ObjectiveTo systematically review the effectiveness and safety of intensive blood pressure lowering in intracerebral hemorrhage (ICH).
MethodsRandomised controlled trials (RCTs) and quasi-RCTs about ICH patients receiving intensive blood pressure lowering were searched from PubMed, EMbase, SCIE, The Cochrane Library (Issue 2, 2013), CBM, CNKI, VIP and WanFang Data until March, 2014. Literature was screened according to the exclusion and inclusion criteria by two reviewers independently and meta-analysis was conducted using RevMan 5.2 software after data extraction and quality assessment.
ResultsA total of 24 studies were included involving 6 299 patients, of which 10 were RCTs and 14 were quasi-RCTs. The results of meta-analysis showed that intensive blood pressure lowering was superior to guideline-recommended intervention in reducing 24-h hematoma expansion rates (OR=0.36, 95%CI 0.28 to 0.46, P < 0.05), 24-h hematoma expansion volume (MD=-3.71, 95%CI-4.15 to-3.28, P < 0.05) and perihematomal edema volume (MD=-1.09, 95%CI-1.92 to-0.22, P < 0.05). Meanwhile, intensive blood pressure lowering improved 21-d NIHSS score (MD=-3.44, 95%CI-5.02 to-1.87, P < 0.05). But there was no significant difference in mortality and adverse reaction between the two groups.
ConclusionCurrent evidence shows that intensive blood pressure lowering could reduce hematoma expansion volume and perihematomal edema volume, which is beneficial to recovery of neurological function, but ICH patients' long-term prognosis needs to be further studied. Due to the limited quantity and quality of the included studies, high quality studies are needed to verify the above conclusion.
Sleep apnea causes cardiac arrest, sleep rhythm disorders, nocturnal hypoxia and abnormal blood pressure fluctuations in patients, which eventually lead to nocturnal target organ damage in hypertensive patients. The incidence of obstructive sleep apnea hypopnea syndrome (OSAHS) is extremely high, which seriously affects the physical and mental health of patients. This study attempts to extract features associated with OSAHS from 24-hour ambulatory blood pressure data and identify OSAHS by machine learning models for the differential diagnosis of this disease. The study data were obtained from ambulatory blood pressure examination data of 339 patients collected in outpatient clinics of the Chinese PLA General Hospital from December 2018 to December 2019, including 115 patients with OSAHS diagnosed by polysomnography (PSG) and 224 patients with non-OSAHS. Based on the characteristics of clinical changes of blood pressure in OSAHS patients, feature extraction rules were defined and algorithms were developed to extract features, while logistic regression and lightGBM models were then used to classify and predict the disease. The results showed that the identification accuracy of the lightGBM model trained in this study was 80.0%, precision was 82.9%, recall was 72.5%, and the area under the working characteristic curve (AUC) of the subjects was 0.906. The defined ambulatory blood pressure features could be effectively used for identifying OSAHS. This study provides a new idea and method for OSAHS screening.
Clinical studies had demonstrated that slow breathing could lower blood pressure significantly. Based on this knowledge, a portable blood pressure depressor was designed in this study. The device used a miniature variable distance capacitive sensor to collect respiratory signal, an STM32 as the main control chip, a WT588D voice chip to generate voice and music and guide slow breathing, and a 3.5-inch color screen to display breathing state and provide guidance. For patients with difficulty in adapting themselves to the slow breathing training, an intelligent guiding breathing algorithm based on feedback regulation mechanism was proposed to train patients to breathe slowly. Ten volunteers with hypertension were recruited and then trained to breathe slowly, accumulating up to 100 times using this device. The results showed that breath rate of the volunteers decreased from 15.16±0.92 times per minute to 9.40±0.29 times per minute, and meanwhile, time length of breath rate less than 8 times per minute in the proportion of total treatment time increased from 0.079±0.017 to 0.392±0.019 as the training times increased. In a conclusion, the proposed blood pressure depressor worked effectively in guiding slow breathing training.
The prevalence of cardiovascular disease in our country is increasing, and it has been a big problem affecting the social and economic development. It has been demonstrated that early intervention of cardiovascular risk factors can effectively reduce cardiovascular disease-caused mortality. Therefore, extensive implementation of cardiovascular testing and risk factor screening in the general population is the key to the prevention and treatment of cardiovascular disease. However, the categories of devices available for quick cardiovascular testing are limited, and in particular, many existing devices suffer from various technical problems, such as complex operation, unclear working principle, or large inter-individual variability in measurement accuracy, which lead to an overall low popularity and reliability of cardiovascular testing. In this study, we introduce the non-invasive measurement mechanisms and relevant technical progresses for several typical cardiovascular indices (e.g., peripheral/central arterial blood pressure, and arterial stiffness), with emphasis on describing the applications of biomechanical modeling and simulation in mechanism verification, analysis of influential factors, and technical improvement/innovation.
Objective To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.
In recent years, the prevalence of hypertension in China has gradually increased. Although the awareness rate, treatment rate and control rate of hypertensive patients in China have been significantly raised, the overall level is still lower than that of western developed countries. In order to improve the rate of family blood pressure control, real-time warning of patients’ overall blood pressure level to doctors and the implementation of doctor-side medical intervention to patients are becoming a necessary condition. At present, the maturing home blood pressure tele-monitoring (HBPT) enhances the feasibility of increasing the interaction between doctors and patients. Randomized controlled trial evidence proves that remote monitoring can improve patient compliance and improve target blood pressure control rate. This paper introduces the relevant research results of HBPT in recent years, aiming to explore the advantages of HBPT for hypertension management and the prospect of further promotion and application.
Objective To explore the impact of early blood pressure reduction on the prognosis of acute ischemic stroke. Methods We searched PubMed, Embase, Cochrane Library, Wanfang, China National Knowledge Infrastructure, and Chongqing VIP, with the search period from databases establishment to December 31, 2024. Randomized controlled studies on early blood pressure reduction within 7 days after the onset of acute ischemic stroke were included, and meta-analysis was conducted using RevMan 5.4 software. Results Finally, 15 randomized controlled studies were included. The meta-analysis results showed that there was no statistically significant difference in the primary outcome (90 days disability or death) and the secondary outcomes (90 days all-cause death) between the early blood pressure reduction group and the control group (P>0.05). Compared with the control group, the early blood pressure reduction group had a higher National Institute of Health Stroke Scale score at 2 weeks [standardized mean difference=0.25, 95% confidence interval (0.07, 0.44), P=0.008]. Conclusion Early blood pressure reduction cannot reduce the risk of 90 days disability or death and 90 days all-cause death in patients with acute ischemic stroke, and may be detrimental to 2 weeks neurological function recovery.
摘要:目的：研究成都地區中老年人群體重指數(BMI)與高血壓患病率及血壓水平的關系。方法:按照隨機抽樣的方法抽取樣本,對711人(平均年齡為63.28±6.25歲;男性占57.8%)進行了相關調查,調查內容中包括身高、體重、血壓及脈搏等。結果:成都地區中老年人群的超重及肥胖所占比重較大(約45%),按BMI分組(lt;18.5 kg/m2,18.5~23.9 kg/m2,24~27.9 kg/m2,≥28.0 kg/m2)的高血壓患病率分別是31.6%,54.8%,64.4%,82.8%,差異有統計學意義。采用logistic回歸分析發現在調整年齡、性別、腰圍及尿酸等后,BMI對高血壓的患病率有獨立影響。在整個人群及女性病人中,血壓隨著BMI的升高而有升高的趨勢,差異有統計學意義。結論:成都地區中老年人群超重及肥胖所占比重較大。BMI可以影響高血壓的患病率及影響女性病人的血壓水平,是高血壓的獨立危險因素。Abstract: Objective: To investigate the effects of body mass index on prevalence of hypertension and blood pressure in the elderly. MethodsA survey, including height, weight, blood pressure and pulse, was carried out in a general population of Chengdu. A total of 711 subjects (average age: 63.28±6.25 years; male: 57.8%) were recruited by random sampling method. Results：The proportion of overweight and obesity was about 45%. The hypertension prevalence rate was significantly positively correlated with BMI (Plt;0.01), and that was also seen in the level of SBP and DBP for the female (Plt;0.05). In logistic regression analysis adjusting for age, gender, waist, uric acid, the standardized OR for higher BMI (≥28.0 kg/m2) as a risk factor of hypertension was 5.140. Conclusion：The proportion of overweight and obesity was great in Chengdu area. BMI can affect the prevalence rate of hypertension and the level of blood pressure.
摘要:目的:研究老年患者動脈彈性功能與圍術期血壓變化的關系。方法:隨機選擇68例ASA分級Ⅰ－Ⅱ級行全麻手術的老年患者,根據檢查所得動脈彈性的結果分為四組,分別是A組(C1、C2均正常),B組(C1異常,C2正常),C組(C1正常,C2異常),D組(C1、C2均異常)。測量其術前血壓及全麻誘導8分鐘后的血壓水平。結果:〓動脈彈性功能不良的患者其術前MAP較高,且全麻誘導以后血壓波動的比例較大。結論:高血壓病的老年患者動脈彈性功能普遍降低;動脈彈性下降的老年病人全麻誘導后血壓波動較大。Abstract: Objective:To investigate the relationship between the function of arterial elasticity and BP changes during perioperation in senile patients.Methods: 68 senile patients ASA class Ⅰor Ⅱ undergoing elective surgery under general anesthestia, were divided into four groups by evaluation of arterial elasticity (C1 was for large arterial elastic index and C2 for small. C1 and C2 were normal in group A, only C2 normal in group B, only C1 normal in group C, neither was normal in group D). Arterial blood pressure (BP) before operation and 8 min after induction were monitored and recorded. Results: Patients with dysfunction of arterial elasticity presented higher MAP during preoperation and significant BP changes after induction. Conclusion: Hypertension plays a key role in arterial elasticity.Arterial Blood Pressure of the senile patients with decreased arterial elasticity changes significantly after general anesthesia induction.
In order to improve the accuracy of blood pressure measurement in wearable devices, this paper presents a method for detecting blood pressure based on multiple parameters of pulse wave. Based on regression analysis between blood pressure and the characteristic parameters of pulse wave, such as the pulse wave transit time (PWTT), cardiac output, coefficient of pulse wave, the average slope of the ascending branch, heart rate, etc. we established a model to calculate blood pressure. For overcoming the application deficiencies caused by measuring ECG in wearable device, such as replacing electrodes and ECG lead sets which are not convenient, we calculated the PWTT with heart sound as reference (PWTTPCG). We experimentally verified the detection of blood pressure based on PWTTPCG and based on multiple parameters of pulse wave. The experiment results showed that it was feasible to calculate the PWTT from PWTTPCG. The mean measurement error of the systolic and diastolic blood pressure calculated by the model based on multiple parameters of pulse wave is 1.62 mm Hg and 1.12 mm Hg, increased by 57% and 53% compared to those of the model based on simple parameter. This method has more measurement accuracy.
