Comparison of the Characteristics and Risk Factors of Carotid Atherosclerosis in High Stroke Risk Populations Between Urban and Rural Areas in North China

Development and Internal Validation of a Risk Prediction Model for Carotid Atherosclerosis in the Hyperuricemia Population

Purpose

The aim of this study was to identify independent risk factors for carotid atherosclerosis (CAS) in a population with hyperuricemia (HUA) and develop a CAS risk prediction model.

Patients and Methods

This retrospective study included 3579 HUA individuals who underwent health examinations, including carotid ultrasonography, at the Zhenhai Lianhua Hospital in Ningbo, China, in 2020. All participants were randomly assigned to the training and internal validation sets in a 7:3 ratio. Multivariable logistic regression analysis was used to identify independent risk factors associated with CAS. The characteristic variables were screened using the least absolute shrinkage and selection operator combined with 10-fold cross-validation, and the resulting model was visualized by a nomogram. The discriminative ability, calibration, and clinical utility of the risk model were validated using the receiver operating characteristic curve, calibration curve, and decision curve analysis.

Results

Sex, age, mean red blood cell volume, and fasting blood glucose were identified as independent risk factors for CAS in the HUA population. Age, gamma-glutamyl transpeptidase, serum creatinine, fasting blood glucose, total triiodothyronine, and direct bilirubin, were screened to construct a CAS risk prediction model. In the training and internal validation sets, the risk prediction model showed an excellent discriminative ability with the area under the curve of 0.891 and 0.901, respectively, and a high level of fit. Decision curve analysis results demonstrated that the risk prediction model could be beneficial when the threshold probabilities were 1-87% and 1-100% in the training and internal validation sets, respectively.

Conclusion

We developed and internally validated a risk prediction model for CAS in a population with HUA, thereby contributing to the CAS early identification.

Fully automatic carotid arterial stiffness assessment from ultrasound videos based on machine learning

Arterial stiffness (AS) refers to the loss of arterial compliance and alterations in vessel wall properties. The study of local carotid stiffness (CS) is particularly important since carotid artery stiffening raises the risk of stroke, cognitive impairment, and dementia. So, stiffness measurement as a screening tool approach is crucial because it can reduce mortality and facilitate therapy planning. This study aims to evaluate the stiffness of the CCA using machine learning (ML) through the features of diameter change (ΔD) and stiffness parameters. This study was conducted in seven stages: data collection, preprocessing, CCA segmentation, CCA lumen diameter (DCCA) computing during cardiac cycles, denoising signals of DCCA, computational of AS parameters, and stiffness assessment using ML. The 51 videos (with 25 s) of CCA B-mode ultrasound (US) were used and analyzed. Each US video yielded approximately 750 sequential frames spanning about 24 cardiac cycles. Firstly, US preset settings with time gain compensation with a U-pattern were employed to enhance CCA segmentations. The study showed that auto region-growing, employed three times, is appropriate for segmenting walls with a short running time (4.56 s/frame). The diameter computed for frames constructs a signal (diameter signal) with noisy parts in the shape of peak variance and an un-smooth side. Among the 12 employed smoothing methods, spline fitting with a mean peak difference per cycle (MPDCY) of 0.58 pixels was the most effective for the diameter signal. The authors propose the MPDCY as a new selection criterion for smoothing methods with highly preserved peaks. The ΔD (D_sys-D_dia) determined in this study was validated by statistical analysis as a viable replacement for manual ΔD measurement. Statistical analysis was carried out by Mann-Whitney t-test with a p-value of 0.81, regression line R² = 0.907, and there was no difference in means between the two groups for box plots. The stiffness parameters of the carotid arteries were calculated based on auto-ΔD and pulse pressure. Five ML models, including K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and random forest (RF), fed by distension (ΔD) and five stiffness parameters, were used to distinguish between the stiffened and un-stiffened CCA. Except for SVM, all models performed excellently in terms of specificity, sensitivity, precision, and area under the curve (AUC). In addition, the scatterplot and statistical analysis of the fed features confirm these remarkable outcomes. The scatter plot demonstrates that a linear hyperline can easily distinguish between the two classes. The statistical analysis shows that the stiffness parameters computed from the database of this work were statistically (p < 0.05) distributed into the non-stiffness and stiffness groups. The presented models are validated by applying them to additional datasets. Applying models to other datasets reveals a model performance of 100%. The proposed ML models could be applied in clinical practice to detect CS early, which is essential for preventing stroke.

Red blood cell distribution width in different time-points of peripheral thrombolysis period in acute ischemic stroke is associated with prognosis

The relationship between red blood cell distribution width (RDW) in peripheral thrombolysis period and prognosis is not fully clarified in those who underwent intravenous thrombolysis (IVT) for acute ischemic stroke (AIS). Our study aimed to clarify this issue. A retrospective analysis of about 510 consecutive thrombolysis cases for AIS from January 2015 to March 2019 in a single-center database was done and followed-up for 3 months. We used univariate and multivariable models to evaluate the relationship between RDW levels at various time-points after IVT and the occurrence risk of hemorrhagic transformation (HT) and recurrent stroke, and used COX regression to assess the hazard ratios of outcomes with RDW levels. Elevated risk of HT was found in higher tertiles of RDW (OR = 10.282, 95% confidence interval (CI) 2.841–39.209, P < 0.001 in Tp tertile G3; OR = 5.650, 95% CI 1.992–16.025, P = 0.001 in T24 tertile G3; OR = 4.308, 95% CI 1.480–12.542, P = 0.007 in T48 tertile G3 and OR = 6.384, 95% CI 2.201–18.515, P = 0.001 in T72 tertile G3, respectively). Occurrence of recurrent stroke was highest in the RDW tertile G3 (HR = 4.580, 95% CI 2.123–9.883, P < 0.001 in Tp tertile G3; HR = 5.731, 95% CI 2.498–13.151, P = 0.001 in T24 tertile G3; HR = 3.019, 95% CI 1.969–4.059, P = 0.031 in T48 tertile G3; HR = 3.318, 95% CI 1.598–6.890, P = 0.001 in T72 tertile G3, respectively). Mean RDW levels ≥13.60 among AIS patients undergoing thrombolysis was associated with higher risk of HT and recurrent stroke.

The Impact of Dietary Diversity, Lifestyle, and Blood Lipids on Carotid Atherosclerosis: A Cross-Sectional Study

Carotid atherosclerosis is a common arterial wall lesion that causes narrowing and occlusion of the arteries and is the basis of cardiovascular events. Dietary habits, lifestyle, and lipid metabolism should be considered integrally in the context of carotid atherosclerosis (CAS). However, this area has been investigated less often in China. To understand the prevalence of CAS in China and the impact of dietary diversity and habits, lifestyle, and lipid metabolism on CAS as well as its predictive factors, a cross-sectional study was performed in two northern and southern Chinese tertiary hospitals from 2017 to 2019. Included participants underwent carotid artery color Doppler ultrasonography, blood lipid examination and dietary evaluation. In total, 11,601 CAS patients and 27,041 individuals without carotid artery lesions were included. The prevalence of CAS was 30.0% in this group. High BMI (OR: 1.685, 95% CI [1.315-2.160]), current (1.148 [1.077-1.224]) or ex-smoking (1.349 [1.190-1.529]), abstinence from alcohol ((1.223 [1.026-1.459]), social engagement (1.122 [1.050-1.198]), hypertension (1.828 [1.718-1.945]), and total cholesterol (1.438 [1.298-1.594]) were risk factors for CAS, while higher dietary diversity according to DDS-2 (0.891 [0.805-0.989]), HDL-C (0.558 [0.487-0.639]), sugar-sweetened beverages (0.734 [0.696-0.774]), and no midnight snack consumption (0.846 [0.792-0.903]) were protective factors. This current study demonstrated that higher dietary diversity was a protective factor against CAS in a healthy population. In addition, current recommendations of healthy lifestyle and dietary habits for preventing CAS should be strengthened. In addition, dietary diversity should concentrate on food attributes and dietary balance, rather than increased quantities.

Etiopathogenic features of severe epistaxis in histological samples from individuals with or without arterial hypertension

There is a consensus that arterial hypertension (AH) is associated with stroke. Therefore, this study aimed to evaluate the histology of the microvasculature associated with the mucosa of the posterior nasal cavity to identify possible factors related to vascular weakening and rupture. Histological sections were obtained from hypertensive and normotensive individuals, regardless of epistaxis. Our results showed that the group with AH had: (a) smaller median diameter of the lumen of arteries and arterioles; (b) increased thickness of the intimal arteries and arterioles, slight inflammatory infiltrate, and rupture of internal elastic lamina; (c) greater thickness of the middle tunica in arterioles; (d) lower percentage of histological sections with non-injured intimal layers in capillaries, arterioles, and small arteries; (e) lower percentage of histological sections with intact media tunic and/or myocytes juxtaposed in arteries and arterioles; (f) no difference between the diameters of small arteries or arterioles. The intima was thicker in individuals with severe epistaxis than in the normotensive group, but it did not differ from the AH group. Thus, hypertension may cause structural lesions in the vascular layers, and in the absence of tissue repair and the persistence of AH, these lesions may favour vascular rupture, especially during hypertensive peaks.

Machine learning models for screening carotid atherosclerosis in asymptomatic adults

Carotid atherosclerosis (CAS) is a risk factor for cardiovascular and cerebrovascular events, but duplex ultrasonography isn’t recommended in routine screening for asymptomatic populations according to medical guidelines. We aim to develop machine learning models to screen CAS in asymptomatic adults. A total of 2732 asymptomatic subjects for routine physical examination in our hospital were included in the study. We developed machine learning models to classify subjects with or without CAS using decision tree, random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP) with 17 candidate features. The performance of models was assessed on the testing dataset. The model using MLP achieved the highest accuracy (0.748), positive predictive value (0.743), F1 score (0.742), area under receiver operating characteristic curve (AUC) (0.766) and Kappa score (0.445) among all classifiers. It’s followed by models using XGBoost and SVM. In conclusion, the model using MLP is the best one to screen CAS in asymptomatic adults based on the results from routine physical examination, followed by using XGBoost and SVM. Those models may provide an effective and applicable method for physician and primary care doctors to screen asymptomatic CAS without risk factors in general population, and improve risk predictions and preventions of cardiovascular and cerebrovascular events in asymptomatic adults.

Current status of premature mortality from four non-communicable diseases and progress towards the Sustainable Development Goal target 3.4: a population-based study in northeast China, 2004-2017

Background and aim

According to the United Nations’ Sustainable Development Goal (SDG) target 3.4, premature mortality from four non-communicable diseases (cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes mellitus, collectively referred to as NCD4) should achieve a minimum decline of 33% in 2030 relative to 2015. This remains a challenge for China. This study aimed to evaluate the current status and progress towards this target in Liaoning Province, one of the three provinces in northeast China.

Methods

We calculated the premature mortality rates (PMRs) per year and average annual percentage changes (AAPCs) from NCD4 using mortality data between 2004 and 2017. The trend was analyzed in the whole population, as well as in subpopulations of gender (male/female) and inhabiting area (urban/rural). PMRs from NCD4 for 2030 were projected by fitting a linear regression based on the current trend, which was identified by a Joinpoint model.

Findings

In the whole population, only chronic respiratory diseases showed a significant decline (AAPC: − 6.5%, p < 0.05), while only cancer showed a significant increase (AAPC: + 1.3%, p < 0.05); taken together, NCD4 showed a significant increase (AAPC: + 0.6%, p < 0.05). In the subpopulations, while males showed a significant increase in NCD4 (AAPC: + 1.5%, p < 0.05), cardiovascular diseases (AAPC: + 1.7%, p < 0.05), cancer (AAPC: + 1.8%, p < 0.05), and diabetes mellitus (AAPC: + 4.2%, p < 0.05), females showed a significant decline in NCD4 (AAPC: − 1.2%, p < 0.05), cardiovascular diseases (AAPC: − 1.8%, p < 0.05), diabetes mellitus (AAPC: − 2.1%, p < 0.05), but showed a mild increase in cancer (AAPC: + 0.5%, p > 0.05). A comparative analysis of the projected PMRs for 2030 with the 2015 levels revealed that only chronic respiratory diseases are expected to achieve the SDG target 3.4, apart from in the urban male subpopulation.

Conclusion

Except for chronic respiratory diseases, NCD4 cannot be expected to achieve the SDG target 3.4 in the whole population of Liaoning Province. Under these circumstances, special attention should be paid to reducing the risks of cancer and providing preventative interventions for men.