摘要:目的: 观察急性链球菌感染后肾炎(APSGN)患儿血压变化规律。 方法 :观察15例重型APSGN(重型组)和20例普通型APSGN(普通组)患儿病程中各时期血压及尿量变化,并进行免疫和生化指标对比。 结果 :APSGN患儿高血压总共26例(7429%),其中重症组高血压14例(933%),普通组高血压12例(60%),高血压发生率重症组高于普通组〖WTBX〗P lt;005。重症APSGN患儿入院时舒张压、少尿期收缩压舒张压、多尿期舒张压与普通组比较均有显著性差异,〖WTBX〗P lt;005;而且重症APSGN与普通组在少尿期持续时间、尿量、多尿期持续时间、尿量的指标比较也均有显著性差异,〖WTBX〗P lt;005。重症APSGN患儿血IgG、BUN、Cr明显高于普通APSGN,Plt;005。 结论 :高血压是APSGN主要临床表现之一,血压增高多发生于少尿期,但重症APSGN患儿于多尿期出现血压增高的第二次高峰,临床上应注意监测,及时治疗。Abstract: Objective: To observe the law of the changes of children’s blood pressure after the infection of steptococcus with acute nephritis(APSGN).〖WTHZ〗Methods :Watching 15 cases of serious APSGN and 20 cases of ordinary APSGN for their changes in blood pressure and their urine amounts in various periods; contrasting their indexes in immunity and biochemistry.〖WTHZ〗Results :Among 26cases(7429%)of patients’high blood pressure with APSGN,of which 14cases (933%)are serious ones and 12cases (60%)are ordinary ones,the occurrence rate of the serious group is higher than that of the ordinary group, Plt;005At the initial stage, either the diastolic presssure or the diastolic pressure and the systolic pressure of the serious group with a small amount of unine, the diastolic pressure with a large amount of urine are evidently different from those of the ordinary group, Plt;005Furthermore,there are evident differences in the durations and amounts of urine with either a small or a large amount of urine, Plt;005The IgG,BUN and Cr of the serious group are evidently higher than those of the ordinary group,Plt;005 Conclusion :High blood pressure is one of the main clinical manifestations of APSGN.The increase of blood pressure mostly occurs during the period of a small amount of urine, but a second summit of high blood pressure with APSGN mostly appears in the period of a large amount of urine.It must be closely observed and therefore given the timely treatment.
The continuous left ventricle blood pressure prediction based on selected heart sound features was realized in this study. The experiments were carried out on three beagle dogs and the variations of cardiac hemodynamics were induced by various dose of epinephrine. The phonocardiogram, electrocardiogram and blood pressures in left ventricle were synchronously acquired. We obtained 28 valid recordings in this study. An artificial neural network was trained with the selected feature to predict left ventricular blood pressure and this trained network made a good performance. The results showed that the absolute average error was 7.3 mm Hg even though the blood pressures had a large range of fluctuation. The average correlation coefficient between the predicted and the measured blood pressure was 0.92. These results showed that the method in this paper was helpful to monitor left ventricular hemodynamics non-invasively and continuously.
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.
摘要:目的:研究老年患者动脉弹性功能与围术期血压变化的关系。方法:随机选择68例ASA分级Ⅰ－Ⅱ级行全麻手术的老年患者,根据检查所得动脉弹性的结果分为四组,分别是A组(C1、C2均正常),B组(C1异常,C2正常),C组(C1正常,C2异常),D组(C1、C2均异常)。测量其术前血压及全麻诱导8分钟后的血压水平。结果:〓动脉弹性功能不良的患者其术前MAP较高,且全麻诱导以后血压波动的比例较大。结论:高血压病的老年患者动脉弹性功能普遍降低;动脉弹性下降的老年病人全麻诱导后血压波动较大。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.
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.
ObjectiveTo investigate the value of morning blood pressure surge in the evaluation of atherosclerosis by the observation of the relationship between morning blood pressure surge and ambulatory arterial stiffness index. MethodsFrom May 2011 to March 2013, we followed up 300 hypertensive patients who received ambulatory blood pressure monitoring, and they were divide into elevated morning blood pressure surge group and non-elevated morning blood pressure surge group. The AASI value was recorded and compared between the two groups according to the grade of hypertension. ResultsMorning blood pressure surge was more common in patients with hypertension. Compared with patients in the non-elevated morning blood pressure surge group, ambulatory arterial stiffness index was significantly higher in patients in the elevated morning blood pressure surge group. ConclusionThere is a certain correlation between morning blood pressure surge and arterial stiffness, which is an important factor leading to a variety of target organ damage.
ObjectiveTo compare home blood pressure monitoring (HBPM) versus ambulatory blood pressure monitoring (ABPM) versus office blood pressure monitoring (OBPM) in diagnosis and management of hypertension, and to find the optimal blood pressure measurement and management.MethodsThe following were compared among three BP monitoring, such as cost-effectiveness, prognostic value of target organ damage (TOD), predictive value of the progress in chronic kidney disease (CKD) and blood pressure variety (BPV). ResultsCompared to OBPM, ABPM was the most cost-effective method in the primary diagnosis of hypertension, but HBPM was the optimal method in long-term and self-management in hypertension. In hypertensives, compared to OBPM, HBPM and ABPM, especially HBPM, had a stronger predictive value for cardiovascular events, stroke, end-stage renal dysfunction (ESRD) and all-cause mortality. In hypertensives with renal dysfunction, controlling HBPM and ABPM, especially controlling ABPM, was an effective way to slow the progress in renal dysfunction, to decrease cardiovascular events, and to decrease the need of dialysis. All BPV derived from OBPM, ABPM and HBPM had a predictive significance of cardiovascular events, and HBPM BPV performed the best.ConclusionCompared to OBPM, ABPM is the best method in primary diagnosis of hypertension and BP control in CKD patients, while HBPM is the best method in predicting and in evaluating BPV, as well as in long-term and self-management in hypertension.
The European Society of Cardiology (ESC) released the "2024 ESC guidelines for the management of elevated blood pressure and hypertension" on August 30, 2024. This guideline updates the 2018 "Guidelines for the management of arterial hypertension." One notable update is the introduction of the concept of "elevated blood pressure" (120-139/70-89 mm Hg). Additionally, a new systolic blood pressure target range of 120-129 mm Hg has been proposed for most patients receiving antihypertensive treatment. The guideline also includes numerous additions or revisions in areas such as non-pharmacological interventions and device-based treatments for hypertension. This article interprets the guideline's recommendations on definition and classification of elevated blood pressure and hypertension, and cardiovascular disease risk assessment, diagnosing hypertension and investigating underlying causes, preventing and treating elevated blood pressure and hypertension. We provide a comparison interpretation with the 2018 "Guidelines for the management of arterial hypertension" and the "2017 ACC/AHA guideline on the prevention, detection, evaluation, and management of high blood pressure in adults."
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.
Objective To analyze the influencing factors of prognosis of patients with traumatic brain injury (TBI), and explore the influence of hemoglobin (Hb) level combined with blood pressure variability (BPV) on the quality of prognosis of patients with TBI. Methods The data of 186 TBI patients who received systemic treatment in the Affiliated Zhangjiagang Hospital of Soochow University between January 2020 and December 2021 were retrospectively analyzed. According to the Glasgow Outcome Scale (GOS) 3 months after treatment, they were divided into group A (GOS 4-5, 159 cases) and group B (GOS 1-3, 27 cases). The general clinical data, BPV indexes and Hb levels of the two groups were analyzed by single factor analysis and multiple logistic regression analysis, and the predictive value of the logistic regression model was evaluated by receiver operating characteristic (ROC) curve, sensitivity, specificity and area under the curve (AUC). Results There was no statistical significance in gender, age, body mass index, blood urea nitrogen, prothrombin time, fasting blood glucose level, or smoking history (P>0.05); the patients’ Glasgow Coma Scale at admission in group A was higher than that in group B (P<0.05), and the constituent ratio with a history of hypertension of group A was significantly lower than that of group B (P<0.05). The between-group differences in systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and Hb at admission, and SBP, DBP, and MAP 72 h after treatment were not statistically significant (P>0.05); the SBP-standard deviation (SD), DBP-SD, SPB-coefficient of variation (CV) and DBP-CV of group B 72 h after treatment were significantly higher than those of group A (P<0.05), and the level of Hb was significantly lower than that of group A (P<0.05). Hb [odds ratio (OR)=0.787, 95% confidence interval (CI) (0.633, 0.978), P=0.031], SBP-CV [OR=1.756, 95%CI (1.073, 2.880), P=0.023] and DBP-CV [OR=1.717, 95%CI (1.107, 2.665), P=0.016] were all independent prognostic factors of TBI patients. The ROC showed that the combined index of BPV and Hb was more valuable than that of single prediction, with an AUC of 0.896 [95%CI (0.825, 0.935), P<0.05]. Conclusions Both BPV and Hb are independent factors affecting the prognosis of TBI patients, and their combined application can more effectively predict the prognosis of TBI patients. Therefore, when treating and evaluating the prognosis of TBI patients, closely monitoring the changes in blood pressure and Hb levels can timely and effectively control the development of the disease, and provide scientific reference for subsequent treatment.