Chronic Management of Hypertension after Stroke: The Role of Ambulatory Blood Pressure Monitoring

Developing and Validating a Nomogram Model for Predicting Ischemic Stroke Risk

Background and purpose: Clinically, the ability to identify individuals at risk of ischemic stroke remains limited. This study aimed to develop a nomogram model for predicting the risk of acute ischemic stroke. Methods: In this study, we conducted a retrospective analysis on patients who visited the Department of Neurology, collecting important information including clinical records, demographic characteristics, and complete hematological tests. Participants were randomly divided into training and internal validation sets in a 7:3 ratio. Based on their diagnosis, patients were categorized as having or not having ischemic stroke (ischemic and non-ischemic stroke groups). Subsequently, in the training set, key predictive variables were identified through multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods, and a nomogram model was constructed accordingly. The model was then evaluated on the internal validation set and an independent external validation set through area under the receiver operating characteristic curve (AUC-ROC) analysis, a Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA) to verify its predictive efficacy and clinical applicability. Results: Eight predictors were identified: age, smoking status, hypertension, diabetes, atrial fibrillation, stroke history, white blood cell count, and vitamin B12 levels. Based on these factors, a nomogram with high predictive accuracy was constructed. The model demonstrated good predictive performance, with an AUC-ROC of 0.760 (95% confidence interval [CI]: 0.736-0.784). The AUC-ROC values for internal and external validation were 0.768 (95% CI: 0.732-0.804) and 0.732 (95% CI: 0.688-0.777), respectively, proving the model's capability to predict the risk of ischemic stroke effectively. Calibration and DCA confirmed its clinical value. Conclusions: We constructed a nomogram based on eight variables, effectively quantifying the risk of ischemic stroke.

Generative Artificial Intelligence in Patient Education: ChatGPT Takes on Hypertension Questions

Introduction Uncontrolled hypertension significantly contributes to the development and deterioration of various medical conditions, such as myocardial infarction, chronic kidney disease, and cerebrovascular events. Despite being the most common preventable risk factor for all-cause mortality, only a fraction of affected individuals maintain their blood pressure in the desired range. In recent times, there has been a growing reliance on online platforms for medical information. While providing a convenient source of information, differentiating reliable from unreliable information can be daunting for the layperson, and false information can potentially hinder timely diagnosis and management of medical conditions. The surge in accessibility of generative artificial intelligence (GeAI) technology has led to increased use in obtaining health-related information. This has sparked debates among healthcare providers about the potential for misuse and misinformation while recognizing the role of GeAI in improving health literacy. This study aims to investigate the accuracy of AI-generated information specifically related to hypertension. Additionally, it seeks to explore the reproducibility of information provided by GeAI. Method A nonhuman-subject qualitative study was devised to evaluate the accuracy of information provided by ChatGPT regarding hypertension and its secondary complications. Frequently asked questions on hypertension were compiled by three study staff, internal medicine residents at an ACGME-accredited program, and then reviewed by a physician experienced in treating hypertension, resulting in a final set of 100 questions. Each question was posed to ChatGPT three times, once by each study staff, and the majority response was then assessed against the recommended guidelines. A board-certified internal medicine physician with over eight years of experience further reviewed the responses and categorized them into two classes based on their clinical appropriateness: appropriate (in line with clinical recommendations) and inappropriate (containing errors). Descriptive statistical analysis was employed to assess ChatGPT responses for accuracy and reproducibility. Result Initially, a pool of 130 questions was gathered, of which a final set of 100 questions was selected for the purpose of this study. When assessed against acceptable standard responses, ChatGPT responses were found to be appropriate in 92.5% of cases and inappropriate in 7.5%. Furthermore, ChatGPT had a reproducibility score of 93%, meaning that it could consistently reproduce answers that conveyed similar meanings across multiple runs. Conclusion ChatGPT showcased commendable accuracy in addressing commonly asked questions about hypertension. These results underscore the potential of GeAI in providing valuable information to patients. However, continued research and refinement are essential to evaluate further the reliability and broader applicability of ChatGPT within the medical field.

Long-term circadian disruption shortens life span and dampens blood pressure diurnal rhythms in stroke-prone spontaneously hypertensive rats

Environmental cues such as light and timing of food intake influence molecular clocks that produce circadian rhythmicity of many biological functions. The master circadian clock is entrained by light input and synchronizes with peripheral clocks in every organ of the body. Careers that require rotating shift work schedules predispose workers to a constant desynchronization of these biological clocks and are associated with increased risk of cardiovascular disease. We used a stroke-prone spontaneously hypertensive rat model exposed to a known biological desynchronizer, chronic environmental circadian disruption (ECD), to test the hypothesis that it would accelerate the time to stroke onset. We then investigated whether time-restricted feeding could delay stroke onset and evaluated its usefulness as a countermeasure when combined with the constant disruption of the light cycle. We found that phase advancing of the light schedule accelerated stroke onset. Restricting food access time to 5 h/day regardless of lighting profoundly delayed stroke onset in both standard 12-h:12-h light/dark or ECD-lighting conditions compared with ad libitum feeding; however, acceleration by ECD versus control lighting conditions was still observed. Since hypertension is a precursor to stroke in this model, we assessed blood pressure in a small cohort longitudinally using telemetry. Mean daily systolic and diastolic blood pressure increased in a similar manner across rats in control and ECD conditions, thus hypertension was not grossly accelerated to cause earlier strokes. However, we observed intermittent dampening of rhythms after each shift of the light cycle reminiscent of a relapsing-remitting nondipping state. Our results suggest that constant disruption of environmental rhythms may be associated with an increased risk of cardiovascular complications in the presence of cardiovascular risk factors.NEW & NOTEWORTHY This stroke-prone spontaneously hypertensive rat model significantly delayed stroke onset with the timed food restriction intervention. Blood pressure recordings in this same model were continuous through the 3 mo and showed dampened systolic rhythms after each shift in the lighting schedule.

Development and validation of a clinical nomogram for differentiating hemorrhagic and ischemic stroke prehospital

OBJECTIVES

The early detection and identification of stroke are essential to the prognosis of patients with suspected stroke symptoms out-of-hospital. We aimed to develop a risk prediction model based on the FAST score to identify the different types of strokes early for emergency medical services (EMS).

METHODS

This retrospective observational study enrolled 394 stroke patients at a single center from January 2020 to December 2021. Demographic data, clinical characteristics, and stroke risk factors with patients were collected from the EMS record database. Univariate and multivariate logistic regression analysis was used to identify the independent risk predictors. The nomogram was developed based on the independent predictors, in which the discriminative value and calibration of the nomogram were verified by the receiver operator characteristic (ROC) curve and calibration plots.

RESULTS

A total of 31.90% (88/276) of patients were diagnosed with hemorrhagic stroke in the training set, while 36.40% (43/118) in the validation set. The nomogram was developed based on the multivariate analysis, including age, systolic blood pressure, hypertension, vomiting, arm weakness, and slurred speech. The area under the curve (AUC) of the ROC with nomogram was 0.796 (95% CI: 0.740-0.852, P < 0.001) and 0.808 (95% CI:0.728-0.887, P < 0.001) in the training set and validation set, respectively. In addition, the AUC with the nomogram was superior to the FAST score in both two sets. The calibration curve showed a good agreement with the nomogram and the decision curves analysis also demonstrated that the nomogram had a wider range of threshold probabilities than the FAST score in the prediction risk of hemorrhagic stroke.

CONCLUSIONS

This novel noninvasive clinical nomogram shows a good performance in differentiating hemorrhagic and ischemic stroke for EMS staff prehospital. Moreover, all of the variables of nomogram are acquired in clinical practice easily and inexpensively out-of-hospital.

Volume of White Matter Hyperintensities, and Cerebral Micro-Bleeds

PURPOSE

In the past years the significance of white matter hyperintensities (WMH) has gained raising attention because it is considered a marker of severity of different pathologies. Another condition that in the last years has been assessed in the neuroradiology field is cerebral microbleeds (CMB). The purpose of this work was to evaluate the association between the volume of WMH and the presence and characteristics of CMB.

MATERIAL AND METHODS

Sixty-five consecutive (males 45; median age 70) subjects were retrospectively analyzed with a 1.5 Tesla scanner. WMH volume was quantified with a semi-automated procedure considering the FLAIR MR sequences whereas the CMB were studied with the SWI technique and CMBs were classified as absent (grade 1), mild (grade 2; total number of CMBs: 1-2), moderate (grade 3; total number of CMBs: 3-10), and severe (grade 4; total number of CMBs: >10). Moreover, overall number of CMBs and the maximum diameter were registered.

RESULTS

Prevalence of CMBs was 30.76% whereas WMH 81.5%. Mann-Whitney test showed a statistically significant difference in WMH volume between subjects with and without CMBs (p < 0.001). Pearson analysis showed significant correlation between CMB grade, number and maximum diameter and WMH. The better ROC area under the curve (Az) was obtained by the hemisphere volume with a 0.828 (95% CI from 0.752 to 0,888; SD = 0.0427; p value = 0.001). The only parameters that showed a statistically significant association in the logistic regression analysis were Hemisphere volume of WMH (p = 0.001) and Cholesterol LDL (p = 0.0292).

CONCLUSION

In conclusion, the results of this study suggest the presence of a significant correlation between CMBs and volume of WMH. No differences were found between the different vascular territories.

Hypertension Among Hemorrhagic Stroke Patients in Northeast China: A Population-Based Study 2017-2019

BACKGROUND We aimed to profile the current epidemiology of hypertension among the survivors of hemorrhagic stroke in northeast China. MATERIAL AND METHODS Our cross-sectional study included 18 796 adults aged 40 years or older and residing in northeast China. Hemorrhagic stroke was identified according to the CT and/or MRI results. Hypertension was defined based on the Chinese hypertension guidelines. RESULTS We identified 208 patients with previous hemorrhagic stroke in this population-based study. The overall prevalence of hypertension in the studied population was 88%. Out of all the survivors of hemorrhagic stroke, 80.9% were aware of their hypertensive condition, 70.5% of the patients were in antihypertensive medications treatment, and only 12% of the patients had their blood pressure under control. Furthermore, only 17.10% of the patients who took hypertensive medications achieved appropriate blood pressure. Calcium channel blockers were more commonly used than other medications. Patients with controlled hypertension had significantly higher percentages of comorbidities when compared to those with uncontrolled hypertension. In our patient sample, the rates of stage 2 and stage 3 hypertension in the hemorrhagic stroke population were 28.8% and 15.9%, respectively, and women had a significantly higher prevalence of stage 3 hypertension when compared with men (21.3% vs. 10.0%, P=0.026). CONCLUSIONS The high prevalence of uncontrolled hypertension and high rates of blood pressure at stages 2 and 3 in patients with prior hemorrhagic stroke indicated a considerable stroke burden in northeast China. Therefore, effective and long-time management of hypertension in stroke survivors should be a priority.

Increasing Blood Pressure Variability Predicts Poor Functional Outcome Following Acute Stroke

INTRODUCTION

Increasing blood pressure variability has been reported following acute stroke, but there is uncertainty about how best to measure it and about the impact on prognosis following acute ischaemic stroke and transient ischaemic attack.

METHODS

Enhanced casual blood pressure and ambulatory blood pressure monitoring were completed at baseline (≤48 h post symptom onset). Blood pressure variability was defined by standard deviation and coefficient of variation of systolic, diastolic, mean arterial pressure, and pulse pressure. Modified Rankin scale score ≥3 described poor functional outcome assessed at 1- and 12-months post-stroke. Multivariable logistic regression models incorporating blood pressure variability measurement and other factors were performed, and odds ratio and 95% confidence intervals reported.

RESULTS

232 patients were recruited; 45 were dependent at 1-month, and 37 at 12-months. Dependent patients were more likely to be older, with a higher burden of pre-morbid conditions, and with increased blood pressure variability. Enhanced casual standard deviations of diastolic blood pressure [1.19 (1.02 to 1.39)] and mean arterial pressure [1.20 (1.00 to 1.43)] predicted dependency at 1-month. Predictors of 12-month dependency included: enhanced casual standard deviation of mean arterial pressure [1.21 (1.0-1.46)]; 24 h ambulatory monitor standard deviations of diastolic blood pressure [2.30 (1.08-4.90)] and mean arterial pressure [1.72 (1.09-2.72)], and the coefficient of variation of mean arterial pressure [1.76 (1.05-2.94)]; day-time ambulatory monitor coefficient of variation of systolic blood pressure [1.44 (1.02-2.03)] and mean arterial pressure [1.46 (1.02-2.08)]; and night-time ambulatory standard deviation of diastolic blood pressure [1.65 (1.03 -2.63)], and the coefficient of variation of mean arterial pressure and [1.38 (1.00- 1.90)] and pulse pressure [1.29 (1.00-1.65)].

CONCLUSION

Increasing blood pressure variability is independently and modestly associated with poor functional outcome at 1- and 12-months following acute stroke.

Biomarkers levels and brachial and central blood pressure during the subacute phase of lacunar stroke and other ischemic stroke subtypes

We aimed to evaluate brachial and central blood pressure (BP) estimates and biomarker levels in lacunar ischemic stroke (IS) and other IS subtypes (nonlacunar stroke). We studied 70 functionally independent subjects consecutively admitted to our institution after a first episode of IS. Subjects with previous heart failure were excluded. BP was measured at admission and during the subacute phase of stroke (5-7 days after stroke onset). Aortic pulse wave velocity (aPWV), augmentation index (AIx), and 24 h brachial and central BP (24h-ABPM) were measured by means of a Mobil-O-Graph device during the subacute phase of stroke. Determination of N-terminal prohormone of brain natriuretic peptide (NT-proBNP), urinary albumin excretion, and echocardiography were performed in all subjects. After adjusting for age and clinical severity, lacunar IS had significantly higher levels of BP at admission (systolic BP 173 ± 37 vs 153 ± 28 mmHg, p = 0.006; diastolic BP: 97 ± 21 vs 86 ± 16 mmHg, p = 0.035) and during the subacute phase of stroke (systolic BP 152 ± 23 vs 134 ± 19 mmHg, p = 0.001; diastolic BP: 84 ± 14 and 77 ± 10 mmHg, respectively; p = 0.038) but lower NT-proBNP levels (median: 36,277 vs 274 pg/mL, p = 0.009) than nonlacunar IS. Central BP, aPWV, and AIx were not different between lacunar and nonlacunar IS, neither the rate of target organ damage. In conclusion, patients with a first episode of lacunar IS have higher BP values at admission and during the subacute phase of stroke and lower levels of NT-proBNP, suggesting a closer relationship with hypertension of this IS subtype.

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

The use of wearable and Internet-of-Things (IoT) for smart and affordable healthcare is trending. In traditional setups, the cloud backend receives the healthcare data and performs monitoring and prediction for diseases, diagnosis, and wellness prediction. Fog computing (FC) is a distributed computing paradigm that leverages low-power embedded processors in an intermediary node between the client layer and cloud layer. The diagnosis for wellness and fitness monitoring could be transferred to the fog layer from the cloud layer. Such a paradigm leads to a reduction in latency at an increased throughput. This paper processes a fog-based deep learning model, DeepFog that collects the data from individuals and predicts the wellness stats using a deep neural network model that can handle heterogeneous and multidimensional data. The three important abnormalities in wellness namely, (i) diabetes; (ii) hypertension attacks and (iii) stress type classification were chosen for experimental studies. We performed a detailed analysis of proposed models’ accuracy on standard datasets. The results validated the efficacy of the proposed system and architecture for accurate monitoring of these critical wellness and fitness criteria. We used standard datasets and open source software tools for our experiments.

Ambulatory blood pressure variability within the first 24 hours after admission and outcomes of acute ischemic stroke

Our purpose was to evaluate the value of blood pressure variability within the first 24 hours after admission in predicting outcomes of patients with acute ischemic stroke (AIS). A greater variability in systolic blood pressure (adjusted odds ratio [OR] = 1.801, 95% confidence interval [CI] = 1.167-2.779) was associated with poor discharge outcome, especially for nondiabetics (adjusted OR = 1.948, 95% CI = 1.184-3.205) and cardioembolism-related patients with AIS (OR = 7.650, 95% CI = 1.370-42.713). However, this correlation was not observed with a long-term (3-month or 6-month) outcome in patients with AIS. There was no association between diastolic blood pressure variability within the first 24 hours after admission and outcome. In conclusion, systolic blood pressure variability within the first 24 hours after admission is a critical predictor for short-term outcome of patients with AIS.

In vivo target bio-imaging of cerebral ischemic stroke by real-time labeling of zinc

Through intravenous injection of zinc gluconate, we could readily realize in vivo fluorescence imaging by real-time labeling the relevant brain regions of CIS model mice based on the in situ biosynthesis of fluorescence zinc nanoclusters in target diseased sites.