MyRisk_Stroke Calculator: A Personalized Stroke Risk Assessment Tool for the General Population

The Evaluation of Content Relevance and Representativeness of the New Stroke Risk Screening Scales

Stroke is a leading cause of death and disability worldwide. Early and comprehensive risk identification is essential for identifying individuals at high risk for stroke. This study aimed to evaluate each question in the new Stroke Risk Screening Scales (SRSS) and assess the domains for content relevance and representativeness. Initially, six stroke experts were invited to evaluate the SRSS questions. The content validity index (CVI), including the item-CVI (I-CVI) and the average-CVI (Ave-CVI), was then calculated. In our study, the acceptable standards for I-CVI and Ave-CVI were ≥0.78 and ≥0.9, respectively. The results showed that all invited experts accepted the invitation and evaluated the SRSS questions. The previous version of the SRSS consisted of 33 questions. Of these, 30 questions reached an I-CVI of ≥0.78, indicating good content validity. Three questions had an I-CVI of 0.67 and were considered invalid; thus, they were deleted. The overall instrument achieved an Ave-CVI of 0.95. Comprehensive SRSS are essential for effective stroke prevention planning. By facilitating the early identification of individuals at high risk for stroke, these scales help reduce the incidence and impact of stroke. The high content validity found in this study supports the reliability of the SRSS as a screening tool. In the future, implementing such validated scales in clinical practice can improve early intervention strategies, ultimately enhancing health outcomes and optimizing the use of healthcare resources.

A Risk Calculator to Predict Suicide Attempts Among Individuals With Early-Onset Bipolar Disorder

Objectives

To build a one-year risk calculator (RC) to predict individualized risk for suicide attempt in early-onset bipolar disorder.

Methods

Youth numbering 394 with bipolar disorder who completed ≥2 follow-up assessments (median follow-up length = 13.1 years) in the longitudinal Course and Outcome of Bipolar Youth (COBY) study were included. Suicide attempt over follow-up was assessed via the A-LIFE Self-Injurious/Suicidal Behavior scale. Predictors from the literature on suicidal behavior in bipolar disorder that are readily assessed in clinical practice were selected and trichotomized as appropriate (presence past 6 months/lifetime history only/no lifetime history). The RC was trained via boosted multinomial classification trees; predictions were calibrated via Platt scaling. Half of the sample was used to train, and the other half to independently test the RC.

Results

There were 249 suicide attempts among 106 individuals. Ten predictors accounted for >90% of the cross-validated relative influence in the model (AUC = 0.82; in order of relative influence): (1) age of mood disorder onset; (2) non-suicidal self-injurious behavior (trichotomized); (3) current age; (4) psychosis (trichotomized); (5) socioeconomic status; (6) most severe depressive symptoms in past 6 months (trichotomized none/subthreshold/threshold); (7) history of suicide attempt (trichotomized); (8) family history of suicidal behavior; (9) substance use disorder (trichotomized); (10) lifetime history of physical/sexual abuse. For all trichotomized variables, presence in the past 6 months reliably predicted higher risk than lifetime history.

Conclusions

This RC holds promise as a clinical and research tool for prospective identification of individualized high-risk periods for suicide attempt in early-onset bipolar disorder.Reprinted from Bipolar Disord 2022; 24:749-757, with permission from John Wiley and Sons. Copyright © 2022.

Incidence of stroke and its predictors among hypertensive patients in Felege Hiwot comprehensive specialized hospital, Bahir Dar, Ethiopia, a retrospective follow-up study

BACKGROUND

Globally, one in three adults has hypertension, a condition that causes 51% of all deaths from stroke. Stroke is becoming a major public health problem and the most common cause of morbidity and mortality among non-communicable diseases in the world and Ethiopia. Therefore, this study assesses the incidence of stroke and its predictors among hypertensive patients in Felege Hiwot Comprehensive Specialized Hospital, Bahir Dar, Ethiopia 2021.

METHODS

A hospital-based retrospective follow-up study design was used, simple random sampling technique was used to select 583 hypertensive patients that had follow-up registration between January 2018 and December 30th, 2020. Data were entered into Epi-data version 3.1 and exported to STATA version 14. The adjusted hazard ratio for each predictor with a 95% confidence interval was calculated using the Cox proportional hazards regression model, and a P-value ≤  0.05 was used to denote statistical significance.

RESULTS

From 583 hypertensive patients 106(18.18%) [95% CI 15-20] were developed stroke. The overall incidence rate was 1 per 100 person-years (95% CI 0.79-1.19). Comorbidities (Adjusted hazard ratio(AHR): 1.88, 95% CI 1.0-3.5), stage two hypertension (AHR = 5.21, 95%CI 2.75-9.8), uncontrolled systolic blood pressure (AHR: 2, 95% CI 1.21-354), uncontrolled diastolic blood pressure (AHR:1.9, 95% CI 1.1-3.57), alcohol consumption (AHR = 2.04, 95%CI 1.2-3.49), age 45-65 (AHR = 10.25, 95%CI 7.47-11.1); and drug discontinuation (AHR = 2.05,95% CI 1.26-3.35) were independent predictors for the incidence of stroke among hypertensive patients.

CONCLUSION

The incidence of stroke among hypertensive patients was high and various modifiable and non-modifiable risk factors highly contributed to its incidence. This study recommends early screening of blood pressure, giving priority to comorbid patients and patients with advanced stage hypertension, and giving health education about behavioral risks and drug adherence.

Assessment of incidence of cerebral vascular diseases and prediction of stroke risk in chronic obstructive pulmonary disease patients using multimodal biomarkers

BACKGROUND

Early assessment of cerebrovascular disease in chronic obstructive pulmonary disease (COPD) patients is an important issue for a favorable influence on the quality of life.

METHODOLOGY

This cross-sectional case-control study was conducted on 38 eligible COPD patients (mean age 55.5 ± 11.5, 25 males, and 13 females) and 26 age-/sex-matched healthy controls. All participants were subjected to stroke risk screening instruments that included the Stroke Riskometer™, the Framingham 10-Year Risk Score, the stroke risk screening tool (the Department of Disease Control of Thailand), the My Risk Stroke Calculator, and Q Stroke. Radiologically, diffusion tensor imaging (DTI) and echo-gradient MRI (T2 star) T2 star imaging were done. Color-coded duplex sonography was done. Laboratory investigations included C-reactive protein (CRP), serum amyloid A, plasma fibrinogen level, serum IL6, 8-Isoprostane, vWF and urinary albumin creatinine ratio.

RESULTS

Stroke risk screening instruments revealed a significant increase in COPD patients. DTI showed a significant bilateral reduction in fractional isotropy and a significant bilateral increase in mean diffusivity of white matter through many areas in COPD patients. Patients also had a significant increase of intima-media thickness, presence of atherosclerotic focal thicknesses or plaques on duplex sonography. There was a significant elevation of CRP, serum amyloid A, plasma fibrinogen level, serum IL6, 8-isoprostane, von Willebrand factor (vWF), and urinary albumin creatinine ratio in COPD patients.

CONCLUSION

COPD patients had an increased risk for stroke that could be assessed on stroke risk screening instruments, DTI, T2 star, duplex sonography, and laboratory investigation and could be correlated with the severity of the disease.

Conceptualizing eating disorder recovery research: Current perspectives and future research directions

BACKGROUND

How we research eating disorder (ED) recovery impacts what we know (perceive as fact) about it. Traditionally, research has focused more on the "what" of recovery (e.g., establishing criteria for recovery, reaching consensus definitions) than the "how" of recovery research (e.g., type of methodologies, triangulation of perspectives). In this paper we aim to provide an overview of the ED field's current perspectives on recovery, discuss how our methodologies shape what is known about recovery, and suggest a broadening of our methodological "toolkits" in order to form a more complete picture of recovery. BODY: This paper examines commonly used methodologies in research, and explores how incorporating different perspectives can add to our understanding of the recovery process. To do this, we (1) provide an overview of commonly used methodologies (quantitative, qualitative), (2) consider their benefits and limitations, (3) explore newer approaches, including mixed-methods, creative methods (e.g., Photovoice, digital storytelling), and multi-methods (e.g., quantitative, qualitative, creative methods, psycho/physiological, behavioral, laboratory, online observations), and (4) suggest that broadening our methodological "toolkits" could spur more nuanced and specific insights about ED recoveries. We propose a potential future research model that would ideally have a multi-methods design, incorporate different perspectives (e.g., expanding recruitment of diverse participants, including supportive others, in study co-creation), and a longitudinal course (e.g., capturing cognitive and emotional recovery, which often comes after physical). In this way, we hope to move the field towards different, more comprehensive, perspectives on ED recovery.

CONCLUSION

Our current perspectives on studying ED recovery leave critical gaps in our knowledge about the process. The traditional research methodologies impact our conceptualization of recovery definitions, and in turn limit our understanding of the phenomenon. We suggest that we expand our range of methodologies, perspectives, and timeframes in research, in order to form a more complete picture of what is possible in recovery; the multiple aspects of an individual's life that can improve, the greater number of people who can recover than previously believed, and the reaffirmation of hope that, even after decades, individuals can begin, and successfully continue, their ED recovery process.

A risk calculator to predict suicide attempts among individuals with early-onset bipolar disorder

OBJECTIVES

To build a one-year risk calculator (RC) to predict individualized risk for suicide attempt in early-onset bipolar disorder.

METHODS

Youth numbering 394 with bipolar disorder who completed ≥2 follow-up assessments (median follow-up length = 13.1 years) in the longitudinal Course and Outcome of Bipolar Youth (COBY) study were included. Suicide attempt over follow-up was assessed via the A-LIFE Self-Injurious/Suicidal Behavior scale. Predictors from the literature on suicidal behavior in bipolar disorder that are readily assessed in clinical practice were selected and trichotomized as appropriate (presence past 6 months/lifetime history only/no lifetime history). The RC was trained via boosted multinomial classification trees; predictions were calibrated via Platt scaling. Half of the sample was used to train, and the other half to independently test the RC.

RESULTS

There were 249 suicide attempts among 106 individuals. Ten predictors accounted for >90% of the cross-validated relative influence in the model (AUC = 0.82; in order of relative influence): (1) age of mood disorder onset; (2) non-suicidal self-injurious behavior (trichotomized); (3) current age; (4) psychosis (trichotomized); (5) socioeconomic status; (6) most severe depressive symptoms in past 6 months (trichotomized none/subthreshold/threshold); (7) history of suicide attempt (trichotomized); (8) family history of suicidal behavior; (9) substance use disorder (trichotomized); (10) lifetime history of physical/sexual abuse. For all trichotomized variables, presence in the past 6 months reliably predicted higher risk than lifetime history.

CONCLUSIONS

This RC holds promise as a clinical and research tool for prospective identification of individualized high-risk periods for suicide attempt in early-onset bipolar disorder.

Development and Validation of a Cognitive Reserve Index in HIV

OBJECTIVES

In the neuroHIV literature, cognitive reserve has most often been operationalized using education, occupation, and IQ. The effects of other cognitively stimulating activities that might be more amenable to interventions have been little studied. The purpose of this study was to develop an index of cognitive reserve in people with HIV, combining multiple indicators of cognitively stimulating lifetime experiences into a single value.

METHODS

The data set was obtained from a Canadian longitudinal study (N = 856). Potential indicators of cognitive reserve captured at the study entry included education, occupation, engagement in six cognitively stimulating activities, number of languages spoken, and social resources. Cognitive performance was measured using a computerized test battery. A cognitive reserve index was formulated using logistic regression weights. For the evidence on concurrent and predictive validity of the index, the measures of cognition and self-reported everyday functioning were each regressed on the index scores at study entry and at the last follow-up [mean duration: 25.9 months (SD 7.2)], respectively. Corresponding regression coefficients and 95% confidence intervals (CIs) were computed.

RESULTS

Professional sports [odds ratio (OR): 2.9; 95% CI 0.59-14.7], visual and performance arts (any level of engagement), professional/amateur music, complex video gaming and competitive games, and travel outside North America were associated with higher cognitive functioning. The effects of cognitive reserve on the outcomes at the last follow-up visit were closely similar to those at study entry.

CONCLUSION

This work contributes evidence toward the relative benefit of engaging in specific cognitively stimulating life experiences in HIV.

Digital Health in Primordial and Primary Stroke Prevention: A Systematic Review

The stroke burden continues to grow across the globe, disproportionally affecting developing countries. This burden cannot be effectively halted and reversed without effective and widely implemented primordial and primary stroke prevention measures, including those on the individual level. The unprecedented growth of smartphone and other digital technologies with digital solutions are now being used in almost every area of health, offering a unique opportunity to improve primordial and primary stroke prevention on the individual level. However, there are several issues that need to be considered to advance development and use this important digital strategy for primordial and primary stroke prevention. Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines we provide a systematic review of the current knowledge, challenges, and opportunities of digital health in primordial and primary stroke prevention.

Effect of Physical Activity on Cardiovascular Event Risk in a Population-Based Cohort of Patients with Type 2 Diabetes

Cardiovascular disease (CVD) is the most common cause of morbidity and mortality among patients with type 2 diabetes (T2D). Physical activity (PA) is one of the few modifiable factors that can reduce this risk. The aim of this study was to estimate to what extent PA can contribute to reducing CVD risk and all-cause mortality in patients with T2D. Information from a population-based cohort including 26,587 patients with T2D from the Navarre Health System who were followed for five years was gathered from electronic clinical records. Multivariate Cox regression models were fitted to estimate the effect of PA on CVD risk and all-cause mortality, and the approach was complemented using conditional logistic regression models within a matched nested case-control design. A total of 5111 (19.2%) patients died during follow-up, which corresponds to 37.8% of the inactive group, 23.9% of the partially active group and 12.4% of the active group. CVD events occurred in 2362 (8.9%) patients, which corresponds to 11.6%, 10.1% and 7.6% of these groups. Compared with patients in the inactive group, and after matching and adjusting for confounders, the OR of having a CVD event was 0.84 (95% CI: 0.66-1.07) for the partially active group and 0.71 (95% CI: 0.56-0.91) for the active group. A slightly more pronounced gradient was obtained when focused on all-cause mortality, with ORs equal to 0.72 (95% CI: 0.61-0.85) and 0.50 (95% CI: 0.42-0.59), respectively. This study provides further evidence that physically active patients with T2D may have a reduced risk of CVD-related complications and all-cause mortality.

Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

Stroke Risk Screening Scales (SRSS): Identification of Domain and Item Generation

BACKGROUND

Stroke is a principal cause of mortality and disability in Thailand and globally. Early and comprehensive risk identification would be critical to identify people at high risk for stroke. Therefore, a comprehensive stroke risk screening tool is needed to assess all possible stroke risks and potential at-risk populations. In the future, such an instrument would benefit early detection and stroke prevention planning.

OBJECTIVE

The objective of the Stroke Risk Screening Scales (SRSS) development is to identify the domains and generating appropriate questions for the new SRSS.

METHODS

Using deductive methods suggested by Godfred Boateng and colleagues (2018), the domains were classified based on the existing literature. The questions were generated based on a comprehensive analysis of existing stroke risk screening scales and their representativeness of each domain. Five existing stroke risk screening tools including 1) the Stroke Riskometer^TM, 2) the Framingham 10-Year Risk Score, 3) the Stroke Risk Screening Tool (The Department of Disease Control of Thailand), 4) the My Risk Stroke Calculator, and 5) QStroke were included and identified.

RESULTS

Overall, 18 domains were included, and each domain was represented with at least one or more questions. Eight domains (44.44 %) are consisting of a dichotomous question alone, another eight domains (44.44 %) consist of multiple questions, which combined between dichotomous, categorical, or fill-in-the-blank questions, one domain (5.55 %) consists of a fill-in-the-blank question, and another one (5.55 %) include only one categorical question.

CONCLUSIONS

Developing a comprehensive tool to be used for stroke risk screening by extending the knowledge of stroke, stroke risk factors, and the best practice for tool development is necessitated for further practical stroke prevention planning.

A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders

Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.

Stroke Awareness among Elderly Hypertensives in a Rural Area of Bangalore District, India

OBJECTIVES

India faces a high and growing burden of hypertension, which is an important cerebrovascular risk factor, especially in elderly persons. Poor awareness contributes to delays in seeking health care, which is undesirable given the emergent nature of stroke. Literature regarding awareness in this subgroup of the population is scarce. Our objective was to assess awareness regarding cerebrovascular disease among elderly persons with hypertension residing in a rural area of Bangalore district, and estimate their individual risk of stroke.

MATERIAL & METHODS

We randomly selected 144 elderly hypertensives residing in a rural area in Bangalore district from a list of known hypertensives maintained as part of a population-based senior citizen health service. We developed an open-ended, face-validated questionnaire, which was administered following informed consent, to assess stroke awareness. We defined good awareness as knowing at least one risk factor, one warning sign, and having the knowledge that stroke requires immediate treatment. Univariate and multivariate analyses were performed to assess factors associated with good or poor awareness.

RESULTS

40% of the study population had not heard of the term 'stroke'. Only 22% could identify the brain as the organ affected. 51% could name at least one symptom. 45% of males and 24% of females believed that their hypertension predisposed them to stroke. 56% could not name a single risk factor. Only 37% of the males and 18% of the females had good overall awareness. Female gender and low literacy were associated with poor overall awareness, while being gainfully employed, a history of alcohol use and doing higher levels of exercise were associated with greater awareness on univariate analysis. None of these factors were statistically significantly related to stroke awareness on multivariate analysis.

CONCLUSIONS

Awareness of different aspects of stroke was very poor, highlighting the need for stroke education at all levels of care.

Does the Carotid Bulb Offer a Better 10-Year CVD/Stroke Risk Assessment Compared to the Common Carotid Artery? A 1516 Ultrasound Scan Study

The objectives of this study are to (1) examine the "10-year cardiovascular risk" in the common carotid artery (CCA) versus carotid bulb using an integrated calculator called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0) and (2) evaluate the performance of AECRS2.0 against "conventional cardiovascular risk calculators." These objectives are met by measuring (1) image-based phenotypes and AECRS2.0 score computation and (2) performance evaluation of AECRS2.0 against 12 conventional cardiovascular risk calculators. The Asian-Indian cohort (n = 379) with type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), or hypertension were retrospectively analyzed by acquiring the 1516 carotid ultrasound scans (mean age: 55 ± 10.1 years, 67% males, ∼92% with T2DM, ∼83% with CKD [stage 1-5], and 87.5% with hypertension [stage 1-2]). The carotid bulb showed a higher 10-year cardiovascular risk compared to the CCA by 18% (P < .0001). Patients with T2DM and/or CKD also followed a similar trend. The carotid bulb demonstrated a superior risk assessment compared to CCA in patients with T2DM and/or CKD by showing: (1) ∼13% better than CCA (0.93 vs 0.82, P = .0001) and (2) ∼29% better compared with 12 types of risk conventional calculators (0.93 vs 0.72, P = .06).

Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models

Background

Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRC_ML) and compares against statistically derived CVRC (CVRC_Stat) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors).

Methods

The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRC_Stat (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRC_Stat, the patients were again risk-stratified using CVRC_ML based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events.

Results

An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRC_Stat calculators were: AECRS2.0 (AUC 0.83, P<0.001), QRISK3 (AUC 0.72, P<0.001), WHO (AUC 0.70, P<0.001), ASCVD (AUC 0.67, P<0.001), FRS_cardio (AUC 0.67, P<0.01), FRS_stroke (AUC 0.64, P<0.001), MSRC (AUC 0.63, P=0.03), UKPDS56 (AUC 0.63, P<0.001), NIPPON (AUC 0.63, P<0.001), PROCAM (AUC 0.59, P<0.001), RRS (AUC 0.57, P<0.001), UKPDS60 (AUC 0.53, P<0.001), and SCORE (AUC 0.45, P<0.001), while the AUC for the CVRC_ML with integrated risk factors (AUC 0.88, P<0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRC_ML with integrated risk factors was 92.52% which was higher compared all the other CVRC_Stat.

Conclusions

ML-based CVD/stroke risk calculator provided a higher predictive ability of 10-year CVD/stroke compared to the 13 different types of statistically derived risk calculators including integrated model AECRS 2.0.

Ph- myeloproliferative neoplasms and the related risk factors for stroke occurrence: Results from a registry of patients treated with Anagrelide

Arterial thrombosis is a common complication in patients with Ph^- myeloproliferative neoplasms (MPN). We searched for the risk factors of stroke in MPN patients from anagrelide registry. We analyzed the potential risk factors triggering a stroke/TIA event in 249 MPN patients with previous stroke (n = 168) or Transient Ischemic Attack (TIA) (n = 140), and in 1,193 MPN control subjects (without clinical history of thrombosis). These patients were registered in a prospective manner, providing a follow-up period after Anagrelide treatment. The median age of the patients in the experimental group was of 56 years of age (ranging from 34-76) and of 53 years of age (ranging from 26-74) in the control group (p < 0.001). Using a multivariate model, we determined the following as risk factors: JAK2V617F mutation (OR 2.106, 1.458-3.043, p = 0.006), age (OR 1.017/year, 1.005-1,029, p = 0.006), male gender (OR 1.419, 1.057-1.903, p = 0.020), MPN diagnosis (OR for PMF 0.649, 0.446-0.944, p = 0.024), BMI (OR 0.687 for BMI > 25, 0.473-0.999, p = 0.05) and high TAG levels (OR 1.734, 1.162-2.586, p = 0.008), all of which were statistically significant for CMP development. Concerning the risk factors for thrombophilia, only the antiphospholipid syndrome (OR 1.994, 1.017-3.91, p = 0.048) was noteworthy in a stroke-relevant context. There was no significant difference between the blood count of the patients prior to a stroke event and the control group, both of which were under a cytoreductive treatment. We found that age, male gender, JAK2V617F mutation, previous venous thrombosis, and hypertriglyceridemia represent independent risk factors for the occurrence of a stroke in Ph^- MPN patients.

Chinese primary knee osteoarthritis progression cohort (CPKOPC) to evaluate the progression of knee osteoarthritis in the Beijing population: a prospective cohort study protocol

INTRODUCTION

Millions of patients are currently suffering from pain and dysfunction caused by osteoarthritis (OA), and billions of dollars have been invested into treatment. Because there is no effective treatment that can reverse the progression of knee OA, it is important to determine the risk factors that may influence the progression. However, although there are many studies that examine risk factors for progression, there are only a few that specifically focus on the impact of each risk factor for predicting progression of knee OA. This study aimed to develop a cohort of patients with primary knee OA in the Beijing area to establish models that identify the influence of each risk factor on the prediction of knee OA progression.

METHODS AND ANALYSIS

This is a prospective, multicentre, hospital-based cohort study. The study population comprises 2000 patients with primary knee OA from the Beijing area. The recruitment and baseline visits started in December 2017 and will finish in November 2018. After baseline visits, the patients will be followed for 3 years or until the occurrence of primary outcomes. Demographic variables will be collected during the baseline visit. Influencing factors including occupational exposures, family history and treatment will be collected at baseline and each follow-up visit. The primary outcome measure is a comprehensive index which will be combined with clinical WOMAC score, imaging K-L grade and clinical outcomes. These data will also be collected at baseline and each follow-up visit.

ETHICS AND DISSEMINATION

This study protocol has been approved by Peking University Third Hospital Medical Science Research Ethics Committee. All the eligible participants will give written informed consent. The findings will be published in peer-reviewed journals and presented at national or international conferences. Besides, the results will be disseminated to all participants via the social software 'WeChat'.

TRIAL REGISTRATION NUMBER

ChiCTR-ROC-17013790; preresults.

A Special Report on Changing Trends in Preventive Stroke/Cardiovascular Risk Assessment Via B-Mode Ultrasonography

PURPOSE OF REVIEW

Cardiovascular disease (CVD) and stroke risk assessment have been largely based on the success of traditional statistically derived risk calculators such as Pooled Cohort Risk Score or Framingham Risk Score. However, over the last decade, automated computational paradigms such as machine learning (ML) and deep learning (DL) techniques have penetrated into a variety of medical domains including CVD/stroke risk assessment. This review is mainly focused on the changing trends in CVD/stroke risk assessment and its stratification from statistical-based models to ML-based paradigms using non-invasive carotid ultrasonography.

RECENT FINDINGS

In this review, ML-based strategies are categorized into two types: non-image (or conventional ML-based) and image-based (or integrated ML-based). The success of conventional (non-image-based) ML-based algorithms lies in the different data-driven patterns or features which are used to train the ML systems. Typically these features are the patients' demographics, serum biomarkers, and multiple clinical parameters. The integrated (image-based) ML-based algorithms integrate the features derived from the ultrasound scans of the arterial walls (such as morphological measurements) with conventional risk factors in ML frameworks. Even though the review covers ML-based system designs for carotid and coronary ultrasonography, the main focus of the review is on CVD/stroke risk scores based on carotid ultrasound. There are two key conclusions from this review: (i) fusion of image-based features with conventional cardiovascular risk factors can lead to more accurate CVD/stroke risk stratification; (ii) the ability to handle multiple sources of information in big data framework using artificial intelligence-based paradigms (such as ML and DL) is likely to be the future in preventive CVD/stroke risk assessment.

The COAST stroke advance directive: A novel approach to preserving patient autonomy

Within the field of neurology, there has been limited discussion of how to best respect patient autonomy in patients presenting with an acute stroke, who often have impairments in language and cognition. In addition to performing a detailed neurologic examination and providing a thorough timeline of their current presentation and medical history, these patients and their families are then asked to quickly make critical medical decisions regarding acute stroke therapies (thrombolysis and endovascular therapy). These discussions are often limited by time constraints and inadequate opportunities for patient education regarding acute stroke care. This article discusses some of the challenges of preserving patient autonomy in patients presenting with acute stroke and the advent of a stroke advance directive (Coordinating Options for Acute Stroke Therapy [COAST]) aimed to overcome these obstacles.

Prevention of stroke: a global perspective

Along with the rising global burden of disability attributed to stroke, costs of stroke care are rising, providing the impetus to direct our research focus towards effective measures of stroke prevention. In this Series paper, we discuss strategies for reducing the risk of the emergence of disease (primordial prevention), preventing the onset of disease (primary prevention), and preventing the recurrence of disease (secondary prevention). Our focus includes global strategies and campaigns, and measurements of the effectiveness of worldwide preventive interventions, with an emphasis on low-income and middle-income countries. Our findings reveal that effective tobacco control, adequate nutrition, and development of healthy cities are important strategies for primordial prevention, whereas polypill strategies, use of mobile technology (mHealth), along with salt reduction and other dietary interventions, are effective in the primary prevention of stroke. An effective collaboration between various health-care sectors, government policies, and campaigns can successfully implement secondary prevention strategies, through surveillance and registries, such as the WHO's non-communicable diseases programmes, across high-income and low-income countries.

Randomized Controlled Trial of an Educational Intervention Using an Online Risk Calculator for Knee Osteoarthritis: Effect on Risk Perception

OBJECTIVE

Young adults, in general, are not aware of their risk of knee osteoarthritis (OA). Understanding risk and risk factors is critical to knee OA prevention. We tested the efficacy of a personalized risk calculator on accuracy of knee OA risk perception and willingness to change behaviors associated with knee OA risk factors.

METHODS

We conducted a randomized controlled trial of 375 subjects recruited using Amazon Mechanical Turk. Subjects were randomized to either use a personalized risk calculator based on demographic and risk-factor information (intervention), or to view general OA risk information (control). At baseline and after the intervention, subjects estimated their 10-year and lifetime risk of knee OA and responded to contemplation ladders measuring willingness to change diet, exercise, or weight-control behaviors.

RESULTS

Subjects in both arms had an estimated 3.6% 10-year and 25.3% lifetime chance of developing symptomatic knee OA. Both arms greatly overestimated knee OA risk at baseline, estimating a 10-year risk of 26.1% and a lifetime risk of 47.8%. After the intervention, risk calculator subjects' perceived 10-year risk decreased by 12.9 percentage points to 12.5% and perceived lifetime risk decreased by 19.5 percentage points to 28.1%. Control subjects' perceived risks remained unchanged. Risk calculator subjects were more likely to move to an action stage on the exercise contemplation ladder (relative risk 2.1). There was no difference between the groups for diet or weight-control ladders.

CONCLUSION

The risk calculator is a useful intervention for knee OA education and may motivate some exercise-related behavioral change.

Association of Ambulatory Blood Pressure Monitoring parameters with the Framingham Stroke Risk Profile

The Framingham Stroke Risk Profile (FSRP) is a novel and reliable tool for estimating the 10-year probability for incident stroke in stroke-free individuals, while the predictive value of ambulatory blood pressure monitoring (ABPM) for first-ever and recurrent stroke has been well established. We sought to evaluate cross-sectionally the association of ABPM parameters with FSRP score in a large sample of 2343 consecutive stroke-free individuals (mean age: 56.0±12.9, 49.1% male) who underwent 24-hour ABPM. True hypertensives showed significantly higher FSRP (11.2±5.0) compared to the normotensives (8.2±5.0, p<0.001), while subjects with white coat hypertension also had higher FSRP (10.2±4.7) than normotensives (8.2±5.0, p<0.001). Compared to dippers that exhibited the lowest FSRP, non-dippers and reverse-dippers exhibited significantly higher FSRP (9.8±4.8 for dippers vs 10.6±5.2 and 11.5±5.0 for non-dippers and reverse-dippers respectively, p≤0.001 for comparisons). In univariate analyses, the ABPM parameters that had the strongest correlation with FSRP were 24-hour (r=0.440, p<0.001), daytime (r=0.435, p<0.001) and night-time (r=0.423; p<0.001) pulse pressure (PP). The best fitting model for predicting FSRP (R²=24.6%) on multiple linear regression analyses after adjustment for vascular risk factors not included in FSRP comprised the following parameters in descending order: 24-hour PP (β=0.349, p<0.001), daytime SBP variability (β=0.124, p<0.001), 24-hour HR variability (β=-0.091, p<0.001), mean 24-hour HR (β=-0.107, p<0.001), BMI (β=0.081, p<0.001) and dipping percentage (β=-0.063, p=0.001). 24-hour PP and daytime SBP variability are the two ABPM parameters that were more strongly associated with FSRP-score. Reverse dippers had the highest FSRP among all dipping status profiles.

Anger and stroke: a potential association that deserves serious consideration

OBJECTIVE

To assess the relationship between states of anger and stroke.

METHODS

Systematic review of the literature.

RESULTS

In total, 21 papers were selected for the systematic review of data published on the subject of anger and stroke. A state of anger may be a risk factor for stroke, as well as a consequence of brain lesions affecting specific areas that are caused by a stroke. Scales to assess anger varied among authors. There was no consensus regarding the area of brain lesions that might lead to a state of anger. Although some authors agreed that lesions on the right side led to angrier behaviour, others found that lesions on the left side were more relevant to anger. Likewise, there was no consensus regarding the prevalence of anger pre or post-stroke. Some authors did not even find that these two conditions were related.

CONCLUSION

Although most authors have accepted that there is a relationship between anger and stroke, studies with uniform methodology need to be conducted if this association is to be properly evaluated and understood.

Development and feasibility of a personalized, interactive risk calculator for knee osteoarthritis

Background

The incidence of knee osteoarthritis (OA) is rising. While several risk factors have been associated with the development of knee OA, this information is not readily accessible to those at risk for osteoarthritis. Risk calculators have been developed for several prevalent chronic conditions but not for OA. Using published evidence on established risk factors, we developed an interactive, personalized knee OA risk calculator (OA Risk C) and conducted a pilot study to evaluate its acceptability and feasibility.

Methods

We used the Osteoarthritis Policy (OAPol) Model, a validated, state-transition simulation of the natural history and management of OA, to generate data for OA Risk C. Risk estimates for calculator users were based on a set of demographic and clinical factors (age, sex, race/ethnicity, obesity) and select risk factors (family history of knee OA, occupational exposure, and history of knee injury). OA Risk C presents personalized risk of knee OA in several ways to maximize understanding among a wide range of users. We conducted a study of 45 subjects in a primary care setting to establish the feasibility and acceptability of the OA risk calculator. Pilot study participants were asked several questions regarding ease of use, clarity of presentation, and clarity of the graphical representation of their risk. These questions used a five-level agreement scale ranging from strongly disagree to strongly agree.

Results

OA Risk C depicts information about users’ risk of symptomatic knee OA in 5 year intervals. Study participants estimated their lifetime risk at 38 %, while their actual lifetime risk, as estimated by OA Risk C, was 25 %. Eighty-four percent of pilot study participants reported that OA Risk C was easy to understand, and 89 % agreed that the graphs depicting their risk were clear and comprehensible.

Conclusions

We have developed a personalized, computer-based OA risk calculator that is easy to use. OA Risk C may be utilized to estimate individuals’ knee OA risk and to deliver educational and behavioral interventions focused on osteoarthritis risk reduction.