Prevalence of stroke stratified by the ankle-brachial blood pressure index (ABI) and interarm and interankle blood pressure (BP) differences.

<p>The numbers of strokes and participants are given alongside the symbols and at the bottom, respectively. <i>P</i>-values are given for trends in each group. A: The prevalence of stroke for different cutoff points of the ABI, interarm BP difference and interankle BP difference. Open circles represent the prevalence of stroke for ABI (left), interarm BP difference (middle) and interankle BP difference (right). B: The prevalence of stroke stratified according to real tertiles/quartiles/quintiles of the ABI distribution. Open circles represent the prevalence of stroke for real tertiles (left), quartiles (middle) and quintiles of the ABI distribution (right).</p

Multivariate logistic regression analysis of factors associated with the prevalence of stroke.

<p>The variables included in the analysis were sex (0, female; 1, male), age (0, 35–55 years; 1, over 55 years), ethnicity (0, Han; 1, Manchu), smoking (1, smoking every day for more than one year; 0, otherwise), alcohol intake (1, once at least every day; 0, otherwise), hypertension (1, systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg, or the use of antihypertensive drugs; 0, otherwise), body mass index (0, <24 kg/m²; 1, ≥24 kg/m²), ankle-brachial blood pressure index (0, <0.9; 1 ≥0.9), interarm blood pressure difference (0, <15 mmHg; 1, ≥15 mmHg), interankle blood pressure difference (0, <10 mmHg; 1, ≥10 mmHg), total cholesterol (1, ≥5.21 mmol/L; 0, otherwise), triglycerides (1, ≥1.7 mmol/L; 0, otherwise), low-density lipoprotein cholesterol (1, ≥3.3 mmol/L; 0, otherwise), high-density lipoprotein cholesterol (0, <0.9 mmol/L; 1, otherwise), fasting blood glucose (1, ≥6.0 mmol/L; 0, otherwise). A: Adjusted co-variables with <i>p</i> < 0.05 in the univariate analysis and other well-documented risk factors for stroke (smoking status, alcohol intake and family history of stroke) were introduced into the multiple logistic regression models. B: Adjusted covariables with <i>p</i> < 0.05 in the univariate analysis, but excluding hypertension and ankle-brachial blood pressure index, and other well-documented risk factors for stroke (smoking status, alcohol intake and family history of stroke) were introduced into the multiple logistic regression models. Abbreviations: ABI, ankle-brachial blood pressure index; BMI, body mass index; BP, blood pressure; FBG, fasting blood glucose. HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides.</p

Sex-stratified baseline characteristics of the study population.

<p>Values are presented as the mean ± standard deviation, median (interquartile range) or <i>n</i> (%). Abbreviations: ABI, ankle–brachial index; BMI, body mass index; BP, blood pressure; Δ, difference; IQR, interquartile range; SD, standard deviation.</p><p>Sex-stratified baseline characteristics of the study population.</p

Net reclassification improvement by adding interankle blood pressure difference to the risk factors independently associated with stroke (age, family history of stroke, hypertension, and ankle-brachial blood pressure index).

<p>Abbreviations: CI, confidence interval; IAΔBP, interankle blood pressure difference.</p><p>Net reclassification improvement by adding interankle blood pressure difference to the risk factors independently associated with stroke (age, family history of stroke, hypertension, and ankle-brachial blood pressure index).</p

Abnormal four-limb blood pressure (BP) measurements and prevalence of stroke.

<p>Numbers above the bars are the rates of prior stroke. The <i>p</i>-value for comparison between the two groups was determined using the chi-square test.</p

Comparison of the characteristics of the participants with and without a history of stroke.

<p>Values are presented as the mean ± standard deviation, median (interquartile range) or <i>n</i> (%). Abbreviations: ABI, ankle–brachial index; BMI, body mass index; BP, blood pressure; Δ, difference; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; IQR, interquartile range; LDL-C, low-density lipoprotein cholesterol; SD, standard deviation; TC, total cholesterol; TG, triglycerides.</p><p>Comparison of the characteristics of the participants with and without a history of stroke.</p

The Association of Four-Limb Blood Pressure with History of Stroke in Chinese Adults: A Cross-Sectional Study

<div><p>Objective</p><p>We investigated the association of ankle-brachial blood pressure index (ABI), interarm blood pressure (BP) difference and interankle BP difference, obtained by simultaneous four-limb BP measurement, with history of stroke in a Chinese adult population.</p><p>Methods</p><p>This cross-sectional study included 1485 participants aged ≥35 years in the framework of the China Hypertension Survey. We performed simultaneous four-limb BP measurement using oscillometric devices with the participants in the supine position and calculated ABI and interarm/interankle BP differences between the 4 limbs. Logistic regression analysis was used to estimate the association of these BP parameters and other factors with a history of stroke.</p><p>Results</p><p>In univariate analyses, participants with ABI <0.9, interarm BP difference ≥15 mmHg, and interankle BP difference ≥10 mmHg had a higher prevalence of stroke than those without (<i>p</i> < 0.0001, <i>p</i> = 0.0152, <i>p</i> = 0.002, respectively). Multiple logistic regression analyses suggested, ABI <0.9 was independently associated with a history of stroke after adjustment for interarm BP difference ≥15 mmHg, interankle BP difference ≥10 mmHg, and traditional risk factors for stroke (<i>p</i> = 0.001). An interankle BP difference ≥10 mmHg was associated with prior stroke after the two variables of hypertension and ABI were removed from the logistic regression model (<i>p</i> = 0.0142). Net reclassification improvement analysis showed that inclusion of interankle BP difference ≥10 mmHg to the independent risk factors (age, family history of stroke, hypertension, and ABI) improved net reclassification by 11.92%.</p><p>Conclusion</p><p>ABI <0.9 is an independent risk factor for stroke prevalence in Chinese adults and it just detected a small propotion of paticipants. The addition of interankle BP difference ≥10 mmHg to the independent risk factors for stroke may improve the prediction of stroke.</p></div

Prevalence of stroke stratified by the ankle-brachial blood pressure index (ABI) and interarm and interankle blood pressure (BP) differences.

<p>The prevalence of stroke was stratified according to the ABI (≥0.9 vs. <0.9, left) interarm BP difference (<15 mmHg vs. ≥15 mmHg, middle), and interankle BP difference (<10 mmHg vs. ≥10 mmHg, right). The <i>p</i>-value (Chi-square test) is given for each comparison. Numbers above the bars are rates of prior stroke.</p

Data_Sheet_1_Voxel- and tensor-based morphometry with machine learning techniques identifying characteristic brain impairment in patients with cervical spondylotic myelopathy.docx

AimThe diagnosis of cervical spondylotic myelopathy (CSM) relies on several methods, including x-rays, computed tomography, and magnetic resonance imaging (MRI). Although MRI is the most useful diagnostic tool, strategies to improve the precise and independent diagnosis of CSM using novel MRI imaging techniques are urgently needed. This study aimed to explore potential brain biomarkers to improve the precise diagnosis of CSM through the combination of voxel-based morphometry (VBM) and tensor-based morphometry (TBM) with machine learning techniques.MethodsIn this retrospective study, 57 patients with CSM and 57 healthy controls (HCs) were enrolled. The structural changes in the gray matter volume and white matter volume were determined by VBM. Gray and white matter deformations were measured by TBM. The support vector machine (SVM) was used for the classification of CSM patients from HCs based on the structural features of VBM and TBM.ResultsCSM patients exhibited characteristic structural abnormalities in the sensorimotor, visual, cognitive, and subcortical regions, as well as in the anterior corona radiata and the corpus callosum [P ConclusionCSM may cause widespread and remote impairments in brain structures. This study provided a valuable reference for developing novel diagnostic strategies to identify CSM.</p

Agreement of tumor sizes, as measured by MRI versus postsurgical pathology.

<p>(A) Bland-Altman plot of tumor size measured by pretreatment MRI examination and postsurgical pathological results; 95% plots are within the limit of agreement (0±10 mm), indicating a good agreement between pretreatment MRI results and postsurgical pathological measurement. (B) Bland-Altman plot of tumor size measured by posttreatment MRI and postsurgical pathology; almost 40% plots are out of the limit of agreement (0±10 mm), which indicates a poor agreement between posttreatment MRI and postsurgical pathology, i.e posttreatment MRI results may not be in place of postsurgical pathological measurement.</p