like (PR14L), Inhibitor of nuclear factor kappa-B kinase subunit epsilon (IKKE), Biorientation of chromosomes in cell division protein 1-like 1 (BD1L1), and protein PR14L exhibited significant differences in the DS group. The pathway analysis revealed that the complement system pathways were activated, and blood coagulation and neuroprotection were inhibited in the DS group (z-score ≥2; p ≤ 0.05). These findings underscore the potential of EVs proteomics in identifying biomarkers for stroke management and prevention, warranting further clinical investigation.

Machine learning-based prognostication of mortality in stroke patients

Objectives

Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke.

Materials and methods

Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors.

Results

The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes.

Conclusions

This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.

Incidence, clinical features, and outcomes of posterior circulation ischemic stroke: insights from a large multiethnic stroke database

Background

Posterior cerebral circulation ischemic stroke (PCS) comprises up to 25% of all strokes. It is characterized by variable presentation, leading to misdiagnosis and morbidity and mortality. We aim to describe PCS in large multiethnic cohorts.

Methods

A retrospective review of a large national stroke database from its inception on the 1st of January 2014 till 31 December 2020. Incidence per 100,000 adult population/year, demographics, clinical features, stroke location, and outcomes were retrieved. We divided the cohort into patients from MENA (Middle East and North Africa) and others.

Results

In total, 1,571 patients were identified. The incidence of PCS was observed to be rising and ranged from 6.3 to 13.2/100,000 adult population over the study period. Men were 82.4% of the total. The mean age was 54.9 ± 12.7 years (median 54 years, IQR 46, 63). MENA patients comprised 616 (39.2%) while others were 954 (60.7%); of these, the majority (80.5%) were from South Asia. Vascular risk factors were prevalent with 1,230 (78.3%) having hypertension, 970 (61.7%) with diabetes, and 872 (55.5%) having dyslipidemia. Weakness (944, 58.8%), dizziness (801, 50.5%), and slurred speech (584, 36.2%) were the most commonly presenting symptoms. The mean National Institute of Health Stroke Score (NIHSS) score was 3.8 ± 4.6 (median 3, IQR 1, 5). The overall most frequent stroke location was the distal location (568, 36.2%). The non-MENA cohort was younger, less vascularly burdened, and had more frequent proximal stroke location (p < 0.05). Dependency or death at discharge was seen in 39.5% and was associated with increasing age, and proximal and multilocation involvement; while at 90 days it was 27.4% and was associated with age, male sex, and having a MENA nationality (p < 0.05).

Conclusion

In a multiethnic cohort of posterior circulation stroke patients from the MENA region and South Asia, we noted a rising incidence over time, high prevalence of vascular risk factors, and poor outcomes in older men from the MENA region. We also uncovered considerable disparities between the MENA and non-MENA groups in stroke location and outcome. These disparities are crucial factors to consider when tailoring individualized patient care plans. Further research is needed to thoroughly investigate the underlying reasons for these variations.

Association of single nucleotide polymorphisms with dyslipidemia and risk of metabolic disorders in the State of Qatar

BACKGROUND

Dyslipidemia is recognized as one of the risk factors of cardiovascular diseases (CVDs), type 2 diabetes mellitus (T2DM), and non-alcoholic fatty liver disease (NAFLD).

OBJECTIVE

The study aimed to investigate the association between selected single nucleotide polymorphisms (SNPs) with dyslipidemia and increased susceptibility risks of CVD, NAFLD, and/or T2DM in dyslipidemia patients in comparison with healthy control individuals from the Qatar genome project.

METHODS

A community-based cross-sectional study was conducted among 2933 adults (859 dyslipidemia patients and 2074 healthy control individuals) from April to December 2021 to investigate the association between 331 selected SNPs with dyslipidemia and increased susceptibility risks of CVD, NAFLD and/or T2DM, and covariates.

RESULTS

The genotypic frequencies of six SNPs were found to be significantly different in dyslipidemia patients subjects compared to the control group among males and females. In males, three SNPs were found to be significant, the rs11172113 in over-dominant model, the rs646776 in recessive and over-dominant models, and the rs1111875 in dominant model. On the other hand, two SNPs were found to be significant in females, including rs2954029 in recessive model, and rs1801251 in dominant and recessive models. The rs17514846 SNP was found for dominant and over-dominant models among males and only the dominant model for females. We found that the six SNPs linked to gender type had an influence in relation to disease susceptibility. When controlling for the four covariates (gender, obesity, hypertension, and diabetes), the difference between dyslipidemia and the control group remained significant for the six variants. Finally, males were three times more likely to have dyslipidemia in comparison with females, hypertension was two times more likely to be present in the dyslipidemia group, and diabetes was six times more likely to be in the dyslipidemia group.

CONCLUSION

The current investigation provides evidence of association for a common SNP to coronary heart disease and suggests a sex-dependent effect and encourage potential therapeutic applications.

Trend analysis of cardiovascular disease mortality, incidence, and mortality-to-incidence ratio: results from global burden of disease study 2017

Background

Cardiovascular diseases (CVDs) are one of the global leading causes of concern due to the rising prevalence and consequence of mortality and disability with a heavy economic burden. The objective of the current study was to analyze the trend in CVD incidence, mortality, and mortality-to-incidence ratio (MIR) across the world over 28 years.

Methods

The age-standardized CVD mortality and incidence rates were retrieved from the Global Burden of Disease (GBD) Study 2017 for both genders and different world super regions with available data every year during the period 1990–2017. Additionally, the Human Development Index was sourced from the United Nations Development Programme (UNDP) database for all countries at the same time interval. The marginal modeling approach was implemented to evaluate the mean trend of CVD incidence, mortality, and MIR for 195 countries and separately for developing and developed countries and also clarify the relationship between the indices and Human Development Index (HDI) from 1990 to 2017.

Results

The obtained estimates identified that the global mean trend of CVD incidence had an ascending trend until 1996 followed by a descending trend after this year. Nearly all of the countries experienced a significant declining mortality trend from 1990 to 2017. Likewise, the global mean MIR rate had a significant trivial decrement trend with a gentle slope of 0.004 over the time interval. As such, the reduction in incidence and mortality rates for developed countries was significantly faster than developing counterparts in the period 1990–2017 (p < 0.05). Nevertheless, the developing nations had a more rather shallow decrease in MIR compared to developed ones.

Conclusions

Generally, the findings of this study revealed that there was an overall downward trend in CVD incidence and mortality rates, while the survival rate of CVD patients was rather stable. These results send a satisfactory message that global effort for controlling the CVD burden was quite successful. Nonetheless, there is an urgent need for more efforts to improve the survival rate of patients and lower the burden of this disease in some areas with an increasing trend of either incidence or mortality.

Age and sex affect deep learning prediction of cardiometabolic risk factors from retinal images

Deep neural networks can extract clinical information, such as diabetic retinopathy status and individual characteristics (e.g. age and sex), from retinal images. Here, we report the first study to train deep learning models with retinal images from 3,000 Qatari citizens participating in the Qatar Biobank study. We investigated whether fundus images can predict cardiometabolic risk factors, such as age, sex, blood pressure, smoking status, glycaemic status, total lipid panel, sex steroid hormones and bioimpedance measurements. Additionally, the role of age and sex as mediating factors when predicting cardiometabolic risk factors from fundus images was studied. Predictions at person-level were made by combining information of an optic disc centred and a macula centred image of both eyes with deep learning models using the MobileNet-V2 architecture. An accurate prediction was obtained for age (mean absolute error (MAE): 2.78 years) and sex (area under the curve: 0.97), while an acceptable performance was achieved for systolic blood pressure (MAE: 8.96 mmHg), diastolic blood pressure (MAE: 6.84 mmHg), Haemoglobin A1c (MAE: 0.61%), relative fat mass (MAE: 5.68 units) and testosterone (MAE: 3.76 nmol/L). We discovered that age and sex were mediating factors when predicting cardiometabolic risk factors from fundus images. We have found that deep learning models indirectly predict sex when trained for testosterone. For blood pressure, Haemoglobin A1c and relative fat mass an influence of age and sex was observed. However, achieved performance cannot be fully explained by the influence of age and sex. In conclusion we confirm that age and sex can be predicted reliably from a fundus image and that unique information is stored in the retina that relates to blood pressure, Haemoglobin A1c and relative fat mass. Future research should focus on stratification when predicting person characteristics from a fundus image.